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Product-led and sales-led growth motions aren't binary; they exist on a spectrum. In this episode of Operations, we dive into the fascinating story of Canva's go-to-market evolution with Aarti Raman, former Global Head of Revenue Strategy and Operations. Canva, the design platform valued at over $30 billion, had already surpassed $1B in ARR through a bottom-up product-led growth model. However, their B2B Enterprise sales model required a shift.In our conversation, Aarti shares the strategy behind transitioning Canva's Enterprise team from a sales-led to a product-led model, the complexities of implementing pricing changes at their size, and why even at their scale, you still have to argue about what a PQL is. Don't miss this deep dive into how Canva balanced product-led adoption with human-driven enterprise sales.Like this episode? Be sure to leave a ⭐️⭐️⭐️⭐️⭐️⭐️ review and share the pod with your friends! You can connect with Sean on LinkedIn or subscribe to our YouTube channel.Want to work with Sean? Reach out to him and the team at BeaconGTM to help with GTM execution at your company.Anyone interested in ordering The Revenue Operations Manual can go here and use the code REVOPS20 for 20% off (or buy from any of your preferred booksellers here)!This episode is brought to you by Default, the inbound growth platform for B2B marketing teams. Visit Default.com/seanlane today to learn more and revolutionize your RevOps today!
In this limited series of the ProductLed Podcast, Wes Bush shares the contents of his new book—The Product-Led Playbook. Each week, we're releasing one chapter at a time, providing you with practical, no-nonsense guidance on how to build a multi-million-dollar product-led business with a lean team. In today's episode, Wes outlines the three phases of the data component essential for scaling a SaaS business. First, he reveals the six core metrics that matter most. These metrics are crucial to understanding user behaviour and pinpointing bottlenecks. Next, Wes introduces the weekly scorecard, a simple yet powerful tool to monitor these core metrics over time, making it easier to spot trends, identify bottlenecks, and make data-driven decisions. Finally, he explains the concept of Product Qualified Leads (PQLs) users who demonstrate high engagement and are likely to upgrade. By installing PQL tracking, teams can prioritize and convert users who are already getting value from the product. Key Highlights: 05:15: Importance of identifying core metrics.09:30: The top metrics to track for a product-led business.12:45: Setting up a weekly scorecard for your team.16:00: Identifying the biggest bottleneck through the scorecard.19:20: Understanding and identifying PQLs.23:50: How to leverage PQLs to increase conversions. Here's where you can purchase The Product-Led Playbook.
Natalie Marcotullio, Head of Growth at Navattic, takes us in for a behind-the-scenes look into the process of launching their freemium motion. Going through the strategic reasons behind the shift and how it fits into their overall growth strategy, she reflects on the challenges of aligning the sales team, navigating risk, and measuring success in both the short and long term. Natalie also shares her perspective on the realities of product-led growth, the importance of soft-pitching ideas, and the unexpected lessons learned during the launch process.Natalie opens up about: The excitement and pressure of launching a freemium planThe value of soft-pitching product-led growth strategies over timeThe challenge of aligning a sales team to embrace PLG leadsThings to listen for: (00:00) Intro(03:17) Why Navattic chose freemium over a free trial model(05:53) The impact of freemium on pipeline and user conversion(08:00) Navigating internal buy-in and the importance of soft-pitching PLG(10:01) Testing and experimenting with lower-priced plans before going freemium(12:13) Aligning the sales team with the PLG motion and managing challenges(14:00) Modeling pipeline projections and the impact on MQL and PQL metrics(16:19) The role of customer support in managing increased user inquiries(18:42) Sales engagement with self-serve users and building trust(20:25) Reflecting on early success indicators and word-of-mouth growthResources:Connect with Natalie:LinkedIn: https://www.linkedin.com/in/natalie-marcotullio/Connect with Andrew:LinkedIn: https://www.linkedin.com/in/andrewcapland/ Hire Andrew as your coach: https://deliveringvalue.co/coaching
Bu sezon sponsorumuz Sanction Scanner ile tanışın, “Breaking Bad” de gördüğümüz kara para aklama sahnelerini hatırlarsınız. Senede 2 trilyon dolarlık kara para aklanıyor.İşte burada Sanction Scanner'ın yazılımı devreye giriyor. Yapay zeka ve makine öğrenmesi ile desteklenen ürünleri, banka ve benzeri finansal kuruluşlara gerçek zamanlı AML, yani Anti-Money Laundering, taramaları yaparak finansal kuruluşla iş yapmak isteyen kişi ve işlemlerin sıkıntı olup olmadığını analiz ediyor. Sanction Scanner hakkında daha fazla bilgiyi buradan ulaşabilirsin: https://sanctionscanner.com/---Brick Institute eğitimleri, deneyimli eğitmenleri ve seçkin katılımcılarıyla birlikte Ürün Yönetimi Temelleri, Ürün Analitiği ve Ürün Liderliği programları çok yakında başlıyor. Bu eğitimler, gerçek hayat uygulamaları ve vaka çalışmaları üzerine odaklanarak, ürün yönetimi alanında uzmanlaşmak, ürün geliştirme süreçlerini kuvvetlendirmek isteyenler için oluşturuldu.Kontenjan sınırlıdır, bu nedenle hemen www.brick.institute adresinden başvuru yaparak yerinizi garantileyin ve eğitime katılmak için kaydolun!----Üretim Bandı'nın Slack grubu olduğunu biliyor muydunuz? 3000'den fazla ürün yöneticisi, girişimci, yazılımcı, tasarımcının bir arada bulunduğu aktif ürün topluluğuna siz de katılın:>>> uretimbandi.com/slackİki haftada bir yayınladığımız, ürün geliştirmeyle alakalı bültenimizi de aşağıdaki linkten takip edebilirsiniz:>>> uretimbandi.com/bulten----------KONUKLevent Aşkan: https://www.linkedin.com/in/leventaskan/Ari Bencuya: https://www.linkedin.com/in/aribencuya/LINKLERErken Aşama SaaS Firmaları İçin GTM ve Ürün Konumlandırma Eğitimi: https://saaslab.pro/erken-asama-saas-gtm-konumlandirma/KONUŞULANLAR(00:00) Başlangıç(04:52) SaaS büyütmenin farkları(06:50) SaaS'ın dört atlısı(14:10) ICP bulmak ve denemeler dengesi(21:38) Her SaaS'ın patikası aynı mı(25:20) İdeal pazara ve müşteriye ulaşmak(33:01) PQL (Ürünü Seven Potansiyel Müşteri) ile önceliklendirme(36:10) Satış süreci verimliliği(43:50) Hayat boyu değer ve Churn(47:23) Onboarding ve entegrasyon(53:50) SaaS büyütme eğitimleri
In this episode, Wes Bush and Laura Kluz examine the use of Product Qualified Leads (PQLs) in a product-led business. Wes explains why PQLs are so important, and how they drive user success. He also addresses the common challenges product-led companies face when identifying PQLs. Wes then break down the four essential components of a PQL so you can identify your own, and shares some specific strategies you can employ with your team to get more PQLs. To read more about PQLS, be sure to check out this article. Highlights: [1:45] Wes introduces the concept of PQLs and their importance. [03:40] Why PQLs are important for startups. [10:20] The common challenges of identifying PQLs. [12:05] The four milestones that make up a PQL [17:15] Importance of tracking PQLs from early stages. [19:56] Wes recounts a success story with his first client using PQLs. About the ProductLed System™️ Stop guessing how to execute a product-led strategy. Instead, follow a proven system that dials in your focus to 2x your self-serve revenue in 12 months by focusing on the nine components that make up a product-led business. You can learn more here.
In this episode, Wes Bush and Laura Kluz examine the use of Product Qualified Leads (PQLs) in a product-led business. Wes explains why PQLs are so important, and how they drive user success. He also addresses the common challenges product-led companies face when identifying PQLs. Wes then break down the four essential components of a PQL so you can identify your own, and shares some specific strategies you can employ with your team to get more PQLs. To read more about PQLS, be sure to check out this article. Highlights: [1:45] Wes introduces the concept of PQLs and their importance. [03:40] Why PQLs are important for startups. [10:20] The common challenges of identifying PQLs. [12:05] The four milestones that make up a PQL [17:15] Importance of tracking PQLs from early stages. [19:56] Wes recounts a success story with his first client using PQLs. About the ProductLed System™️ Stop guessing how to execute a product-led strategy. Instead, follow a proven system that dials in your focus to 2x your self-serve revenue in 12 months by focusing on the nine components that make up a product-led business. You can learn more here.
@taylorawelch explores the intricacies of the sales funnel, focusing on the middle stage. Contrary to common practices, he advocates for introducing friction in this phase to filter out unqualified leads. He discusses the importance of vetting potential clients based on need and time, ensuring alignment with your target market. Learn the art of inserting friction strategically to avoid attracting the wrong audience and preserve your time and reputation. Let's dive into the nuances of qualification and discover how it leads to genuine, long-term client relationships.IF you enjoyed the show please leave us a review to help push this message to more listeners around the world!Please visit Taylorawelch.com to access all of Taylor's socials and content Text Taylor: 615-326-5037Daily Mind Medicine is back!Chapters: (00:58) Funnel levels(01:42) Thinking content(02:40) Inserting friction(03:47) Qualification in middle of funnel(04:31) Need and time qualification(05:43) PQL(06:00) Selling a way of thinking
Morgan sits down with Breezy Beaumont, former Head of Growth & Marketing at Correlated, to discuss product qualified leads (PQLs) and how they transform a product-led growth strategy. They dive into building revenue intelligence, PQL vs. MQL vs. SQL, how sales changes in a product-led company, and building the revenue teams of the future. Get ready for a serious knowledge drop from one of the leaders in product-led revenue.Connect with Breezy BeaumontLinkedInWebsiteIn this episode, we cover:Product-led growth vs. ABM & Outbound (1:20)Sales teams in product-led companies (3:10)Adding value in the buyer's lifecycle (6:42)Product qualified leads – PQL (8:31)Why PQLs exist post-purchase (13:29)Common mistakes & successes (15:48)Mistakes in shifting a company to product-led growth (19:32)Creating centralized revenue intelligence (23:36)Judgment calls when creating PQLs (28:15)Breaking down silos (31:28)The end of the CMO? (34:47)Layering on traditional outbound in product-led firms (37:02)Product-led movements for enterprise buyers (43:08)Follow Nicholas Thickett on LinkedIn: https://linkedin.com/in/nicholasthickettFollow Morgan Smith on LinkedIn: https://linkedin.com/in/morganjsmithJoin the 1Up Club to power up your prospecting. Get access to power plays, special briefings, and even DIY enablement docs that help you prospect better. Learn more at b2bpowerhour.com/join.
What's up folks, we've got another roundup episode today and we're talking AI. Before you dismiss this and skip ahead, here's a quick summary of why the excitement around generative AI isn't just hype—it's a sustainable shift.While some may perceive AI to be losing steam, largely due to a surge of grifters in the field, this is not your average trend. In Episode 78, we spoke with Juan Mendoza, CEO of TMW, about why generative AI is distinct. It's not mere hype or a future possibility; generative AI delivers practical value today.Examining Google Trends data for the search term "AI + marketing," we notice a significant surge starting in November 2022, coinciding with the release of ChatGPT. This surge peaked in May 2023 when GPT-4 became mainstream. Normally, you'd expect interest to wane after such a peak, but it has barely dipped. We're currently sitting at a 94/100 search interest, compared to this summer's peak. This suggests a sustained, rather than fleeting, interest in the technology.While nobody has a crystal ball, there's broad agreement that AI is far from making marketing roles obsolete. Instead, it's augmenting the work we do, not replacing it.In an effort to explore further how we can better future proof ourselves, I've asked guests what specific aspects of marketing make it resistant to AI. The insights from these discussions have been fascinating, underscoring the unique value and human touch that marketers bring to the table.Here's today's main takeaway: Your real edge in marketing fuses a nuanced understanding of business context, ethics, and human emotion with capabilities like intuition, brand voice and adaptability—areas where AI can sort data but can't match ability to craft compelling stories. AI isn't pushing you aside; it's elevating you to a strategic role—given you focus on AI literacy and maintain human oversight. This isn't a story of human vs. machine; it's about how both can collaborate to tackle complexities too challenging for either to navigate alone.AI is less a replacement and more of a reckoning. It's not coming for us; it's coming for our inefficiencies, our lack of adaptability, and our refusal to evolve. AI is holding up a mirror to the marketing industry, asking us not if we can be replaced, but rather, why we haven't stepped up our game yet. Buckle up; this roundup of experts doesn't just debate the future—it challenges our very role in it.Why AI Can't Fully Replace Human Nuance in Marketing OperationsLet's start off in Marketing Operations with Mike Rizzo, the founder of MarketingOps.com. We asked him to dive into his view that AI won't be replacing marketing jobs "anytime soon," a point that has some level of ambiguity. The question aimed to uncover what Mike specifically means by "anytime soon" and why he believes that AI won't fully automate the marketing Operations sector in the near future.Mike highlighted the intricacy of marketing operations that he believes will be resistant to full automation. Specifically, he mentioned that marketing across SMBs and enterprises involves nuanced processes. The differentiation between types of leads—MQL, SQL, PQL, and so on—each has its own distinct workflow and architecture. This makes it a highly tailored field, more a craft than a science, and challenging to automate.Mike pointed out that the entire operational architecture, from data movement to notification protocols, is unique to each organization. It's precisely this framework that makes it hard to replicate with AI, regardless of its computational abilities. While he admitted that AI could offer suggestions in optimizing specific metrics or elements, such as lead scoring, Mike emphasized that these technologies serve better as consultants rather than decision-makers.The implementation of martech stacks, according to Mike, is akin to running a product. From understanding the product roadmap to enabling team members, AI can at best serve as a consultation service, streamlining processes but never fully taking over. Each tech stack is tailored to an organization's needs, something that AI, for all its merits, struggles to capture in its full complexity.Mike also confessed to leveraging AI for particular tasks but remains skeptical about its ability to handle the fine-tuning required in the marketing ops and RevOps space. He argued that while AI can assist, it can't replace the distinct, specialized requirements that each marketing operation demands.Key Takeaway: Mike suggests that AI has its uses, but the nuanced, unique nature of marketing operations makes it a field that's resistant to full automation. There's value in human oversight that not even the most advanced AI can replicate.Trust in Data and the Ability to Constrain AI ResponsesWhile AI might have some challenges with the nuances of marketing Ops, AI does have a foothold in some marketing sectors. Boris Jabes, the co-founder and CEO at Census, acknowledged AI's ability to drive efficiency, especially in advertising. In spaces where "fuzziness" is acceptable, such as Ad Tech, AI already performs exceptionally well. Marketers utilize advanced algorithms in platforms like Google and Facebook to better place their ads, and these platforms are continuously fueled by world-class AI. In these instances, AI isn't just convenient; it's almost imperative for maintaining competitive performance.However, Boris warns that there are areas where AI falls short, specifically in customer interactions that require nuanced understanding and empathy. For example, using AI to answer questions about ADA compliance or other sensitive matters can result in "hallucinations," or incorrect and inappropriate responses. Herein lies a crucial challenge: How do you constrain AI to deliver only appropriate, correct information?Additionally, Boris identifies data trustworthiness as a significant hurdle. AI's performance depends on the quality of data it's trained on. Large enterprises are often hesitant to adopt AI without reliable data, and thus, miss out on its advantages. Conversely, smaller companies are more willing to experiment, but their scale is insufficient to make industry-wide impacts.Despite the challenges, Boris argues that staying away from AI is not an option for today's marketers. Whether you are aiding the machine with quality data or deciphering how AI can be employed responsibly, there's room for human marketers to provide valuable input and oversight.Key Takeaway: AI has carved out a substantial role in specific sectors of marketing like Ad Tech, but it still has limitations that require human oversight. Trust in data and the ability to constrain AI responses are areas where marketers can add significant value.Marketers Are Future Prompt Thinkers and AI RegulatorsOver the next few years, marketers will be invaluable when it comes to ensuring data integrity and guiding AI's influence. Let's explore how marketing roles might evolve across different verticals. Pratik Desai has some fascinating predictions about the role of marketers. He's the founder and Chief Architect at 1to1, an agency focused on personalization strategy and implementation.When asked about the limitations preventing AI from taking over the marketing landscape, Pratik dives into the intricacies of how AI operates in different sectors. According to him, AI in marketing can be bifurcated into "Curation AI" and "Generation AI." Curation AI, as the name suggests, curates content and recommendations. Generation AI, a more recent evolution, generates content from scratch.Curation AI has shown promise, especially in less regulated industries like e-commerce. Here, even if AI gets it wrong 15% of the time, the increase in efficiency and accuracy for the remaining 85% is often considered a win. But switch the lens to highly regulated sectors like financial services or healthcare, and the stakes skyrocket. Here, even a 1% mistake rate could translate into severe regulatory or even life-impacting issues. This inherent limitation necessitates a "marketer control" layer to ensure compliance and accuracy.In comes Generation AI, aimed at resolving some of these content-based challenges. With its ability to generate images and copy at scale, Pratik posits that it could revolutionize how marketing programs are run. This technology can create content in seconds, which would otherwise take a design team weeks to produce. But again, the human element isn't completely removable. Marketers will still need to oversee these automated processes, especially in regulated sectors where the margin for error is minuscule.Key takeaway: The role of the marketer is changing but not disappearing. In industries with low regulation, marketers transition to becoming "critical prompt thinkers," while in more regulated sectors, they wear the additional hat of "AI regulators." This reveals the dual nature of AI: a tool that can enhance efficiency yet requires human oversight for nuance and regulatory compliance.The Need for Ongoing Dialogue Between AI and the MarketerThis inherent necessity of a "marketer control" layer to ensure compliance and accuracy is a shared thread. When asked about the potential of AI to take over the marketing realm, Tamara Gruzbarg—VP Customer Strategy at ActionIQ—offered a seasoned perspective, advocating for a more nuanced view. She was explicit that AI can certainly handle the grunt work—automating repetitive tasks and even aiding in content generation. However, Tamara highlighted the irreplaceable role of human marketers when it comes to understanding brand voice, tone, and style.Tamara also cautioned against overlooking the human element in data analytics and predictive modeling. She argued that constructing models for critical business metrics like conversion rates and lifetime value demands a deep understanding of business context. AI tools may be adept at crunching numbers, but they fall short in interpreting the underlying structure and implications of the data.Tamara introduced the "human-in-the-loop" philosophy that they follow at ActionIQ, emphasizing the need for ongoing dialogue between the AI and the marketer. This interaction ensures that AI-generated content aligns with the brand's unique voice and message, preventing a homogenized marketplace where every brand sounds the same.The discussion confirmed the ongoing need for marketers to "cut through the noise." Tamara argued that a human touch is essential for achieving this, particularly in an era where AI can churn out volumes of generic content. She pointed out that while AI could be a valuable partner in initial drafts and multiple versions of content, the final say should always be human.Key Takeaway: Tamara stressed the importance of human expertise in data analytics and predictive modeling. While AI can handle data computation, it lacks the ability to understand business context and the nuances of data. She advocates for a "human-in-the-loop" approach at ActionIQ, which keeps marketers engaged with AI tools. This collaboration ensures brand-specific messaging and avoids market homogenization.AI's Creative Strengths and Brand Style Guide LimitationsThis idea of brand specific messaging also extends to visual brand marketing and AI's lack of ability to follow a brand guideline… at least for now. Pini Yakuel is the CEO of Optimove, a platform that's operating light years ahead of most martech when it comes to AI features. When asked about the roadblocks stopping AI from completely replacing human marketers, Pini focused on the intricacies of creative studio work. He points out that while AI can perform well in tasks such as comic book illustrations, it still falls short when you factor in the human elements—like nuance, emotion, and unique design language—that often define a brand.Pini recounts a conversation he had with one of his designers about this very issue. The designer expressed that AI could create fantastical images—like a unicorn riding a motorcycle on Mars—but couldn't quite replicate the specific design language integral to their brand, Optimove. Despite AI's capabilities in artistry and replication, it lacks the human touch needed to navigate the complex and nuanced design landscape that brands often require.He emphasized that while AI can go wild with creative elements, it's not yet proficient at maintaining the unique "look and feel" that a brand's specific style guide may dictate. For instance, integrating various elements into a cohesive design that represents a brand authentically is something AI still struggles with. According to his designer, the technology simply isn't there yet, at least not to a level that can replicate the careful and intentional choices a human designer would make.This limitation isn't just about not having enough processing power or data; it's about an inherent lack of understanding of human emotion, culture, and nuanced communication. These elements often serve as the underpinning for any successful marketing campaign, aspects that AI can't yet replicate.Key Takeaway: Pini argues that the barrier to AI fully replacing human marketers lies in the inability to understand and replicate the nuanced, human elements that make up a brand's unique design language. Until AI can integrate this "human touch," it will remain a tool rather than a replacement.The Trust Barrier in AI's Quest to Replace MarketersIf you asked a marketer in the mid 80s if the Internet would replace everything a marketer did back then, they probably would've been skeptical. To be fair it didn't replace everything but marketing looked dramatically different 10-15 years after that. At the heart of roles shifting and a marketer control layer is this idea of adapting. Deanna Ballew is Senior Vice President of DXP Products at Acquia where her team is focused on innovating with AI for marketers. When asked about the likelihood of AI replacing marketers, Deanna emphasized that it's not a matter of "if," but "how" we adapt to this looming shift. In line with comments from Boris, she added that the obstacle isn't the capability of the AI but the trust—or lack thereof—in the data it uses. Deanna points out that tools like ChatGPT aren't yet trusted because they rely on an immense pool of uncurated data. To trust an AI with marketing tasks, there's a need for curated, proprietary models.Deanna brings the focus back to a crucial but often overlooked factor: AI literacy among marketers. As AI technology advances, so must the understanding marketers have about the underlying models. The future isn't just about AI doing the work but about marketers asking the right questions. Chat UX interfaces could enable marketers to query data effectively, bypassing the need for a business intelligence analyst. However, this streamlined process depends on the trustworthiness of the data.Here's the flip side: As marketers become more literate in AI, their roles will shift from manual tasks to higher-value activities. Think about posing complex questions to AI-driven systems, which could then provide strategic insights that marketers can translate into actionable campaigns. Marketers could use these interfaces to directly ask, "What's the next best customer segment to go after?"—with the system offering insights based on trusted data.The advancement of AI is like a double-edged sword. On one side, it promises to relieve marketers of mundane tasks; on the other, it demands a new set of skills and a higher level of trust in the data. Deanna stresses that the transformation is inevitable, but the timeline is undetermined, hinging on how quickly trust can be established in AI-generated data and models.Key Takeaway: Deanna underscores the role of "trust" as the linchpin for AI adoption in marketing. Marketers should focus on increasing their AI literacy and understanding of underlying models to prepare for this seismic shift. Without trusted data and models, even the most advanced AI can't eliminate the human checkpoint in marketing decisions.The Organic Evolution of AI in MarketingThere's a clear trend so far, that the human checkpoint in AI is going away anytime soon. That means there's a clear signal for marketers to follow Deanna's advice and double down on AI literacy. The next question is really about how fast you should consider doing this. How fast will we need to adapt?Aliaksandra Lamachenka, a Marketing Technology Consultant, had a surprising and insightful answer. She drew an analogy with post-war Japanese architecture, specifically a concept known as "Japanese Metabolism." This architectural philosophy thought of buildings as living organisms with a spine to which modular capsules could be attached or detached. Despite its early promise in the '50s and '70s, this concept now largely exists as an idea, with few practical implementations. The buildings initially envisioned as the future of living are now mostly used for storage.What does this have to do with AI replacing marketers? Aliaksandra contends that society needs time to adapt and accept new concepts, just as with Japanese Metabolism. The notion of AI taking over marketing roles is a similarly radical shift that society isn't ready to fully embrace. Moreover, she believes that the evolution of AI will be more organic than revolutionary, a natural progression shaped by cultural and societal shifts.Aliaksandra underscores that although AI has vast potential, the speed at which humans can adapt and accept these changes is the bottleneck. She compares AI's future impact to the way modular buildings and integrated landscape houses have slowly, but organically, become part of architectural reality. Aliaksandra asserts that AI's growth will similarly happen organically over decades, not through immediate disruption but by evolving naturally into our processes and systems.She concludes by pointing out that the ideas of the past often serve as the blueprints for future innovation. Whether it's post-war Japanese architects or today's AI developers, the radical concepts and technologies introduced will take time to become an integral part of society. Like the modular houses of today that owe their conceptual roots to Japanese Metabolism, future AI capabilities will likely be adaptations of current bold ideas.Key Takeaway: Aliaksandra suggests that the pace at which humans can adapt to new ideas is the limiting factor in AI's ability to replace marketers. She predicts a gradual, organic evolution of AI in marketing, driven more by human adaptation than by technological capabilities.AI's Shortfall in Grasping Marketing's Emotional and Intuitive SideWhile the advance of AI in the marketing sphere could be more of a steady march than an overnight revolution, there's a threshold it hasn't crossed: the realm of human intuition and gut decision-making. Tejas Manohar, Co-founder and Co-CEO at Hightouch, offered a nuanced take, emphasizing both the promises and limitations of AI. Tejas mentioned that AI technologies, like generative AI and reinforcement learning, have already begun revolutionizing how marketing campaigns and experiments are run. They offer incredible potential for automating tasks such as data experimentation, audience segmentation, and personalization.However, Tejas made it clear that AI is not ready to replace human marketers entirely. The core of his argument lies in the duality of the marketing role, which requires both quantitative and qualitative skills. While AI can crunch numbers, run experiments, and even generate content, it falls short when the job requires a deeper understanding of human emotions or intuition-based decision-making. Tejas points out that marketers often rely on a mix of data and gut feeling, using insights to make substantial strategic changes. Current AI technologies are just not equipped to understand or implement these nuanced elements.He also discussed the notion of AI as a complementary tool rather than a replacement. Tejas is bullish on the idea that AI will augment marketers, particularly by providing them with easier access to critical business data. He envisions a future where marketers won't have to request specific scripts or datasets but can work independently to glean insights, thanks to advancements in AI technologies.The issue of AI completely taking over marketing, Tejas concluded, is also tied to broader ethical and societal questions. If AI gets to a point where it can wholly replace human skills and intuition, society will face "singularity type problems" affecting not just marketing but every job role.Key Takeaway: According to Tejas, AI's current role in marketing is as an augmenter, not a replacer. While it excels at quantitative tasks, it lacks the nuanced understanding of human emotion and intuition that is critical for effective marketing. Its potential lies in the empowerment it can offer marketers through data access and automation.The Thrill of Using Generative AI in Your Martech StackMany of the marketers I chatted with echoed Tejas, that AI may be able to process data and spit out automated directives, but it can't yet replicate the unpredictable, qualitative essence of what makes marketing tick. One particular guest flipped the script on me and argued that the exciting debate is how AI will augment, not replace, the roles of marketers.The Martech Landscape creator, the Author of Hacking Marketing, The Godfather of Martech himself, mister Scott Brinker had a clear perspective: we're not there yet. For Scott, "good marketing" remains a domain where human intuition and creativity hold court. AI may be able to process data and spit out automated directives, but it can't yet replicate the unpredictable, qualitative essence of what makes marketing tick. The buzzphrase "Your job won't be replaced by AI; it will be replaced by another marketer who's good at using AI" captures the current sentiment aptly. Cheesy as it may sound, Scott sees a grain of truth here. Far from envisioning a future where AI eliminates human roles, he expects technology to bolster the capabilities of marketing professionals. It's about learning how to weave AI into current practices to improve efficiency and expand possibilities.But where Scott finds the most promise is in the evolving role of marketing ops leaders and martech professionals. The real thrill comes from the ability to leverage generative AI to optimize what a marketing stack can do. Essentially, AI becomes a potent tool in the toolbox of the modern marketer, especially in operations. The tech is less about replacing humans and more about magnifying their abilities.However, Scott's perspective doesn't herald the end of human involvement; it simply reframes it. AI becomes a part of the job, a powerful component in the array of strategies and tactics that marketers employ. For him, it's about balance, not replacement. AI might be good, even exceptional, at crunching numbers and predicting outcomes based on existing data. But it can't yet think creatively or strategically in the way humans can, which is where the core of "good marketing" lies.Key Takeaway: The future of marketing isn't a binary choice between human intuition and machine capabilities. Rather, it's a synergistic relationship where each amplifies the other. For Scott, the real excitement lies in how AI will augment, not replace, the roles of marketers.AI's Storytelling Shortfall in Marketing's Emotional LandscapeWhile AI will continue to amplify the reach and efficiency of marketing efforts, experts agree, their role remains largely complementary to human skill sets. Despite its analytical prowess and automation capabilities, AI hasn't cracked the code on intuition and following brand guidelines but what about emotional intelligence or compelling storytelling—elements that are often considered the heart and soul of effective marketing. Lucie De Antoni, Head of Marketing at Garantme, brought forth some astute observations. Sure, AI is making strides in many industries, marketing included. It can automate and even enhance several elements of the marketing process. But what AI notably lacks, according to Lucie, is the ability to replicate human creativity and emotional intelligence.Marketing isn't just a numbers game. It's about storytelling, tapping into human emotions, and crafting narratives that resonate with people. Lucie argues that these are areas where AI falls short. While machine learning can analyze trends and predict consumer behavior to a certain extent, it's not equipped to fully understand the nuances of human sentiment or create emotionally resonant campaigns. This shortcoming isn't necessarily a drawback; Lucie sees it as a positive aspect. If AI were capable of such emotional intelligence and creativity, it would put marketers in a tricky situation. The very things that make marketers invaluable—understanding human behavior, crafting compelling stories, evoking emotion—are elements that AI can't yet emulate.So, the reality isn't that AI is primed to push marketers out of their jobs, but rather that it can become a tool that complements human skills. Lucie suggests that this "limitation" of AI serves as a safeguard for the unique value that human marketers bring to the table. The tech may evolve, but it's unlikely to eclipse the human ability to connect on an emotional level anytime soon.Key Takeaway: Lucie emphasizes that the strength of human marketers lies in their ability to understand and evoke human emotions—a skill set that AI, despite its advancements, cannot yet replicate. Therefore, while AI can be a powerful tool, the human element in marketing remains irreplaceable.Episode RecapAI is already rampant in marketing, particularly in fields like Ad Tech. However, generative AI is not a magic bullet; human expertise is essential for interpreting data and grasping brand nuances. A "human-in-the-loop" approach creates a checks-and-balances system, fostering trust in the data generated by AI and offering the emotional intelligence that machines lack.Marketing roles are evolving but definitely not vanishing. In sectors with fewer regulations, marketers could morph into strategic thinkers, whereas in tightly controlled industries, they're becoming essential AI regulators. To effectively ride this wave, increasing AI literacy among marketers is non-negotiable.The speed at which AI becomes a staple in martech is not solely a question of technological prowess. It's about how quickly humans can adapt and find ways to integrate AI into existing frameworks. The most viable future is not a zero-sum game between human and machine; it's a collaborative one, where each enhances the other's strengths.You heard it here first folks: Your real edge in marketing fuses a nuanced understanding of business context, ethics, and human emotion with capabilities like intuition, brand voice and adaptability—areas where AI can sort data but can't match ability to craft compelling stories. AI isn't pushing you aside; it's elevating you to a strategic role—given you focus on AI literacy and maintain human oversight. This isn't a story of human vs. machine; it's about how both can collaborate to tackle complexities too challenging for either to navigate alone.✌️--Intro music by Wowa via UnminusCover art created with Midjourney
Today's episode hones the best practices for go-to-market strategies tailored to technology companies targeting developers and tech experts. Our guest, Ben Williams, a former executive turned PLG strategic advisor, is here to share valuable insights. Earlier this year, he played a pivotal role steering a cybersecurity company to an astonishing $7.4 billion valuation. Host Maja guides a knowledge-packed discussion, exploring the critical pitfalls and blind spots in PLG strategies with Ben and draws from his experiences at Snyk to examine the choice between niche focus versus multiple Ideal Customer Profiles. They also explore customer discovery methods, how to prioritize experiments, and validate MVP and MVB (Minimum Viable Business Model). Key Takeaways: [00:10] Introduction of Ben Williams [05:05] Strategies for effective developer marketing [05:55] Building community and providing value to developers [08:05] Tactics for helping developers learn and grow [11:40] Effective approaches to developer marketing [12:10] Addressing privacy concerns and building trust [13:45] Transparency and data tracking for developers [15:30] Finding the right-sized market slice [17:20] Transitioning from self-serve to enterprise sales [18:50] Using product engagement states to identify potential customers [19:50] Leveraging engagement states for tailored outreach [22:30] Shifting from PQL to PQA for customer actions [25:00] Prioritizing experiments using ICE scoring [26:25] Focus on experiment execution and learning [27:50] Questioning experiment frequency for new teams [28:40] Faster experimentation with a shift to strategy [29:20] Documenting learnings and breaking silos [31:15] Discussing failures in reviews [32:45] 20% baseline for experiment success [33:55] Embracing failure and experimentation speed [38:55] Customer discovery mechanisms [39:30] Highlighting underutilized pricing experiments [41:35] Pricing impact and fear [42:37] Airtable and Notion for growth processes [46:00] Balancing work and personal life About Ben Williams: Ben Williams, a PLG advisor and former VP of Product at Synk, played a pivotal role in propelling a cybersecurity company focused on cloud computing to an impressive valuation of 7.4 billion in 2023. Now, Ben is dedicated to his mission of empowering product-led companies to make informed decisions and achieve better results. Links: Ben Williams | LinkedIn PLGeek
This week, Doug Camplejohn, CEO of Airspeed joins the hosts to discuss the difference between building products and building culture, the true definition of workplace culture, and PQL (people query language). The group also touches on the lessons from Microsoft's acquisition of LinkedIn, the rise of Slack, and how AI will integrate into our daily apps.
Duane Dufault discusses the tier one MQLs should be the sales team's top priority for outreach and engagement during the trial phase. These tier one MQLs are identified as the leads with the highest potential for ROI. Duane emphasizes the importance of promptly contacting these leads, as it greatly increases the likelihood of conversion. The sales team should approach these leads, follow a sales process, conduct a discovery call, provide a demo, and assist them throughout the trial. By doing so, Duane suggests that the leads will have a much higher chance of converting.This approach contradicts the traditional product-led growth strategy, where leads are not contacted until they have completed the trial and paid their first invoice. However, Duane argues that waiting until high usage activation and PQL measurement is a lagging indicator of conversion for enterprise accounts. Therefore, for higher ROI deals or leads in the product-led growth funnel, it is crucial to prioritize reaching out to tier one MQLs and guiding their trial experience.[00:00:56] Selling upstream challenges and myths.[00:03:10] Transitioning to sales-led process.[00:08:25] High usage activation and PQL measurement.[00:09:52] Product led growth strategies.If you get value from this episode, be sure to subscribe and share the episode with your friends, as we all can benefit from more positivity and leadership in today's society.Be sure to follow Duane Dufault on all the social platforms to get daily hits of tactical advice that you can take action on right awayLinkedin | Facebook | Instagram | Twitter | Youtube | TikTok
好久不见，大家端午快乐！聊了这么多AI技术，是时候聊聊更实际的问题：AI产品如何在海外做增长？近年来，我们看到越来越多的中国软件创业公司，尤其是最近涌现的 AI 应用类公司，都会考虑以国际市场作为第一站。PLG，产品驱动增长，也是大多数海外AI产品采用的增长和商业化模式。技术在变，Go-To-Market 的很多经验，万变不离其宗，或许可以让大家在探索的路上少走一些弯路。 Hello World, who is OnBoard!? 今天这位嘉宾，就是Monica 一直想要邀请来的硅谷软件领域的实战派大牛，Hila Qu。Hila 原来在大名鼎鼎的开源上市公司 GitLab 担任 Head of growth, 增长负责人。Gitlab之前，Hila也在硅谷几个不同阶段的ToB, ToC公司，担任过核心增长职位。硅谷最一线的创新公司是怎么实践PLG的，Hila 大概是最有发言权的人之一了。Gitlab从开源产品到收入超过4亿美金的上市公司，更是PLG的典范。这次与Hila长达两个多小时的对话，全都是一线实战干货： ToB, ToC 产品增长有什么不同？ 怎样的公司和产品适合PLG？ PLG模式需要怎样的销售？ 如何打造围绕产品的增长团队？ 产品早期数据分析体系如何搭建…… 真的可以拿出笔记本了。 Enjoy! 嘉宾介绍 Hila Qu（Twitter @HilaQu）, 前 Gitlab 增长总监，从0到1搭建增长体系。曾任硅谷Fintech 独角兽 Acorn 增长副总裁。现在，Hila 是硅谷顶尖产品与增长培训平台 Reforge 入驻企业家（EIR），也是独立企业增长顾问，服务企业包括 Nord Security, Replit StreamNative 等。 Hila 的 LinkedIn, 公众号：兜里有糖甜, MediumEmail: hui.qu.2009 艾特 gmail.com 我们都聊了什么 [01:57] Hila 的职业之路，从ToC 到ToB 公司做增长如何转型 [05:39] 正本清源，Hila 如何定义PLG？为什么说 PLG 不只是传统的 Growth hacking （增长黑客）？ [08:10] ToC 产品的增长与 SaaS 公司 PLG 有哪些核心差异？为什么说对于 SaaS 公司，获客只是“增长”的第一步？ [12:42] 怎样的产品适合采用 PLG ？ [16:19] 为什么 PLG 需要好的产品 onboarding 体验与销售两条腿走路？ [21:41] 公司的不同阶段，如何平衡大客户需求与 PLG 增长知之间的优先级？ [27:41] 给创始人的 PLG 101：怎样是一个完整的PLG 增长体系流程？ [33:53] 什么是产品体验的 Aha Moment？如何设计一个好的 Aha Moment? 有什么常见的误区？Gitlab 如何定义 golden user journey? [43:40] 企业发展不同阶段，PLG 产品的数据体系如何搭建？ [48:30] 收费：什么时候开始收费？为什么说收费体系的建立是一个动态过程？ [57:48] SaaS 产品定价如何设计和跟踪？我们能从 Netflix 上学到什么？ [61:44] Gitlab 的实践分析：如何设计实验？从activation 到 retention, 如何确定用户流程中的北极星指标？ [68:47] 什么是获客中的 PQL (Product Qualified Leads)? 什么是好的 PQL？ [74:54] 什么是一个好的增长实验？早期数据不足的时候，如何设计实验？ [79:47] 如何从0到1搭建增长团队？Head of Growth 入职第一件事应该做什么？ [87:37] 搭建产品数据分析体系，有哪些常见的挑战和误区？ [90:02] 招聘，招聘！什么是适合 PLG 的增长和销售人才？应该具备哪些能力？ [96:55] AI 它又来了：如何识别产品早期的“噪音用户”？ [99:41] Hila 提供哪些 PLG 相关的咨询服务？如何与你的增长顾问有效沟通？ [104:07] 快问快答！Hila 推荐了一本童书？！ 我们推荐的内容 Hila Qu 的公众号：兜里有糖甜 Hila 的书：《硅谷增长黑客实战笔记》 Hila 推荐的书：The Almanack of Naval Ravikant: A Guide to Wealth and Happiness Hila 推荐的书：Someday Hila Qu:【万字长文】SaaS增长新趋势：产品驱动增长PLG Hila 的英文文章：Five steps to starting your PLG motion Hila 的英文访谈: The ultimate guide to adding a PLG motion | Hila Qu (Reforge, GitLab) Lenny's Newsletter 参考文章 草根SaaS产品：如何定价，打包，涨价？ 45张PPT了解《硅谷增长黑客实战笔记》 GitLab's Hila Qu on What B2B Companies Can Learn About Growth from B2C - OpenView Hila Qu (GitLab): B2B vs. B2C Growth How to Build A Growth Model (Part 1) How to Build A Growth Model (Part 2) 别忘了，关注M小姐的微信公众号，了解更多中美软件、AI与创业投资的干货内容！ M小姐研习录 (ID: MissMStudy) 大家的点赞、评论、转发是对我们最好的鼓励！ 如果你能在小宇宙上点个赞，Apple Podcasts 上给个五星好评，就能让更多的朋友看到我们努力制作的内容，打赏请我们喝杯咖啡，就给你比心！ 有任何心得和建议，也欢迎在评论区跟我们互动~
In this episode, I sit down with Alexa Grabell, the Co-founder and CEO of Pocus, a startup that helps PLG companies to turn product data into revenue. This episode is a deep-dive into all things product-led sales with practical lessons for any founder considering layering in this motion to their growth strategy. Alexa also shares her journey of starting Pocus, how she and her co-founder spent 5 months on customer discovery and how that helped them in raising Seed and Series A rounds with a total of USD $23M in funding. Chapters: (00:00) Intro (02:09) What is Pocus? (02:59) Backstory behind the idea (05:03) Discovering product-market fit (06:54) Investment rounds (07:21) What's the company's ICP? (08:19) The grand vision for Pocus (09:08) Product-led growth (10:25) PLG and product-led sales explained (12:40) At what point does it make sense to layer in product led sales? (15:54) The user vs. the decision maker (17:12) Hiring a sales team (19:32) How Pocus helps PLG companies (23:10) Product qualified leads (PQL) (24:33) PCommon playbooks for PLS success (29:29) The challenge to shift from traditional sales to PLS (32:52) What is Pocus' primary growth motion? (34:00) Content and community growth strategies (35:29) Marketing's role in product-led sales (37:33) Navigating partnerships (39:04) Thinking about the competitive landscape (40:53) Key takeaways to start on PLS motion (42:32) Lightning Round Further listening: https://www.lennyspodcast.com/the-ultimate-guide-to-product-led-sales-elena-verna/ Guest Contact Info: linkedin.com/in/alexagrabell/ twitter.com/alexa_grabell pocus.com pocus.com/community pocus.com/playbook-library Sponsor: This podcast is brought to you by grwth.co. Grwth offers fractional CMOs, paired with best-in-class digital marketing execution to support early-stage startup success. With a focus on seed and series A companies, Grwth has helped a number of SaaS, digital health, and e-commerce startups build their go-to-market function and scale up. To learn more and book a free consultation, go to grwth.co. Get in touch with Mosheh: linkedin.com/in/moshehp twitter.com/MoshehP hello@pmfpod.com www.pmfpod.com
Highlights today include: PQL's Customers & Representatives will not be Impacted by Deco's Bankruptcy, Signify on Track to Double its Positive Impact on the Environment, New Family of Discrete Recessed and Surface Luminaires by Fluxwerx, Smallest 4 Watt LED Emergency Driver in the Industry – by Fulham, Up Close with Monica Kristo – LM&M Feb 2023, Lighting Engineer – Light & Health Research Center, Inside Sales Associate – Alva Lighting.
On this week's episode, Dustin is joined by Jen Igartua, CEO at Go Nimbly, to discuss the shift from MQLs to PQLs and how marketing and revenue teams can avoid the pitfalls they experienced with MQLs. In this episode, you'll learn how PQL's are focused on finding out when a prospect is ready for a human-led experience and how to avoid jumping the gun and getting ahead of that "aha moment" for your prospects. If you have any questions or want to learn more about Jen and PQLs, send her a connection request on LinkedIn! If you're a revenue operator, check out Go Nimbly's RevOps Foundations Guide to learn how you can identify the gaps and align your team on revenue operation goals.
Product-led growth (PLG) starts with the end user. And a healthy PLG engine depends on users discovering your product through no- or low-cost channels, including word-of-mouth, organic search, product virality, communities, and marketplaces.But Kyle Poyar of OpenView points out that as PLG companies grow past $10M annual recurring revenue (ARR), your larger accounts start to drive a disproportionate amount of revenue and incremental growth, and these accounts will typically begin their journey with a product interaction before they ever talk to sales. As a result, you'll manage to close larger and larger deals by simply allowing users to opt into a sales-led path or by adopting the product qualified lead (PQL) playbook. So, it's no wonder that PLG companies contemplate introducing an outbound motion.In this episode of Product Led Revenue, Kyle gets into the outbound side in a PLG motion and its benefits. Kyle and our host Breezy Beaumont discuss a new era for PLG, challenges for PLG organizations, and how to handle person email signups.
A growing number of SaaS companies are shifting from sales to product-led growth models. According to the Product-Led Growth Benchmarks 2022 report, 58% of B2B technology companies have a PLG motion in place.But product-led growth, while contributing to efficient growth, can be challenging. If you are moving from a sales model to product-led growth, you not only need to change the mindset of the teams but also adapt the teams to the new model. Although PLG stands for "product-led growth," the product team will have to work with marketing, sales, and sometimes services that will likely be outside your team.In this episode of Product Led Revenue, our host Breezy Beaumont welcomes Partho Ghosh, senior director of product and growth at Hootsuite. Partho and Breezy get into the benefits and challenges of the PLG model. They discuss organizational structure in the PLG model, the difference between PQL and PUQL, and the free and paid plans offered by Hootsuite.
Rejoice Fellow Travelers! Quantum Leap is back! Join hosts Allison Pregler, Matt Dale and Christopher DeFilippis to celebrate Quantum Leap's midseason return with our review of episode 9, which finds Ben as a bodyguard who must prevent the murder of a pop singer. Meanwhile, PQL tightens its net around the fugitive Janis Calavicci. Speaking of […]
A new episode of Quantum Leap? That sounds like A Decent Proposal! Join hosts Allison Pregler, Matt Dale and Christopher DeFilippis to witness Ben's first Leap into a woman – a bounty hunter chasing an escaped fugitive and dodging a boyfriend who has marriage on his mind. And back at PQL, Magic drops the bombshell […]
Summary Machine learning is a data-hungry approach to problem solving. Unfortunately, there are a number of problems that would benefit from the automation provided by artificial intelligence capabilities that don’t come with troves of data to build from. Christopher Nguyen and his team at Aitomatic are working to address the "cold start" problem for ML by letting humans generate models by sharing their expertise through natural language. In this episode he explains how that works, the various ways that we can start to layer machine learning capabilities on top of each other, as well as the risks involved in doing so without incorporating lessons learned in the growth of the software industry. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Your host is Tobias Macey and today I’m interviewing Christopher Nguyen about how to address the cold start problem for ML/AI projects Interview Introduction How did you get involved in machine learning? Can you describe what the "cold start" or "small data" problem is and its impact on an organization’s ability to invest in machine learning? What are some examples of use cases where ML is a viable solution but there is a corresponding lack of usable data? How does the model design influence the data requirements to build it? (e.g. statistical model vs. deep learning, etc.) What are the available options for addressing a lack of data for ML? What are the characteristics of a given data set that make it suitable for ML use cases? Can you describe what you are building at Aitomatic and how it helps to address the cold start problem? How have the design and goals of the product changed since you first started working on it? What are some of the education challenges that you face when working with organizations to help them understand how to think about ML/AI investment and practical limitations? What are the most interesting, innovative, or unexpected ways that you have seen Aitomatic/H1st used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Aitomatic/H1st? When is a human/knowledge driven approach to ML development the wrong choice? What do you have planned for the future of Aitomatic? Contact Info LinkedIn @pentagoniac on Twitter Google Scholar Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Aitomatic Human First AI Knowledge First World Symposium Atari 800 Cold start problem Scale AI Snorkel AI Podcast Episode Anomaly Detection Expert Systems ICML == International Conference on Machine Learning NIST == National Institute of Standards and Technology Multi-modal Model SVM == Support Vector Machine Tensorflow Pytorch Podcast.__init__ Episode OSS Capital DALL-E The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Get ready for liftoff, because we're boarding Atlantis! Enter the final frontier with hosts Allison Pregler, Matt Dale and Christopher DeFilippis as they discuss Ben's Leap to save a doomed Space Shuttle mission. And follow the rest of the PQL team as they hunt down Janice Calavicci in an attempt to uncover the mystery behind […]
Summary The increasing sophistication of machine learning has enabled dramatic transformations of businesses and introduced new product categories. At Assembly AI they are offering advanced speech recognition and natural language models as an API service. In this episode founder Dylan Fox discusses the unique challenges of building a business with machine learning as the core product. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Your host is Tobias Macey and today I’m interviewing Dylan Fox about building and growing a business with ML as its core offering Interview Introduction How did you get involved in machine learning? Can you describe what Assembly is and the story behind it? For anyone who isn’t familiar with your platform, can you describe the role that ML/AI plays in your product? What was your process for going from idea to prototype for an AI powered business? Can you offer parallels between your own experience and that of your peers who are building businesses oriented more toward pure software applications? How are you structuring your teams? On the path to your current scale and capabilities how have you managed scoping of your model capabilities and operational scale to avoid getting bogged down or burnt out? How do you think about scoping of model functionality to balance composability and system complexity? What is your process for identifying and understanding which problems are suited to ML and when to rely on pure software? You are constantly iterating on model performance and introducing new capabilities. How do you manage prototyping and experimentation cycles? What are the metrics that you track to identify whether and when to move from an experimental to an operational state with a model? What is your process for understanding what’s possible and what can feasibly operate at scale? Can you describe your overall operational patterns delivery process for ML? What are some of the most useful investments in tooling that you have made to manage development experience for your teams? Once you have a model in operation, how do you manage performance tuning? (from both a model and an operational scalability perspective) What are the most interesting, innovative, or unexpected aspects of ML development and maintenance that you have encountered while building and growing the Assembly platform? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Assembly? When is ML the wrong choice? What do you have planned for the future of Assembly? Contact Info @YouveGotFox on Twitter LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Assembly AI Podcast.__init__ Episode Learn Python the Hard Way NLTK NLP == Natural Language Processing NLU == Natural Language Understanding Speech Recognition Tensorflow r/machinelearning SciPy PyTorch Jax HuggingFace RNN == Recurrent Neural Network CNN == Convolutional Neural Network LSTM == Long Short Term Memory Hidden Markov Models Baidu DeepSpeech CTC (Connectionist Temporal Classification) Loss Model Twilio Grid Search K80 GPU A100 GPU TPU == Tensor Processing Unit Foundation Models BLOOM Language Model DALL-E 2 The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
HashiCorp started as an open-source tool.It's now a $6B public company with PLG, enterprise, and open-source products.Caroline Guo, HashiCorp's Head of Growth, shared with us how HashiCorp built a cutting-edge internal engine for its PLG motion.Here are some of the learnings we discussed on the latest episode of the Product Led Sales podcast:The data available from open-source products differs from sales-led and product-led products. HashiCorp had to retool it's approach to data as it moved into PLG.Product and GTM teams in PLG should be goaled against a standardized set of metrics. Listen to the episode to hear Caroline deep dive into the exact metrics HashiCorp cares about at each step of the user journey.How HashiCorp enriches its Salesforce to arm its sellers with product usage data and PQL scores…and much much more in the full episode!
Summary Machine learning is a transformative tool for the organizations that can take advantage of it. While the frameworks and platforms for building machine learning applications are becoming more powerful and broadly available, there is still a significant investment of time, money, and talent required to take full advantage of it. In order to reduce that barrier further Adam Oliner and Brian Calvert, along with their other co-founders, started Graft. In this episode Adam and Brian explain how they have built a platform designed to empower everyone in the business to take part in designing and building ML projects, while managing the end-to-end workflow required to go from data to production. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! Your host is Tobias Macey and today I’m interviewing Brian Calvert and Adam Oliner about Graft, a cloud-native platform designed to simplify the work of applying AI to business problems Interview Introduction How did you get involved in machine learning? Can you describe what Graft is and the story behind it? What is the core thesis of the problem you are targeting? How does the Graft product address that problem? Who are the personas that you are focused on working with both now in your early stages and in the future as you evolve the product? What are the capabilities that can be unlocked in different organizations by reducing the friction and up-front investment required to adopt ML/AI? What are the user-facing interfaces that you are focused on providing to make that adoption curve as shallow as possible? What are some of the unavoidable bits of complexity that need to be surfaced to the end user? Can you describe the infrastructure and platform design that you are relying on for the Graft product? What are some of the emerging "best practices" around ML/AI that you have been able to build on top of? As new techniques and practices are discovered/introduced how are you thinking about the adoption process and how/when to integrate them into the Graft product? What are some of the new engineering challenges that you have had to tackle as a result of your specific product? Machine learning can be a very data and compute intensive endeavor. How are you thinking about scalability in a multi-tenant system? Different model and data types can be widely divergent in terms of the cost (monetary, time, compute, etc.) required. How are you thinking about amortizing vs. passing through those costs to the end user? Can you describe the adoption/integration process for someone using Graft? Once they are onboarded and they have connected to their various data sources, what is the workflow for someone to apply ML capabilities to their problems? One of the challenges about the current state of ML capabilities and adoption is understanding what is possible and what is impractical. How have you designed Graft to help identify and expose opportunities for applying ML within the organization? What are some of the challenges of customer education and overall messaging that you are working through? What are the most interesting, innovative, or unexpected ways that you have seen Graft used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Graft? When is Graft the wrong choice? What do you have planned for the future of Graft? Contact Info Brian LinkedIn Adam LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Graft High Energy Particle Physics LHC Cruise Slack Splunk Marvin Minsky Patrick Henry Winston AI Winter Sebastian Thrun DARPA Grand Challenge Higss Boson Supersymmetry Kinematics Transfer Learning Foundation Models ML Embeddings BERT Airflow Dagster Prefect Dask Kubeflow MySQL PostgreSQL Snowflake Redshift S3 Kubernetes Multi-modal models Multi-task models Magic: The Gathering The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/[CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/?utm_source=rss&utm_medium=rss
Summary Machine learning is a data hungry activity, and the quality of the resulting model is highly dependent on the quality of the inputs that it receives. Generating sufficient quantities of high quality labeled data is an expensive and time consuming process. In order to reduce that time and cost Alex Ratner and his team at Snorkel AI have built a system for powering data-centric machine learning development. In this episode he explains how the Snorkel platform allows domain experts to create labeling functions that translate their expertise into reusable logic that dramatically reduces the time needed to build training data sets and drives down the total cost. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today! Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Your host is Tobias Macey and today I’m interviewing Alex Ratner about Snorkel AI, a platform for data-centric machine learning workflows powered by programmatic data labeling techniques Interview Introduction How did you get involved in machine learning? Can you describe what Snorkel AI is and the story behind it? What are the problems that you are focused on solving? Which pieces of the ML lifecycle are you focused on? How did your experience building the open source Snorkel project and working with the community inform your product direction for Snorkel AI? How has the underlying Snorkel project evolved over the past 4 years? What are the deciding factors that an organization or ML team need to consider when evaluating existing labeling strategies against the programmatic approach that you provide? What are the features that Snorkel provides over and above managing code execution across the source data set? Can you describe what you have built at Snorkel AI and how it is implemented? What are some of the notable developments of the ML ecosystem that had a meaningful impact on your overall product vision/viability? Can you describe the workflow for an individual or team who is using Snorkel for generating their training data set? How does Snorkel integrate with the experimentation process to track how changes to labeling logic correlate with the performance of the resulting model? What are some of the complexities involved in designing and testing the labeling logic? How do you handle complex data formats such as audio, video, images, etc. that might require their own ML models to generate labels? (e.g. object detection for bounding boxes) With the increased scale and quality of labeled data that Snorkel AI offers, how does that impact the viability of autoML toolchains for generating useful models? How are you managing the governance and feature boundaries between the open source Snorkel project and the business that you have built around it? What are the most interesting, innovative, or unexpected ways that you have seen Snorkel AI used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Snorkel AI? When is Snorkel AI the wrong choice? What do you have planned for the future of Snorkel AI? Contact Info LinkedIn Website @ajratner on Twitter Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Snorkel AI Data Engineering Podcast Episode University of Washington Snorkel OSS Natural Language Processing (NLP) Tensorflow PyTorch Podcast.__init__ Episode Deep Learning Foundation Models MLFlow SHAP Podcast.__init__ Episode The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Summary Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today! Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. Your host is Tobias Macey and today I’m interviewing Travis Addair about Predibase, a low-code platform for building ML models in a declarative format Interview Introduction How did you get involved in machine learning? Can you describe what Predibase is and the story behind it? Who is your target audience and how does that focus influence your user experience and feature development priorities? How would you describe the semantic differences between your chosen terminology of "declarative ML" and the "autoML" nomenclature that many projects and products have adopted? Another platform that launched recently with a promise of "declarative ML" is Continual. How would you characterize your relative strengths? Can you describe how the Predibase platform is implemented? How have the design and goals of the product changed as you worked through the initial implementation and started working with early customers? The operational aspects of the ML lifecycle are still fairly nascent. How have you thought about the boundaries for your product to avoid getting drawn into scope creep while providing a happy path to delivery? Ludwig is a core element of your platform. What are the other capabilities that you are layering around and on top of it to build a differentiated product? In addition to the existing interfaces for Ludwig you created a new language in the form of PQL. What was the motivation for that decision? How did you approach the semantic and syntactic design of the dialect? What is your vision for PQL in the space of "declarative ML" that you are working to define? Can you describe the available workflows for an individual or team that is using Predibase for prototyping and validating an ML model? Once a model has been deemed satisfactory, what is the path to production? How are you approaching governance and sustainability of Ludwig and Horovod while balancing your reliance on them in Predibase? What are some of the notable investments/improvements that you have made in Ludwig during your work of building Predibase? What are the most interesting, innovative, or unexpected ways that you have seen Predibase used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Predibase? When is Predibase the wrong choice? What do you have planned for the future of Predibase? Contact Info LinkedIn tgaddair on GitHub @travisaddair on Twitter Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Predibase Horovod Ludwig Podcast.__init__ Episode Support Vector Machine Hadoop Tensorflow Uber Michaelangelo AutoML Spark ML Lib Deep Learning PyTorch Continual Data Engineering Podcast Episode Overton Kubernetes Ray Nvidia Triton Whylogs Data Engineering Podcast Episode Weights and Biases MLFlow Comet Confusion Matrices dbt Data Engineering Podcast Episode Torchscript Self-supervised Learning The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Summary Machine learning is a force multiplier that can generate an outsized impact on your organization. Unfortunately, if you are feeding your ML model garbage data, then you will get orders of magnitude more garbage out of it. The team behind Galileo experienced that pain for themselves and have set out to make data management and cleaning for machine learning a first class concern in your workflow. In this episode Vikram Chatterji shares the story of how Galileo got started and how you can use their platform to fix your ML data so that you can get back to the fun parts. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! Your host is Tobias Macey and today I’m interviewing Vikram Chatterji about Galileo, a platform for uncovering and addressing data problems to improve your model quality Interview Introduction How did you get involved in machine learning? Can you describe what Galileo is and the story behind it? Who are the target users of the platform and what are the tools/workflows that you are replacing? How does that focus inform and influence the design and prioritization of features in the platform? What are some of the real-world impacts that you have experienced as a result of the kinds of data problems that you are addressing with Galileo? Can you describe how the Galileo product is implemented? What are some of the assumptions that you had formed from your own experiences that have been challenged as you worked with early design partners? The toolchains and model architectures of any given team is unlikely to be a perfect match across departments or organizations. What are the core principles/concepts that you have hooked into in order to provide the broadest compatibility? What are the model types/frameworks/etc. that you have had to forego support for in the early versions of your product? Can you describe the workflow for someone building a machine learning model and how Galileo fits across the various stages of that cycle? What are some of the biggest difficulties posed by the non-linear nature of the experimentation cycle in model development? What are some of the ways that you work to quantify the impact of your tool on the productivity and profit contributions of an ML team/organization? What are the most interesting, innovative, or unexpected ways that you have seen Galileo used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Galileo? When is Galileo the wrong choice? What do you have planned for the future of Galileo? Contact Info LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Galileo F1 Score Tensorflow Keras SpaCy Podcast.__init__ Episode Pytorch Podcast.__init__ Episode MXNet Jax The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Summary Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the outputs of machine learning models and the complexity involved in making it a reality. In this episode Shir Chorev and Philip Tannor explain how they are addressing the problem with their open source deepchecks library and how you can start using it today to build trust in your machine learning applications. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today! Your host is Tobias Macey and today I’m interviewing Shir Chorev and Philip Tannor about Deepchecks, a Python package for comprehensively validating your machine learning models and data with minimal effort. Interview Introduction How did you get involved in machine learning? Can you describe what Deepchecks is and the story behind it? Who is the target audience for the project? What are the biggest challenges that these users face in bringing ML models from concept to production and how does DeepChecks address those problems? In the absence of DeepChecks how are practitioners solving the problems of model validation and comparison across iteratiosn? What are some of the other tools in this ecosystem and what are the differentiating features of DeepChecks? What are some examples of the kinds of tests that are useful for understanding the "correctness" of models? What are the methods by which ML engineers/data scientists/domain experts can define what "correctness" means in a given model or subject area? In software engineering the categories of tests are tiered as unit -> integration -> end-to-end. What are the relevant categories of tests that need to be built for validating the behavior of machine learning models? How do model monitoring utilities overlap with the kinds of tests that you are building with deepchecks? Can you describe how the DeepChecks package is implemented? How have the design and goals of the project changed or evolved from when you started working on it? What are the assumptions that you have built up from your own experiences that have been challenged by your early users and design partners? Can you describe the workflow for an individual or team using DeepChecks as part of their model training and deployment lifecycle? Test engineering is a deep discipline in its own right. How have you approached the user experience and API design to reduce the overhead for ML practitioners to adopt good practices? What are the interfaces available for creating reusable tests and composing test suites together? What are the additional services/capabilities that you are providing in your commercial offering? How are you managing the governance and sustainability of the OSS project and balancing that against the needs/priorities of the business? What are the most interesting, innovative, or unexpected ways that you have seen DeepChecks used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on DeepChecks? When is DeepChecks the wrong choice? What do you have planned for the future of DeepChecks? Contact Info Shir LinkedIn shir22 on GitHub Philip LinkedIn @philiptannor on Twitter Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links DeepChecks Random Forest Talpiot Program SHAP Podcast.__init__ Episode Airflow Great Expectations Data Engineering Podcast Episode The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Summary Building an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Srivastava, co-founder of Basten, explains how the platform empowers data scientists and ML engineers to get their work in production without having to negotiate for help from their application development colleagues. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today! Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Your host is Tobias Macey and today I’m interviewing Tuhin Srivastava about Baseten, an ML Application Builder for data science and machine learning teams Interview Introduction How did you get involved in machine learning? Can you describe what Baseten is and the story behind it? Who are the target users for Baseten and what problems are you solving for them? What are some of the typical technical requirements for an application that is powered by a machine learning model? In the absence of Baseten, what are some of the common utilities/patterns that teams might rely on? What kinds of challenges do teams run into when serving a model in the context of an application? There are a number of projects that aim to reduce the overhead of turning a model into a usable product (e.g. Streamlit, Hex, etc.). What is your assessment of the current ecosystem for lowering the barrier to product development for ML and data science teams? Can you describe how the Baseten platform is designed? How have the design and goals of the project changed or evolved since you started working on it? How do you handle sandboxing of arbitrary user-managed code to ensure security and stability of the platform? How did you approach the system design to allow for mapping application development paradigms into a structure that was accessible to ML professionals? Can you describe the workflow for building an ML powered application? What types of models do you support? (e.g. NLP, computer vision, timeseries, deep neural nets vs. linear regression, etc.) How do the monitoring requirements shift for these different model types? What other challenges are presented by these different model types? What are the limitations in size/complexity/operational requirements that you have to impose to ensure a stable platform? What is the process for deploying model updates? For organizations that are relying on Baseten as a prototyping platform, what are the options for taking a successful application and handing it off to a product team for further customization? What are the most interesting, innovative, or unexpected ways that you have seen Baseten used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Baseten? When is Baseten the wrong choice? What do you have planned for the future of Baseten? Contact Info @tuhinone on Twitter LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Baseten Gumroad scikit-learn Tensorflow Keras Streamlit Podcast.__init__ Episode Retool Hex Podcast.__init__ Episode Kubernetes React Monaco Huggingface Airtable Dall-E 2 GPT-3 Weights and Biases The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Hey folks, today we're joined by Ramli John, one of my favorite marketers and someone I've admired and followed on Twitter for many years.Ramli got his start in marketing at PepsiCo as a Marketing Systems Analyst where he stayed for 4 years. After a co-founding stint Ramli moved to Toronto where started his freelancing career as a SaaS growth consultant. Along the way he also worked at a few different companies including SkyVerge which exited to GoDaddy.He also spent a few years teaching as a Marketing Instructor at big name spots like RED Academy, Centennial College and CXL Institute.He started what's widely known as one of the top marketing podcasts on the planet, Growth Marketing Today and he's one of the inspirations of our podcast here.He went on to join Product-Led - The leading community for PLG Pros founded by Wes Bush the famous author of Product-Led Growth. During his time there Ramli wrote his own book with Wes. It hit shelves last year: Product-Led Onboarding. I've read it twice and it's been a huge career growth lever for me.Now he's landed in what seems to be the perfect role, Director of Content at one of the top no-code onboarding tools in Appcues.Damn what a resume, what a journey, Ramli it's an honor to have you.Questions and topicsRamli there's a bunch of jumping off points here, I want to get into the podcast, the book and also the new gig but I'd love to start with an early career question.Early career at Pepsi and startupYou did a 180 when you went from a massive 100k + enterprise at Pepsi to then co-founding a startup. How wild was the transition and what advice would you have for listeners in big companies thinking of starting something one day?Podcast growthYou did Growth Marketing today for 4 years, I remember you posting once about how long it took you before you finally started to gain big traction. What advice do you have for people creating content with a small audience, sometimes feeling like they are speaking into the void.Teacher questionRamli, you spent a few years teaching, first at RED academy, a tech and design school, then at CXL Institute in their Demand Gen mini degree and also at Centennial College teaching a 14-week course on web analytics. What gave you the itch to spend 3 years teaching and maybe talk about the process of designing a course from scratch and all the work involved there.On writing your first bookTalk to us about writing your first book and the difference between the process of writing a course vs a book. Obviously Wes was probably a big inspiration but was this something you've always wanted to do and will there be more books in the future?PQL vs. PAIListeners have probably heard of PQLs by now, Product Qualified Leads or criterias that tell you someone has experienced your product or gotten some mileage in it. In your book, Product-Led Onboarding, you talk a lot about PAI, Product Adoption Indicators. Can you unpack the difference between both of those for listeners? Key onboarding milestonesMany people will dumb down onboarding to just getting users to the ‘aha moment' like it's something that magically unlocks onboarding challenges. You actually break down the nuance here and coin 3 different moments of value: Perception, Experience and Adoption. Can you walk us through a practical example of this?Conversational bumpersIn your book, one of my favorite analogies is your bowling analogy and how you compare onboarding emails and SMS messages as conversational bumpers to help users get their first strike. Unpack this for our listeners.Appcues, 6 months inYou're about half a year into Appcues leading the content team, teaching SaaS teams about onboarding and product adoption. When I saw you announce that I was like damn, that's the ultimate fit, Ramli gets to go back to SaaS and he gets to keep pumping out content about onboarding. I'd love to hear how the journey has been so far but maybe start by telling us how this opportunity came about.Happiness questionRamli, you've got a ton of stuff going on, you're a podcaster, an author, a frequent speaker, a soon to be dad and you're leading a content team at one of the coolest SaaS in the world. One question we ask all our guests is how do you remain happy and successful in your career? How do you find balance between all the things you're working on while staying happy? ---Ramli's linksTwitter: https://twitter.com/ramlijohn LinkedIn: https://www.linkedin.com/in/ramlijohn/LinkedIn posts: https://www.linkedin.com/in/ramlijohn/recent-activity/shares/ Product-Led Onboarding book: https://productled.com/book/onboarding/ Appcues: https://www.appcues.com/ Growth Marketing Today: https://growthtoday.fm/ ✌️--Intro music by Wowa via UnminusCover art created by SLB
What's up everyone – today on the show we're joined by exceptional martech mastermind: Dave Rigotti. He's the co-founder & CEO of Inflection.io, a marketing technology startup focused on helping companies with product-led growth.Before building his own company, Dave has had a fascinating career in marketing. He got his start at Microsoft working on the Bing marketing team just as the search engine was launched. He quickly discovered his love for growth and B2B marketing. Hen then spent half a decade at Bizible, a marketing attribution platform where he worked his way up to VP of Marketing – and was part of the successful exit to Marketo He spent a year at Marketo running ABM and demand gen before they were famously acquired by Adobe At Adobe, Dave was Director of Account Based Marketing focused on Marketo and Magento products Last year, while working on Inflection, he also launched the ProductLed.Marketing community which has more than 700 members and is continuing to grow. Dave, we're excited to have you on the show – thanks for taking the time. Questions we asked Dave:What is Product-Led Marketing or Product-Led Growth? What's the substance of PLG? What's the difference between PLG and customer led growth? How is PLG different from all the buzzwords that hit marketing over the years?People in tech love to find new ways of avoiding calling marketing marketing. Growth hacking, conversational marketing, community led growth and now product led growth… What do you say to all the folks who claim this is just another buzzword that will fade?How is PLG different from freemium and why does this instigate such brutal Twitter wars? Traditional: generate leads and serve sales. PLG: using your product as part of your GTM. More customer centric. Jon isn't active on social but I've witnessed my fair share of PLG debates on twitter.What do you say to folks who claim PLG has been around for decades (appcues, mailchimp) and that it's simply a repackaging of freemium and free trial models… that its the old marketing playbook for the SMB segment? Where does a PLG model make sense? Can this be done with enterprise software that requires integration and onboarding support? How do you shift to a PLG strategy when you're selling a B2B to enterprise and you require 1-2 weeks of integration and setup before end users can get a glimpse of the product in action. Do you think some B2B buyers prefer the sales led model? Sometimes I don't always have 14-30 days to pork around in a product and figure out on my own if this will meet my company needs… sometimes I need someone to show me around and tell me how itll solve my problems We can skip the MQL vs PQL debate, but how do you define a PQL when your product is constantly changing?Product usage data is the holy grail of data for PLG marketers. How do you see teams forming their marketing strategy around product usage, activation, and engagement?How is PLG a whole new game?You wrote an awesome piece for OpenView Partners that PLG is a whole new game for marketers. Can you walk us through what this new game looks like? What do you say when you hear the phrase PLG is just a product that sells itself? What are marketers in PLG companies doing differently to accelerate growth and revenue? How will PLG influence marketing technology over the next 10 years? What are your big predictions? Shifting gears, Dave, you've worked at some of the most recognizable marketing technology companies on the planet. Not only have you held senior roles in those companies, but you've been on the inside of two major acquisitions. Give us a sense of your career story and how you ended up as a co-founder and CEO in this space?What differences do you see working at enterprises versus running a startup? What lessons do you apply to your own startup, and what things do you try to do differently?Dave, you're a super busy guy. You're a dad, a husband, a startup founder, and community leader – one question we ask all our guests is how do you remain happy and successful in your career? How do you find balance between all the things you're working on while staying happy?--Dave RigottiTwitter - LinkedInProduct-led marketing communityInflection.io✌️--Intro music by Wowa via UnminusCover art created by SLB
Chase Wilson is a PLG specialist who focuses on solving product led-growth issues. Chase has just launched his new company, Flywheel, a platform that helps hasten self-serve revenue by bridging in-product behaviour and marketing tactics. And today, he sits down with us and tackles areas such as improving your range of vision by making effective marketing strategies. He will also discuss the significance of user experience and pointers on structuring data. And how the company's perspective has become a game-changer in giving customer experience. Show Notes [1:10] Starting Flywheel to solve the product-led growth issue [2:24] His idea of Product Qualified Leads? [5:05] The concept of MQL and why is it problematic? [6:52] Product led growth is highly flexible, and every journey that the company goes down is pretty unique. [7:08] How does changing your perspective of PQL, MQL, and SQL affect the customer's experience? [8:41] You need to take the product usage data, the sales interaction data as well as the marketing interaction data and put it together as an overall timeline. [10:41] Why is it called team qualified leads? [13:30] Advice to listeners About Chase Wilson Chase Wilson is an alumnus of the University of Chicago and is now the Co-Founder and CEO of Flywheel. Chase is known for his determination, creativity, and resourcefulness when it comes to managing his team and marketing insights. Chase's talent is his capacity to communicate his rare ideas in a way that inspires multitudes. Which makes him a force to be reckoned with in the industry. Links Flywheel Atlassian Profile Chase on LinkedIn Email Address: chase@theflywheel.app
Morgan sits down with Breezy Beaumont, Head of Growth & Marketing at Correlated, to discuss product qualified leads (PQLs) and how they transform a product-led growth strategy. They dive into building revenue intelligence, PQL vs. MQL vs. SQL, how sales changes in a product-led company, and building the revenue teams of the future. Get ready for a serious knowledge drop from one of the leaders in product-led revenue. Connect with Breezy BeaumontLinkedInWebsiteIn this episode, we cover:Product-led growth vs. ABM & Outbound (1:20)Sales teams in product-led companies (3:10)Adding value in the buyer's lifecycle (6:42)Product qualified leads – PQL (8:31)Why PQLs exist post-purchase (13:29)Common mistakes & successes (15:48)Mistakes in shifting a company to product-led growth (19:32)Creating centralized revenue intelligence (23:36)Judgment calls when creating PQLs (28:15)Breaking down silos (31:28)The end of the CMO? (34:47)Layering on traditional outbound in product-led firms (37:02)Product-led movements for enterprise buyers (43:08)Follow Nicholas Thickett on LinkedIn: https://linkedin.com/in/nicholasthickettFollow Morgan Smith on LinkedIn: https://linkedin.com/in/morganjsmithVisit our site b2bpowerhour.com to learn more about our upcoming live shows, community, and more.
Heroku is huge. Like, really really huge. It processes 60+ billion requests per day, and 13 million apps have been built with it.And Christopher Lauer has been there right from the beginning. He joined Heroku when it was just a handful of employees and has been building its self-serve engine ever since. In this episodes he shares a few golden nuggets about product growth, like why we should be more patient when working on growth, how to run the right experiments, and how to identify conversion signals.Chris has been working on PLG and PQL before these terms even existed. And what he taught me in our podcast wasn't anything I heard from anyone else!Connect with Chris on Linkedin if you have more questions. Visit our Product Led Sales blog for more insights, and follow HeadsUp on Linkedin and Twitter.If you have questions for me about PLG, growth, or marketing, here's my Linkedin and Twitter.
12.00 - Todays subject: Product Led Growth Ola Sars the founder of Spotify for Business, Soundtrack Your Brand shares his expertise on Product Led Growth (PLG). Ola shares why and how PLG today is their bible of strategy and how the organisation has changed during this transformation journey. Tune in to get hands on "how to do it" by Ola who have put 1000 hours research and trials and errors - and succeeded. Ola also talks about you as an investor should evaluate a SaaS with an implemented PLG strategy and why it increases the valuation of the company.
What the heck is a PQL? And how is one of the best tech companies out there implementing it? While it stands for product qualified lead, what that actually means is a little different for every company. In this episode, VP of Growth Thibault Imbert and senior director of revenue marketing, Morgan Palomares, of GitHub, explain how they define a product qualified lead, how they are implementing the mindset and workflows at Github, and how they plan to measure success.Like this episode? Be sure to subscribe, leave a ⭐️⭐️⭐️⭐️⭐️⭐️ review, and share the pod with your friends! You can connect with Matt Bilotti, and Thibault Imbert on Twitter at @MattBilotti, @, @thibault_imbert, and @DriftPodcasts, and Morgan Palomares on LinkedIn
Product-Led Growth (PLG) and B2B Sales - how do they co-exist in the evolving world of Go-To-Market strategy in B2B SaaS?On this episode of the Metrics that Measure Up podcast we are joined by Tim Geisenheimer, Founder and CEO of Correlated. Tim's first experience was leading a sales team in a PLG company where he experienced the change in how customers first experience a product. PLG requires the sales team to truly understand "HOW" customers are using the product, and adjust their sales outreach accordingly.First, we discussed the real and perceived risks that sales face in a PLG company. Tim said that sales is critical in PLG companies, though the skills and approaches change.Sales professionals must become product consultants who can help users gain the most value from the product. This requires the sales professional to have much deeper visibility into how the user is using the product.Second, we pivoted to discuss how PLG may impact pipeline development and the Sales Development function. One of the interesting changes is that the number of inbound leads increases dramatically in PLG companies, and SDRs will be required to learn the skills of inbound lead qualification instead of outbound led generation.This evolved into the creation of a Product Qualified Lead (PQL). A PQL is the scoring of a potential paying customer based upon how they are using the free version of the product. Some of the common variables used to score a PQL include log-in frequency and are they using high-value features. These two primary signals can be used to score a lead and help prioritize which users and accounts to reach out to first.Is "Value-Based Selling" still a key sales skill required to be successful? Tim went back to the need for B2B sales professionals to have better product knowledge, as it relates to the business value delivered.I pushed Tim on 'if" Customer Success is better positioned to help free users become paying customers? His response centered on the need for sales to become product consultants and that sales skills will be critical to optimizing free users converting into paying customers.Tim highlighted that a foundational element required for sales to be productive in PLG companies is having easy-to-access and understandable product analytics information available by the user, by account in the Customer Relationship Management platform. I asked Tim if "existing" CRM vendors can provide the required functionality for this? In response to this question, Tim highlighted that existing CRM tools are limited in providing this functionality, in part due to the relational database model that traditional CRM tools are based. The most common "metrics" that he sees Chief Revenue Officers being measured upon in PLG companies is "existing customer expansion" and "Net Dollar Retention Rate" which highlights one of the most attractive aspects of the "land and expand" model for PLG companies. Average Time to Expansion is a new leading indicator that PLG companies are tracking, with best-in-class companies seeing < 60 days from the time a client first signs up to expanding their "paid" usage.The classic "Close Rate" metric will be different in PLG companies, and not as important as other expansion-centric metrics. The other topic we discussed was if "sales comp plans" are changing in PLG companies. One change is assigning sales reps longer to accounts to shepherd the "expansion" journey. In fact, Snowflake has no CS resources as sales own total responsibility for existing client expansion.If you won revenue responsibility in a PLG company, this is a great listen to understand the evolving world of sales in B2B SaaS and Cloud companies.
FT Sor Rita: El arte de ligar en el 2021 ha cambiado. Por suerte en PQL tenemos un experto ligador; el mismo que donde pone el ojo, pone la bala…
On this episode of the Metrics that Measure Up podcast we are joined by Wes Bush, founder and CEO of ProductLed.Wes's journey to founding ProductLed and becoming a leading voice on Product-Led Growth started when he was responsible for demand generation for B2B SaaS companies. Specifically, when he led demand generation at Vidyard, they launched a new product that allowed them to evolve from "hosting videos" to providing a self-service tool that allowed end-users to quickly film, edit and share videos across email and social media channels.This launch quickly led to over 100,000 users and eclipsed the value to Marketing Qualified Leads almost overnight.This experience started Wes to think that there were not well-defined "playbooks" for companies to leverage as they started to evaluate and take the first steps into a Product-Led journey.Wes shared the PLG "Layer Cake" model, which highlights the foundational elements required for any PLG program: 1. Data Layer - getting a solid product analytics infrastructure in place 2. Product Layer - gaining deep insights into the User Experience 3. Conversational Layer - when and how to interact with the userIn a sales-led motion, traditionally only sales and marketing are deeply engrained in the process and the metrics that predict outcomes. In a PLG motion, every function can benefit from having access to the product analytics to inform their decision making such as: - How users are finding out about and then to start using a product (Marketing)- When users are at a point of activation, that the probability of converting to a paid user or enterprise-wide license is most likely (sales)- Where in the product on-boarding process do users start to attrite or stop using the product (products)- What features are used in the product that most correlate to customer retention (Customer Success)Tooling and platform infrastructure will need to evolve in Product Led companies. Specifically Wes sees a day when a platform that natively includes both product analytics information + internal outreach resource process information resides natively. Integrating product utilization information into existing CRM tools is a good short-term band-aid, but not an optimal solution long-term.Freemium versus Free Trials each have appropriate use cases, One of the primary variables for which model to use is how long does it take to reach that "aha moment", often referred to as the "Activation Point". For products that inherently have longer journeys to achieve real user value, a freemium product may perform much better than a time-restricted free trial period.Product Qualified Leads (PQL's) the #1 metric for the PLG motion. A critical component of the initial PQL is proven activation point(s) that predictably lead to higher conversation rates to paying customers. Another variable to consider is the ability to supplement product utilization data with Ideal Customer Profile (ICP) and Buyer Persona data to optimize both the conversion rates and validate the current understanding of the best target customer cohort(s).Time-to-Value is another key metric to capture, and factor into both the PQL criteria, but also into the product roadmap. The "SOONER" a user can experience value, the higher returns on your PLG investment.We wrapped up this episode with Wes providing three things to consider if you are evaluating whether PLG makes sense for your company:1. Technology is deflationary - users want to pay less over time2. Enterprise customer buying process is up 55% - find a way to decrease that for them3. Product experience has become part of the buying experienceDon't "TELL" them - "SHOW" them - a key tag line in the Product-Led Growth economy!!!
Product Analytics + Product Led Growth are critical partners for success.Ken Fine, CEO of Heap Analytics recently joined me on the Metrics that Measure Up podcast to discuss the inextricable linkage between these two concepts.PLG currently exists in a continuum of maturity, with some companies managing the entire customer lifecycle using a product-led motion, while the majority still using a traditional sales led motionKen believes the dominant Go-To-Market model in the future will be an artful combination of both a product-led and sales-led motion with a key focus on reducing friction across customer acquisition, expansion, and retention.Some products are better suited for PLG, and others that require more configuration, integration, and implementation assistance will be better served with a combination of product and human assistance.Ken highlighted that PLG is applicable across every stage of the customer lifecycle.PLG requires developing a hypothesis, testing the concept, then using data to determine the efficacy of the experiment, and then continuously iterating to optimize the performance metrics.Activation is the point in a journey where a user finds value from using a product in a PLG motion. Often, activation is referred to as the “aha moment” for a user.Identifying the “activation point” is a blend of art and science, with a strong focus on data that directly impacts company value impacting metrics such as new customers, revenue, share of wallet, etc.Ken’s experience includes being the CEO of a company that deploys Product Led Growth in combination with a Sales Led motion. When asked about the “predictive” data they found to predict conversion to paid, Ken highlighted that when users progressed to using their query tool "x" number of times, conversion rates are higher. In addition, when users leverage their integration feature, that provides a “step level function” in conversion rates.The number of times a PLG company reaches out to a free trial or freemium during free product utilization is an evolving process. Based upon a user reaching an “activation” point, they have a product specialist resource reach out, and are still developing a global heuristic using a “test and learn” approach to determine the number of outreaches that optimize the conversion rate.When asked about the best “resource” to reach out to free or freemium users, Ken highlighted that “it depends”. In their model, a solution consultant with deep product knowledge is the initial resource to reach out to provide product-centric assistance but also are trained to identify sales opportunities.Product Qualified Led’s (PQL) is a new metric that highlights when a free trial or freemium user has reached an activation point and is in a position to convert to a new or expanding customer. PQL’s are scored on different levels of qualification, similar to an MQL, though much more qualified based upon actual product usage and engagement. PQL’s go beyond hypothesis and use proven product usage analytics that are predictive of conversion.In summary, Ken shared that if you have traditionally had a sales-led model, that change management is a critical, yet often overlooked element of deploying a PLG model. In short Ken shared - “NAIL IT BEFORE YOU SCALE IT”!If you are considering or recently started your PLG journey, Ken and Heap Analytics are a great follow.
Sam Crowell Richard is responsible for growth across the OpenView Partners portfolio. OpenView Partners is a leader in advocating Product Led Growth strategies across their portfolio, which includes leading PLG companies including Calendly.Sam has invested in her career preparing for a growth role in a PLG focused venture capital firm, including learning the secrets of digital marketing in a digital agency, and then for 5+ years at an early stage, PLG company, Dispatch, ultimately acquired by Vista Equity, a leading Private Equity firm in the B2B SaaS and Cloud industry.Product Led Growth companies see 80% - 90% of their initial freemium/trial users acquired using digital marketing techniques such as SEO, though conversion to paid customers only occur 50% - 60% of the time without the involvement of a human resource. The type and complexity of the solution directly correlates' to the requirement for the engagement of a human resource to assist the user to become a paying customer.The approach and skillset of the resource initially reaching out to the PLG acquired user is different than in a traditional sales-led environment. Additional insights, including how they are using the product, possibly areas they have not yet experienced, and allows the vendor's initial outreach to be with a much warmer, engaged led.Product usage, often derived from a Product Analytics platform is a critical foundational component for a PLG company. One of the areas of focus is how product usage information is provided to the resources responsible for user outreach. A new consideration for revenue operations is how to provide product usage information within the construct of CRM environments. Though not a primary topic today, the need for a different CRM for PLG companies may be a new market opportunity.Top Metrics for PLG companies:1. Organic Search: What % of traffic and new users come from SEO2. User Journey Metrics: 3. Activation Rate: where people are finding value in your product4. CAC Payback Period: - must be much quicker for PLG companies, with < 12 months being great and some even reaching < 6 months. Price point is a key factor in this benchmarkActivation rate is a nuanced metric, as it is different for every solution.1. Does action correlate to positive business outcome a. 50% conversion rate to paid2. Activation point activity or task completed by > 50% of trial users3. Activation point reached quickly a. 1 week - 1 month40% - 60% of PLG free users represent "Zombie Users" which will never convert. Activities by agents, robots, and poor fit users represent this category and should be identified as early as possible.We also discussed "Product Qualified Leads" or PQL's. This is a key metric that is calculated based upon product utilization by free/trial users. It's interesting that only 35% of PLG companies are using PQL's. This is an increase over the past year, but still not being used by a majority of PLG companies. PQL's provide a unique opportunity to decrease the friction and resulting lack of alignment that MQL's have introduced between sales and marketing.Natural Rate of Growth is a new "PLG" centric metric that OpenView Partners uses to understand the organic growth rate of PLG companies.Lastly, if you are an early-stage professional considering a career in B2B SaaS, Sam shares her advice that you create your own rotational program to gain a well-rounded understanding of how B2B SaaS companies operate across all functions in the company.Sam provides a wealth of insights and advice for any B2B SaaS company considering or are in the early days of a PLG strategy.
Randy Reid discusses the Get-a-Grip Podcast featuring Edward Bartholomew, Deco Lighting and PQL announce a strategic alliance, NLB is offering a video on 3D printing, John Palk of SESCO has an article on 3D printing in designing lighting (dl), and LEDdynamics is now powered by the sun!
Sales and marketing have evolved significantly in the past few decades, especially in the SaaS space. In the 90s, for example, we had sales-led growth, with sellers doing cold calling and hitting the phones. In the 2000s, it was about marketing-led sales or marketing-led growth, with events, inbound leads and SDRs doing outbound prospecting. Now, according to my guest in this episode of the Modern Selling Podcast, we are moving into a new era of product-led sales. Doug Landis is a Growth Partner at Emergence Capital. In this role, he is responsible for capturing, creating and sharing go-to-market strategies and ideas with the Emergence Capital portfolio companies and the greater SaaS community. Join us in this conversation about the future of the sales force and how to better qualify your leads. What is Product-Led Growth? “I would argue in this generation and especially over the next three to five years,” says Doug, “you're going to see a tectonic shift to product-led growth, meaning the product is leading every single interaction. Instead of us doing outbound prospecting to a brand new client cold, we're actually reaching out to people who are deeply already involved and getting value out of our product.” He gives the example of Slack, Dropbox or Twillio, where people just go to their websites, enter some information and can start using the product right away, getting full value. In this scenario, people have a need and instead of having a sales conversation with a rep or requesting a demo, they can try a product for free and immediately know and understand whether it is the right fit for them, the solution they were looking for. After customers try the product, an SDR would call them and help them get more value out of it. “So now an SDR’s role is different,” Doug says, “because I'm no longer cold calling people who I think are a good fit. I'm actually looking for signals in the product based on how you're using it to call you and help you learn how to get more value out of the product and in doing so you will then become a paying customer.” This scenario implies we are moving from a Marketing Qualified Lead (MQL) or a Sales Qualified Lead (SQL) to a Product Qualified Lead (PQL). And when working with PQLs, both sellers and marketers have a different role in the buying process. SDRs become Product Specialists, now having conversations with prospects who have tried the product, and marketers focus on leading people to a product trial, not a web form. Listen to the whole episode to learn Doug’s predictions about the future of SDRs and how their role will dramatically change. From SDRs to Product Specialists Here are some ways Doug sees the SDR and AE roles shifting: Sales conversations will focus on discovering why a free user should turn into a paying customer. It’s all about upselling opportunities and how the product could be used more broadly across the client’s organization. Using data on product usage to create more sales opportunities. Although many SaaS companies are already doing this, Doug predicts it will be more common in the next two years, as companies ask themselves, how do we get people into our product with the least amount of friction with the most amount of value? This is the future of the sales force and as sales leaders, we must think differently about the characteristics of our sellers and the metrics we use to measure sales success. “What we're looking for is more product signals versus the prototypical marketing signals, like the MQL and the SQL,” Doug says. Listen to the episode to hear how the PQL is more valuable than the MQL, and why Doug thinks the MQL actually doesn’t exist (Hint: they are just contacts until someone talks to them and validates they are a good fit). Also learn why modern sales organizations must change the way they qualify leads and the real job of an account executive.
Katie Mitchell is Head of Marketing at UserLeap, the platform designed for modern user research. Also hailed as the first continuous research platform, UserLeap uses artificial intelligence to help UX researchers and product managers obtain quantitative insights at scale so they can make informed product decisions in just hours. In this episode, Katie talked about her primary responsibilities, their marketing plans for 2021, and her advice to those who want to go product-led. Show Notes [00:44] Her main responsibilities right now [04:07] Difference in her roles and responsibilities [06:47] He definition of PQL [08:29] What has been working in terms of her transition into becoming product-led [13:16] What their plans are for 2021 in terms of marketing [15:26] The role of salespeople in their product-led journey [19:20] Her advice to those who want to go product-led [20:16] Where people can find out more about her and UserLeap About Katie Mitchell Katie Mitchell is a curious problem solver and a DC-based marketer. She is also the Head of Marketing at UserLeap. For over a decade, she has worked at various SaaS-based companies across B2B and B2C. She helps them elevate their brands and accelerate their growth. Before that, she has worked with large consumer brands like Campbell’s Soup Company, PBS, and BB&T Bank. Profile Katie on LinkedInKatie on TwitterUserLeap
Levan discusses the myths of PLG and how to effectively implement it across your product, marketing and sales teams.PEOPLEGuest: Sam Levan, CMO & Cofounder, MadKudu, a lead scoring platform that helps you optimize your marketing by predicting expected revenueHost: Anil Hemrajani, Founder of Startup SidekickTAKEAWAYSBe very clear on the type of PLG motion you want (e.g. self-service, land & expand); it's difficult enough implementing just one.Redefine the role of sales in PLG, so there's no friction.TIMELINE01:09 – Can you provide us an overview of MadKudu? Our goal is to make every startup and marketer successful. Many CEOs don't trust their marketing team, often because the leads aren't qualified. The problem is marketers today don't have enough visibility into where the quality leads are in the funnel. We mostly help B2B SaaS startups.02:35 - What is Product Led Growth (PLG)? It's somewhat opposite of sales led growth, which makes the product available after you buy it. With PLG, you need a product as the main driver of revenue.03:22 - Why is PLG hot right now, even though the concepts have been around for sometime? For the past decade, we've seen freemium, free trials and so on. What's changed are financial successes (e.g. Zoom, Slack, Notion, InVision). Investors now see this as a fantastic, financial machine, growing from nothing to a billion dollar valuation.05:20 - What are some myths about PLG? PLG sounds great but it's a lot harder to make it work. It's not all self-service; there three sales models: low-touch credit card sale, land & expand (e.g. Slack) and more traditional models (e.g. content is for inbound leads with follow-up sales) -- companies must be clear on the sales model.07:15 - Does this mean you don't need sales people anymore? Absolutely not; you do need sales people if you're selling to the enterprise. Even companies that claim they don't have sales people, actually do (e.g. masked as product specialists). 08:20 - How do you achieve the virality that companies such as Slack and Zoom have? One of the mistakes product-led companies make is forgetting their customers. They have great analytics/dashboards (e.g. retention). When companies claim they are not getting the growth (e.g. activations), you have to remove the noise in all the signups by segmenting your leads (e.g. students versus buyers).10:30 - What are some of the metrics that you look at? You have to pick your metrics carefully, since they become your northstar. PQL and PQA (product qualified leads and accounts) are two we look at. These matter because generating revenue is a team effort. For example, when leads come in, sales people start calling the leads even if the leads aren't any good -- you have to identify the qualified leads to see which prospects are ready to begin having a conversation. It's important to have that agreement between product, marketing & sales on when a lead is qualified -- essentially a SLA between these teams. 13:55 - What are your thoughts on funnels versus flywheel charts? It's understanding the customer journey, different personas (buyers, users, stakeholders). The flywheel should include buyers and users, not just users.15:00 - Takeaways (see above)
Phil Corson is the senior product manager at 7shifts, an intuitive employee scheduling and management software that’s designed for the restaurant industry. Many restaurant managers have been using the software to minimize the time spent on management logistics and to reduce labor cost. 7shifts also comes with free mobile apps (Android and iOS) to ensure managers and employees have everything at their fingertips. In this episode, Phil shared what their product-led journey has been like, how their shift to product-led happened, and his advice to product-led companies when it comes to PQL. Show Notes [02:50] What their product-led journey has been like so far [04:15] How their to product-led happened [07:08] Some of the benefits of the product-led team defining PQL [09:06] Who gathered the data to figure out what tier 1 lead is [09:47] How their team iterate on the definition of PQL [17:38] The threshold when a lead becomes valuable for them [19:18] Changes he’d like to see [21:00] What he’ll tell himself a year ago if he can go back in time [21:32] The journey behind the buy-in [23:59] His advice to product-led companies when it comes to PQL [25:54] Where people can find him online About Phil Corson Phil Corson is the senior product manager and the team lead of the product led growth team at 7shifts. Phil has over 5 years of experience in B2B SaaS and product management. He is passionate about solving problems and bringing value to customers. Phil also has a proven track record for creating million-dollar software products for various industries. Profile 7shiftsPhil Corson on LinkedInPhil Corson on Twitter
Karishma Rajaratnam is the head of growth at ChartMogul, the world’s first subscription data platform is designed to help make consolidate and clean billing data easy. ChartMogul can help businesses understand the dynamics of their business and see where they need to focus their efforts. In this episode, Karishma talked about the marketing channels they have tapped, how they work seamlessly with their sales team, and how the sales team deals with warm outbounds. Show Notes [02:19] What ChartMogul is [03:14] What their primary focus is right now [03:59] How they work with the sales team [06:30] Tasks their sales team are currently handling [07:38] Marketing channels they have tapped [10:55] What her functions are [12:42] How they work seamlessly with their sales team [13:47] How often they get together with the sales team [15:10] How the company’s sales functions has evolved [15:15] Their basic version of a PQL [18:08] How the sales team deals with warm outbounds [19:04] Tools they use to segment the experiences [20:47] Piece of advice she would like to give to product-led companies [22:30] Where people can find out more About Karishma Rajaratnam Karishma Rajaratnam is a brilliant marketer passionate about growing the SaaS business. She is also the head of growth at ChartMogul where she leads the acquisition, onboarding, and engagement. She is also in charge of partner marketing, content and customer marketing, and product marketing and GTM. Karishma is also advisor to early-stage founders and works with startups like TechStars, SaasBoomi, and Co.labs. Profile ChartMogulKarishma Rajaratnam on TwitterKarishma Rajaratnam on LinkedIn
Sin filtros y con una invitada que puso PQL de cabeza… David, Kathy y Gabo nos cuentan sus fetiches y fantasías… prepárense para lo que sea.
The GrowthTLDR Podcast. Weekly Conversations on Business Growth.
Since the inception of Marketing Automation, the MQL has been a staple in how B2B marketing companies generate revenue. An MQL is a lead your business has generated, and the possibility of it turning into a customer is higher than other leads. The reason you mark it an MQL is because it has certain demographic characteristics that would suggest it's a good fit for your product/services. Plus, it's also demonstrated an intent to buy via engagement patterns that you track - opening emails, visiting web pages, downloading content. A PQL is no different, but the engagement patterns come from actual product usage, making it a far more valuable lead. The person has already used your product and got value from it before your sales team ever talk to them. In this episode of the GrowthTLDR we talk about how you can build an early PQL model for your company.
We kick off our coverage of the final season of our beloved show. Season 5 of “Quantum Leap” premiered with the episode fans were told would never happen: Sam leaps into Lee Harvey Oswald. Stuck leaping into the alleged assassin at various points in his adulthood from 1957 to 1963, Sam takes on more and more of his leapee's personality, raising the dangerous possibility that history will repeat itself. Meanwhile, Al interrogates the real Oswald back at PQL, trying to uncover the conspiracy of who was really responsible for killing Kennedy. We pick apart and put back together a fan favorite - an episode Bellisario wrote as a direct rebuttal to Oliver Stone's “JFK”. We also talk about some of the real-life conspiracies surrounding the assassination. It's a long twisting discussion with emotional highs and lows and an exploration of what works, what doesn't, and what may have in a different world. Ultimately, the human cost of this real-life, world-shattering event cannot be done justice, but we try to make sense of it all in some way - striving to contextualize the episode - and the real life event it leads to- in the time in which we live.
In this interview with Moritz Dausinger, we are discussing the concept of Product Qualified leads and how they can help us improve the quantity and the quality of our conversions. Moritz is sharing with us his definition of PQLs which doesn't only include activation criteria and helps us understand how can we optimise our sales process. Key Takeaways: - What is a PQL? - Why should we care about PQLs and how to define them? The interviewee: Moritz is the CEO of Refiner. The interviewer: Aggelos Mouzakitis is the founder of Growth Sandwich. He created Growth Sandwich, back in 2017 with a sole vision: to help promising early-stage teams get their products to market in a solid manner. He has worked or trained more than 500 marketers and founders on how to get to the market with the right mix of tactics and a product that drives engagement and happiness. About Growth Sandwich: Growth Sandwich is the first European Product-led Go-to-Market Strategy agency. We specialise in helping SaaS products and businesses that operate in the subscription economy. Our approach is 100% customer-centric and we help post-Product/Market fit companies establish a repeatable selling motion and recurring revenues.
Tyler Bench is king of the wide funnel. With over 700,000 new Lucidchart registrations a month, Tyler has a big task as Director of Demand Generation. However, with great user numbers, comes great responsibility. Lucidchart’s PQL approach strategically aligns marketing and sales, essentially changing the customer experience within SaaS while taking competitive advantage of the landscape. Listen to this episode of Hey Salespeople to learn more about Tyler’s strategy when it comes to PQLs and his three part axis of qualification. Visit Salesloft.com for show notes and insights from this episode.
Nosso convidado para o podcast de hoje é Ricardo Palma, Product-led Growth na RD, responsável por ajudar a equipe de produto a manter o RD Station em constante melhoria. Ricardo é fera quando o assunto é RD Station. Nesse bate-papo ela irá nos contar muito sobre o que é um PQL, como utiliza-lo ao seu favor e muito mais.
Wes Bush, a product-led growth expert for B2B SaaS, gives super crisp information about product-led growth. He explains in brief about product qualified leads (PQL) and how to determine one for your SaaS. After listening to this podcast, you'll understand if your product is right for product-led growth and how to implement the product-led growth strategy. Intercom study - https://www.intercom.com/blog/designing-first-run-experiences-to-delight-users/ Wes on LinkedIn - http://linkedin.com/in/wesbush/ Traffic is Currency - https://trafficiscurrency.com/
In the old days, we create qualified leads by offering up lots of content. Our prospects found our content, filled out a form, downloaded the content, and then we reached out to them. This strategy still works, but over the last few years, we realized that what people like more than free content is free stuff. And it turns out that giving away software, a product, or service is an even more effective way to qualify leads and move prospects through the buyer journey. [0:43] Question: What is a Code Funnel? The code funnel is an engagement strategy that allows people to have an experience with your product or software before you ask them for any commitment. This try before you buy (TBYB) strategy allows users to begin extracting value from your product quickly while creating product qualified leads (PQLs). If you’re not sold on giving away your product or service, don’t worry. It’s not uncommon for this strategy to run into some resistance at first. Embracing a code funnel or TBYB strategy has a lot of benefits. First of all, it allows you to help more people. Your product or service is solving problems and helping your customers, so the more people who are making use of it the more people you are helping. Hand in hand with this, TBYB means your product has a quicker time to value. Everyone wants solutions and help right now. The faster you can put your solutions into the hands that need them, them better. And the more prospects use your software or product, the more qualified they become. Qualified prospects rise to the top because the people who are actually using the software are more qualified than those who aren’t. This means that when you do reach out to these PQL’s you’re less disruptive because they are already qualified and ready to talk to you and you know exactly what to offer them and when to offer it. [11:13] Dan's Rant You can make a code funnel out of anything! Don’t say X product/service can’t be given away so we can’t use the code funnel. There is some way to implement try before you buy for every product or service. [16:23] Todd's Truth “The best way to engage buyers is to anticipate what problems they want to solve, diagnose how they research solutions, and help them solve those problems is a fast, comprehensive, and personalized way.” Companies that make it easy will be the ones that win. [19:02] 3 Takeaways Review your engagement process and strategies now Ask buyers what they want to see before buying Think of three ways people can try before they buy Links Inbound Organization Assessment (https://www.inboundorganization.com/inbound-organization-assessment) Inbound Organization Audiobook (https://www.audible.com/pd/Inbound-Organization-Audiobook/1469098903) We’re extending the MSPOT contest! You can download the MSPOT template, submit it for review, and anyone who submits an MSPOT will be entered into the contest. The first-place winner will win an hour to review their MSPOT with Todd and Dan! Download and Submit Your MSPOT (https://www.inboundorganization.com/mspot-review) P.S. Are you enjoying the podcast? Did you read Inbound Organization? Taking a quick moment to rate and review Inbound2Grow and Inbound Organization on whatever service you use is the best way to let us know how we’re doing. Your ratings and reviews make a big difference, and we appreciate you taking the time to provide your feedback. Thanks to Rebecca Miller our podcast editor, social media coordinator, and blogger and to Zachary Jameson for producing the audio for the podcast. Check out Zachary on Upwork if you need podcast audio services.
How did SaaS startup Woopra increase lead to customer conversion rates while also shortening their sales cycle and improving the efficiency of the sales team? This week on The Inbound Success Podcast, Woopra Head of Partnerships Elle Morgan shares the company's product qualified lead model and breaks down specifically what a PQL is, how it differs from an MQL, and the specific behavioral indicators that Woopra evaluates to determine whether its leads qualify as PQLs. Listen to the podcast to learn more about building a product qualified lead model, nurturing PQLs, and how this approach can improve sales and marketing alignment. Transcript Kathleen Booth (Host): Welcome back to The Inbound Success podcast. I'm your host, Kathleen Booth. And today I am happy to say, my guest is Elle Morgan, who is the Head of Partnerships at Woopra. Welcome, Elle. Elle: Thank you for having me, Kathleen. Kathleen: Tell our audience a little bit about yourself. What do you do? What's your background? And what does Woopra do? Elle: Yeah, absolutely. I lead our Partnerships Team here at Woopra. I have, you know, been in some fashion of marketing for almost 10 years now, and have had my hands in really every aspect of marketing. So I've done a little of mid-management, corporate communications, to growth, some digital, and everything in between, primarily with start-ups, at least for the last seven years. I realized I really thrive in that build versus manage stage of an organization. You know, in a small startup company, every single thing that you do makes an immediate and measurable impact. Even though there are no certainties or things to fall back on, there's the ability to really constantly iterate and try new new things without the red tape that you might have to fight through in a bigger organization. About 10 months ago, I took on a Partnerships role here at Woopra, partially because of this PQL model that we're gonna speak about today. But in my position now, Partnerships has really, for me, been a combination of marketing, while creating content designed around customer use cases, integration, best practices, and how to successfully combine different technologies to yield greater results, along with a taste of sales and the relationship-building aspects that kind of keep me challenged, and let's me learn from our customers in a way that really inspires my writing by their questions and the stories that I hear from them. Kathleen: It's interesting that you say you really love the start-up environment because, you know, I was looking through your background on LinkedIn, and I noticed you've spent a lot of time in the San Francisco Bay area, and I feel like wow, that's ... What a great place for somebody who likes the start-up world to be. It's kind of like being a kid in a candy store. Elle: It is. I, you know, started in the start-up scene in LA, in the little Silicon Beach area. I'd always been at bigger companies and that passed, but when I got my first taste, I realized that, okay, if I really wanna do start-ups the way that I'd like to, I have to be up here in the Bay Area to really feel like I've given it my all. Kathleen: That's great. And what a great city to live in, even just aside from the start-up world. Elle: No, I can't complain. Kathleen: Yeah. It's such a neat place. So Woopra to me is really interesting. And, you know, so many of our listeners are marketers, and I looked at the website and the platform, and was so intrigued because it's all about understanding the customer journey. And I want you to talk a little bit more about that, but you know, that's the thing I think that more than potentially anything else, we as marketers talk a good game about customer journey mapping, but I don't think we really do it. Some people don't do it at all. And if most of us are doing it, we're not doing it really well and there's a variety of reasons for that. You know, it could be the amount of effort that has to go into it. It could be trying to get your head around all of the data. In any case, tell us a little bit about Woopra and what it does, and then we can kind of get into some of the other goodies that we're gonna talk about. About Woopra Elle: Yeah. So Woopra is a customer journey analytics platform. Essentially what that means is we have our own tracking technology to understand who is on your website, where they came from, the pages they're browsing, the usual things like that. We also track product engagement data. So from the minute they come to your site, sign up for your product, what different types of features are they engaging with? Where do they go from there? How many people become customers? And then, after that point, are they engaged or not? What does a healthy customer look like? But another big piece to that puzzle is bringing in data from other touchpoints in the customer journey. So if we think about the journey outside of like the silos of MQL, are they opening your emails? Are they engaging with you in LiveChat? How many support tickets does a person answer? Woopra has this, you know, integrations layer. The idea is that you can bring in data from essentially any touchpoint in the customer journey to build up these reports that show whether or not people who take these certain combinations of actions are more or less likely to both convert, become a healthy customer, that right customer fit. Also being able to leverage that data to drive more personalized interactions and communications, and campaigns throughout all of those different touchpoints. Kathleen: Neat. And from what I saw on the site, it looks like you also provide a very visual interface. You know, you're able to deliver information on that customer journey in a way that that is super visual, and therefore very easy to digest and understand, which I think is just- Elle: Yeah. Kathleen: ... amazing. Elle: Funny for me. Before joining Woopra, I'd never gone outside Google analytics for my insights, and the data that was at my disposal and like my marketing tool. So I knew clicks, conversions, like maybe who was opening my emails. But I wasn't able to say, "Okay, are people engaging with this product?" And then opening my emails, "And do those two have an impact on one another?" So, for the first time, I'm certainly not a data analyst, but I feel like the platform makes it easier for me to go in and essentially ask any question, or have access to that little data to drive what I do. Kathleen: Now, there's a clear use case for SaaS companies to use Woopra because you are able to provide in-app usage analytics, in the sense of like the customer's adopting the product to this extent, and here's how that influences the degree to which they, you know, maybe go from freemium to paid customer, et cetera. What about outside of the SaaS world? I mean, are there applications there as well? Elle: Yeah, absolutely. I would say like 50% of our client base is SaaS. Another, probably, 40% is E-commerce, and the rest is scattered. So it's really anybody who has a strong digital presence, who's looking to understand on the, you know, E-commerce side, what products are they purchasing? Where did they come from? Did they come back and make another purchase? You've got, you know, the on-demand companies like Uber who wanna understand are people making repeat rides? Are they canceling? What is the distance traveled? Any time you're running your business with a strong digital presence, I think there's 100% an application to leverage data across the board to design a better, or more connected, customer experience. Kathleen: Yeah, and the cool thing is there is so much data available to us as marketers. It's just making sense of it all, you know? Like I have all of that data in my various systems. I mean, we use HubSpot, Google Analytics, SEMrush, Lucky Orange. We have so many different platforms, and it's all out there. But it's just tying all those various pieces together, and kind of extracting insights that I think is the real challenge. So that's really neat. Elle: Absolutely. Great. Nurturing Product Qualified Leads Kathleen: Well, we were gonna talk a little bit about nurturing product-qualified leads, because you know, when you and I first talked, I was asking you with Woopra's marketing, what's really contributing to your success as a team? So I'd love to just hear a little bit more about that from you. Elle: Yeah, absolutely. So before I took over Partnerships, when I first joined the company, I was leading our Marketing Team, and because Woopra runs on a freemium model, where essentially people can sign up for the platform and play around with it as long as they like up until they have the need for some more advanced analytics features, we realized that we were having tons of signups, organically, which is great. We had about, you know, 1500 signups a month to the product, primarily coming organically through word of mouth, SEO, and a very small amount through some Adwords brand-name campaigns that we are running. But we also only had two salespeople. And for two salespeople, 1500 leads, they didn't know where to start, they didn't know who was an actual qualified sales opportunity of those people who were signing up, and then testing out the product. But we were tracking essential customer engagement metrics. So we knew where people came from, the content they engaged with, if they chatted with us on LiveChat, and I was trying to look at those top-level MQL metrics. Were they reading blog posts? Were they browsing our appraising page? But we felt like even with that, combined with the 1500 leads, was not enough to qualify these sales opportunities. And even worse, when we ran the numbers, we found that just because somebody read six blog posts did not necessarily make them more or less likely to be qualified as a paying customer. We had a lot of students using the platform, a lot of small-time bloggers, who just were never gonna reach that point where they actually wanted for a full-blown paid analytic solution. Kathleen: I'm so glad you just said that, because I used to own a marketing agency, and now I head up marketing for IMPACT. And in both cases, when I've dug into the data, actually the people that wind up converting and becoming customers tend to look at less content prior to their conversion than the people who don't convert. And it's so counter-intuitive but it is the story that only good data can tell. Elle: Absolutely. Kathleen: If you didn't dig deeper and really glean that insight, you could waste a whole lot of time on leads that were not likely to convert in the first place. Elle: Yeah. And it's like an honest mistake. I mean, I was trained in marketing to look at those things and then to say, "Okay, they've read this much, they came in from this ad. Pass it along to Sales." But when you finally start to look at the data, you realize that content consumption is not indicative, necessarily, of what makes the right customer type for you. Kathleen: Yeah. 100%. They could be job applicants. As you said, they could be students. They could be what I like to call DIYers, you know, people who are gonna consume everything voraciously and then figure out a way to do it by themselves. There's a lot of reasons for that. Sometimes, if you're practicing ABM, they can be from a company that's a good fit, but they might not be the right decision maker, so there's a lot that plays into that. But that's neat. Did you use Woopra itself to figure out- Elle: Yeah. Kathleen: ... behavior that led to conversions? Elle: Yeah. So I was really lucky, coming into Woopra and having all this data already tracked. So I was able to look inside the product and build up these customer journey reports to see the series of steps that people took, and what made them a qualified, or potentially qualified, customer. So we saw that, in our case at least, when they sign up with a product, added a new organization, added a tracking snippet and built their first report, they were 30% more likely to convert into a paying customer. So the question that I had from this was, how can I accelerate this process? How can we combine marketing and sales efforts to help new users navigate through these steps to really see the full value of the product faster? And this is where, essentially what's called PQL, or the Product Qualified Lead model, was born for us. Kathleen: So how does a PQL differ from an MQL? PQL v. MQL Elle: Yeah, so the PQL model essentially flips the traditional lead funnel upside down, starting instead with product adoption. It doesn't ignore content consumption or other tradition MQL metrics but incorporates them into a larger narrative that indicates not only for us prospective interest, but whether or not this user would truly be a good fit for your product. Using that data we were able to test new methods in PQL and learn really what combination of behaviors and company attributes were indicative of a true new potential customer opportunity for our sales team. Kathleen: So you're using that data to bubble up the most important indicators of likelihood for customer conversion. Do you then feed that into a lead scoring model and have some kind of a trigger? Is there a numerical value when that Product Qualified Lead exceeds it that's then passed on to sales? How does that work? Elle: Yeah, so we did a couple of different things. We used a tool called Clearbit, integrated with our own platform for data enrichment. So even people who are potentially anonymous on our site, we could identify what company they were with, the vertical, the company size and pass that information along to our sales team so that when that person identified themselves, we'd have a little bit more information in terms of who they are before we even got to product engagement. But my initial campaign was super simple, and I just wanted to test and see what would work. So we would send an email introducing a feature when we recognized that the user hadn't used it yet during their initial first week in the product. Very simple, right? And the email open rates on that campaign were around 50%- Kathleen: Oh that's really high. Elle: 50% opened. Impressive, right? I was so excited. Of those who opened, 25% of those went on to try that new product feature and after using that feature, we would then use a trigger within our own analytics systems to simply notify our sales team via Slack that we had this new, qualified, engaged user. So for the first campaign that really helped narrow down that pool of leads, but we still found that wasn't enough to truly qualify these 1500 leads. So the next phase of this was combining that with additional data points that were at our disposal. So we knew through Clearbit's company sized vertical, that combining that with companies that scored well on the company set level and product engagement level would be the first to push to our sales team. So combining all of the information we had from Clearbit with "Are they using these product features? How often are they logging into the platform? Have they added their tracking snippet?" and all of these other key indicators was our way of bubbling those up to the sales team and either sending them through Slack, or just creating really nice reports for them in Woopra so they could go in every day and see, "Okay, here are my hot leads for the day." Kathleen: Now, it sounds like you have a combination of behavioral data as well as demographic data, because you've got Clearbit there to enrich. Are you using any of that, particularly on the behavioral side, to do negative lead scoring? Are there certain behaviors that are red flags that you know, this person might meet the threshold but it's a tell that they're not a good fit or is it really mostly just positive? Elle: Yeah, it's a little hard with a company like Woopra, and especially in San Francisco, you could have a 10 person company that's a start up, just got around to finding funding and they are very intuitive with their data. They know they need a full fledged analytic solution. So were we to simply look at something like employee headcount, we would have no idea which customers might actually be a true fit for Enterprise. So we have probably at least 15 to 20 Enterprise accounts where the company sizes are 10 to 20 people. Very small, very agile, but they know that they need access to this level of data. So we do look at that. But again, combining that with what are they doing when they sign up, which different features are they using -- for us there's no really negative lead score, it's just an indicator of whether or not the sales team should be reaching out immediately, or if we should wait longer and hope that this person pre-qualifies themselves through further usage. And in those cases we are still adding those people to different Drift campaigns to help nurture those leads. But it is a great way for our sales team to instantly log in every day and know, "Here are the hot leads I should reach out to immediately." Kathleen: Now, going back to the email that you said, your first pass at trying to use that data to improve conversions, you did that email which has the goal of trying to get the user to adopt a certain product feature that they have not adopted yet. To what do you attribute the really high open rates that you got on that? Elle: I think, you know, consumers today expect a much higher level of personalization and any time you can take something that they've done or who they are and better personalize that campaign, then you're going to hopefully get higher open rates. I think it was relevant for them. It was recent because it was something they'd just signed up for and hadn't quite used yet. So all of those aspects certainly helped. We've tried to do a lot more since then. But for us, I think it's easy, maybe not easy, but, people can find something small like that to test out. A small campaign, just to see if it makes an impact or not and then expand from there. Adopting PQL does not have to be this six to nine month program, it's something that for the most part marketers can start testing today with different elements of product engagement data that they're collecting to see what might stick and what might not. Kathleen: Now, if I understood you correctly, you have this premium model and there's no time limit on how long somebody can use the premium version. There might be a volumetric limit as far as the number of records that it will process. Given that there's no expiration date on premium usage, how far into somebody's adoption of your premium product do you hit them and nurture them? Elle: It really depends on what we're learning about the customer initially. We on Friday released a much more full fledged PQL system. We're calling it a "pressure point." Not only are we collecting the company size, company fit vertical information like that when they sign up, but we have strategically placed pressure points throughout the platform where if people are trying to use a feature that's maybe an advanced analytics feature, it's something that only is included in a paid subscription, that pressure point will pop open it'll say, "Okay it's time to upgrade to use this feature." Now we're counting every time somebody hits those blockers and on what feature they hit those blockers and how often over the course of the week. And what's funny is, like on Friday we launched it, I came in on Monday, I ran a report and I said, "Okay, show me everyone who's hit at least five of those blockers." And I was blown away to see that there were people over the weekend who had hit it at least 200 times. So for me, that's a really great indicator of these are the people who are 100% fit for a paid subscription or more advanced version of our product. And now that we're collecting this level of data, not only can I see what features or integrations they were most interested in to feed that to the sales team so they have a good basis for that conversation, but we can also use that to personalize email campaigns going forward and tell them off the bat, you know, this person was really, really interested, it's time to tackle them and get on it. Kathleen: Have you found that there's a certain degree of frequency with what you can reach out to your users and is there any limit to that? In other words, do you need to limit it to once every three days, once a week? I mean, I imagine you could potentially wind up in a situation of email overload. Elle: Absolutely. We are definitely still learning a lot in terms of how often to email and the types of content we should send. Right now, we're basically sending two emails in the first week, two emails in the second week for nurturing. And then based on that, it'll either be sent to the sales team, or we'll follow back up with something like a product update. For me personally, I would like to see if we can find a way to either expand the number of emails that we're sending or leverage this new pressure point data that we're collecting to better personalize those emails and see what happens, but I would 100% say if you're trying to send at least the marketing emails more than two to three times a week, it could be overwhelming. Kathleen: Yeah, I would agree with that. Now, a key part of this whole process for you is to hand off to sales. Can you talk a little bit about how you're working with your sales team? Not just through that hand off, but are you communicating with them well in advance of that around customer fit and the data you're finding and how that feeds into the leads they're getting. And then what kind of data do you pass them when they eventually do get that lead? I would love to know more about your sales and marketing alignment. Woopra's Sales and Marketing Alignment Model Elle: Yeah, so we're lucky in that we're all kind of leveraging the same platform to learn about our customers, to run reports to ask questions. During the onboarding process, we introduce our sales team to the companies that we believe are a good fit for Woopra based on those that have been with us for years. So they have a good understanding that people who are in SaaS who have these types of integrations will probably fair well in this company size. From there, we do build a certain number of reports for them. So things like if they're running in a different territory, here are the top leads in your territory, here are some other important engagement metrics that we're gonna incorporate from you. There's definitely a lot more that we could do, but we do have weekly alignment calls where we're walking through those reports with them and making sure that the data that we're sending them is actually driving results, that they're not drowning in unqualified leads, that we didn't get something wrong. It's all iterative and testing like, yes we might think that somebody installing an integration makes her more likely to convert him and maybe the numbers show that today, but that doesn't mean that's going to continue in the long run. So it is a constant iteration and evolution and the way that we work with this team to ensure that everything that we're doing in the marketing side is bringing, not the right opportunities today, but the right customer types in the long term. Because it doesn't do anybody any good if they're signing on people today who are going to churn three months later. Kathleen: Now who's on that weekly call? Is that the whole sales team and you? Or do you have additional people from your team? Elle: Yeah, so we bring myself, the sales team, we have a sales engineer there, and that's the gist of it. We pass along some of the information to our CFO, we're a smaller team, a little more agile. It is nice to have both the marketer's perspective in those meetings to help drive the content for them and help them look at the data, but also having that engineer in the room. So if perhaps they're doing a demo and something's not quite right, or they're customer requests that come up during the POC phase that aren't quite in the product, we can help to build those into the product roadmap and keep everyone aligned. Woopra's Marketing Results Kathleen: That's great. So tell me a little bit about the results you've seen from implementing this product qualified nurturing. Elle: Yeah, so through the initial email phase and then a couple other PQL campaigns that we ran, one of which was using Drift's live chat feature to incorporate the data that we had about that customer and then automatically, for example, pop up when a Drift live chat message when somebody had installed integration and we wanted to share a piece of content. So we've tried a bunch of different things over really the last year. Since then we have found that 50% of the leads now coming through the PQL model -- so these high qualified ones that the sales team are reaching out to -- make it through the demo request phase. And of those, 25% of them are at least getting to the POC phase before closing. So that in and of itself, for me, out of the 1500 leads, being able to allow them to hone in and really target on those that are going to make it through that phase has been great. Kathleen: That's especially important if you have a smaller team, just not to be wasting the limited bandwidth you have on poorly qualified leads. Elle: Exactly. And you would think it would matter even more for a bigger team, but I think it's probably harder to test and integrate different types of campaigns like this when you do have hundreds of sales team members to work with. Kathleen: Yeah. Now has your freemium to paid customer conversion rate increased? Elle: It has. So it was 30% before, freemium to paid, and we're at 35% now. My hope is that with this pressure point system we'll be able to jump that up to closer to 40% or 45%. But even then, I'm pretty happy with the results overall. Kathleen: Yeah, that's great. And do you find that the length of time from adoption of the freemium product to conversion and to a paid customer is speeding up? Elle: It definitely is because our sales team isn't wading through as much in the pool to identify who to reach out to. I think when I first started at the company it was about six months to close from a new sign up, from the day they signed up to an actual enterprise deal. As of our last all-hands, we're down to three or four months now. Kathleen: Wow, that's great. Elle: So it's ... yeah, doesn't seem huge, but it really is a significant difference when you're a startup, in the long run to be able to accelerate that process. And for me, that was kind of "proof is in the pudding." It showed me that something that we're doing is working and that we just need to, maybe not double down on it, but try to find better ways to improve this existing model that we have. Kathleen: Absolutely. Those are impressive numbers. Well all good stuff. Kathleen's Two Questions Kathleen: As somebody who's been in marketing for a while and who's, especially in the startup world, which I think is so interesting and so fast moving, I'm curious to get your answers to the two questions I love to ask all of my guests. The first is, company or individual, who do you think is doing inbound marketing really well right now? Elle: Let's see. HubSpot obviously leads the forefront in terms of really setting the stage for what inbound can be and validating it's use through numerous different types of campaigns from different free offering features as teasers to powerful content to having a very lightweight version of their product that different teams can use for free. But I was researching this question and going through my emails and every time I get a really good email or a very personalized campaign when I sign up for a new product, I add them to a folder called "Good Emails." And one that stuck out in my mind was Appcues. I'm not sure if you're familiar with that company. Kathleen: Yep. Elle: Yeah, they do in-app onboarding software and I guess you would expect them to be really good at this since they're collecting all of that PQL data that we just talked about. But what I noticed was that all of the campaigns that I received from them for my first two weeks of signing up were based on both the company that I was in, so my vertical, they targeted SaaS. They knew the integration tools that I was using that integrated with theirs and they spoke to those. And they also based those emails around the different features that I was using. So it felt personal, it was relevant, and the emails were light enough that they caught my attention without drowning me in sales calls or overwhelming me with too much content. Kathleen: Well, that tracks very well with what you said earlier about recency, relevance, and personalization. Elle: Yeah. Kathleen: That makes a lot of sense. The other question is, digital marketing changes so quickly and I'm always fascinated to learn how people like yourself stay up to date and educate themselves on all of those changes that are happening. Elle: Isn't your podcast the only thing people listen to? Kathleen: Well I know we're definitely one of them, that's for sure. Elle: I like Neil Patel's blog. I'm not sure if you've read "Open View Partners." They have a blog called "Open View Labs," which is really great content for all things startups from fundraising to success stories. There's also a venture capitalist from Red Point called Tomasz Tunguz, who writes content from product strategy to compensation to startup culture development. But yeah, I've noticed that personally I prefer bloggers who provide insight into every aspect of running a startup because I've found the more that you can learn about how different departments run and should collaborate and integrate with one another, the more you're able to design marketing campaigns that are really harnessing that collective knowledge of the organization to deliver more personalized or long-term results. Kathleen: Yeah, those are great suggestions. One of the reasons I like to ask this question is I feel, myself, that there is this bubble that we as marketers live in and it's very easy to stay within the bubble and we're all listening to the same podcasts and reading the same blogs and it's dangerous because if you do what everyone else is doing, you're going to get results that everyone else is going to get. And really what you want are better results than everybody else. So I like those suggestions a lot and I'm going to have to check those out. So if people have questions about product qualified lead nurturing or about Woopra or anything else that we've talked about today, what is the best way for them to find you online? How to Contact Elle Elle: Yeah, so you can either find me on LinkedIn at Elle Morgan, that's E-L-L-E. Free feel to email me at Elle@woopra.com or hop over to Woopra.com, it's W-O-O-P-R-A.com. Kathleen: Fantastic. I will put all of those links in the show notes. Before we close out for today, I have an ask for anybody who's listening. If you listened to today's episode and you like what you heard or you've been listening and you've gotten value out of the podcast, it would mean a lot to me if you would just take a few minutes and go into iTunes or Stitcher or whatever platform you listen on and leave a review. It makes a huge difference in terms of getting us in terms of more interested marketers. So take five minutes out of your week and leave a review for the Inbound Success podcast. And if you know somebody else doing kick-ass inbound marketing work, tweet me @workmommywork because I would love to interview them. That's it for this week, thank you so much, Elle, it was a lot of fun. Elle: Thank you, Kathleen. Have a good one. Kathleen: You too.
Avec Notre invité le Pasteur Steve Rasier, on parle de dépression et de suicide dans la communauté, on fait un retour sur le "scandal" PQL et l'homosexualité dans l'église. Host; Renzel Dashington --- Send in a voice message: https://anchor.fm/cerealekillers/message
Cette semaine on retrouve dans "l'émission insolite by Pql" Camille Casanova, Gaspard et Kévin au côté de Stéphane Antoine. Parce qu'on fait tous des conneries et nous c'est de ça qu'on rit!
Suite à l'enquête du journal de Montréal sur l'Église PQL et ses pratiques abusives, nous avons décidé d'enregistrer une émission sur ce sujet. Pour en savoir plus sur l'apôtre Isaac et l'évangile de prospérité qu'il prêche, veuillez consulter ce dossier journalistique: http://www.journaldemontreal.com/2018/01/29/ruines-par-un-gourou-qui-promet-des-miracles-1. Guillaume et Pascal, quant à eux, expliquent pourquoi ces faux-prophètes réussissent encore à tromper des gens et vous rappellent les mises en garde biblique et les moyens de déjouer ces faux-enseignants de la Parole de Dieu. Musique (deux choix s'imposaient): -Shai Linne, Fal$e Teacher$ -Luc De Larochellière, Sauvez mon âme Cliquez sur ce lien pour voir tous les titres de Coram Deo: http://prechelaparole.sermon.net/pdf/20954925
If you liked this episode, we bet that you’ll love our blog content. blog.drift.com/#subscribe Subscribe to never miss a post & join the 20,000+ other pros committed to getting better every day. --- Mark Roberge came by Drift earlier in the month to hang out and have lunch with our team — so we figured we would record it and turn it into a conversation for Seeking Wisdom. Mark helped HubSpot grow basically from day one (as SVP of Worldwide Sales and Services and then Chief Revenue Officer) and now spends his time as a Senior Lecturer in the Entrepreneurial Management Unit at the Harvard Business School. Two quick highlights you don't want to miss: - 22:00 Mark’s thoughts on the shift from the PQL model to the MQL model and how it’s changed sales - 39:00 Mark walks through his interview technique Follow Mark on Twitter twitter.com/markroberge Connect With Us: Follow David (twitter.com/dcancel) and Dave (twitter.com/davegerhardt) on Twitter. Come hang out with us at seekingwisdom.io and on Twitter @seekingwisdomio. Learn more about Drift at Drift.com.
Elke and Tim Inkster have made an important and enduring contribution to Canadian literature. In 1974 they founded The Porcupine's Quill (PQL), a publishing house based in Erin, Ontario. Renowned for excellence in design and production, and for taking risks with new, unpublished authors, the firm has helped kick-start the careers of many of Canada's best known writers . PQL publications have won numerous awards and serve as an example to the world of Canadian publishing excellence. Its first title came off the press in 1975: Brian Johnson's only book of poems, Marzipan Lies. Brian Johnson is currently the film critic for Maclean's and "claims to have met Mick Jagger of the Rolling Stones, twice!" (almost certainly an in-joke here that I'm not privy to). Many of the early titles were slim volumes written by poets Tim Inkster had met as a student at the University of Toronto — amongst them Ed Carson who until recently was President of Penguin Canada, and Brian Henderson who is currently the publisher at Wilfred Laurier University Press. I met the Inksters recently in the garden behind their Press House. It butts up against the West Credit River, where this little critter spent most of the morning chopping and hauling lumber from one bank to the other. While he was doing this Tim and I made our way back into the press room to talk about the history of The Porcupine's Quill and how to go about collecting its books. During this discussion we hit on how market forces often influence appearance: namely glossy versus matte finished covers. It was here that Tim got into describing the difficulties he's encountered dealing with Chapters, Canada's one and only big box bookstore.
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