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2 episodes with analytics insights
2 episodes with analytics insights
2 episodes with analytics insights
How does SharkNinja use data to fuel its rapid growth and product innovation? Join Cindi Howson and Elpida Ormanidou, VP of Analytics and Insights at SharkNinja, as they dissect SharkNinja's data-driven culture, Elpida's journey in the data space across CPG and retail, and her insights on AI in the workplace. Key Moments: Data-Driven Culture (03:36): SharkNinja strongly emphasizes data in its culture, utilizing it to inform decision-making processes. The company is committed to using customer feedback gathered through data to drive the development and refinement of its product offerings. CEO's Data Focus for Customer-centric Innovation (05:43): SharkNinja's CEO demonstrates a notable dedication to data by actively engaging with it. This involvement includes closely reviewing customer feedback and using data insights to guide product discussions and challenge teams to improve. Data Ethics and Privacy (09:17): SharkNinja places a high priority on data ethics and privacy, emphasizing the importance of earning customer trust. Elpida shares how the company is committed to using customer data responsibly and has implemented strong controls to protect privacy. AI and the Future of Work (20:31): Elpida discusses the transformative impact of AI on the future of work, characterizing it as a revolution. She emphasizes the importance of proactively addressing the changes by reskilling and upskilling the workforce to adapt to new roles and technologies. Key Quotes:"Value gets created at the time of consumption. We create value for the business when data gets consumed, not when it gets connected, not when it gets processed, not when it gets synthesized, only when it's being used to drive decisions that create value for the company." - Elpida Ormanidou"Think of a company as a chain, where everything is interlinked to level up. Today's struggle is that while we have good AI applications, it's an art to connect them to create the next level of experience, particularly for customers. What works in a lab doesn't work the same in real life; there are so many different factors.” -Elpida Ormanidou"Where others have fear, I have hope and optimism that the more we automate and we remove mundane tasks from our day-to-day life or even our work life, the more we would be able to use our beautiful brains to reimagine and create new things that as a race will drive us forward for another 3,000 years." -Elpida OrmanidouMentions:SharkNinja Coolar: FrostVault TechnologySharkNinja HydrovacSurat: 100 Resilient Cities of the WorldMadam Curie: A Biography, By Eve CurieGuest Bio Elpida Ormanidou Elpida Ormanidou is the Vice President of Analytics & Insights at SharkNinja. She has extensive experience in data and analytics, having worked at companies like Walmart and Starbucks. At SharkNinja, she leads the data strategy and is passionate about fostering a data-driven culture. Elpida is a strong advocate for ethical data practices and responsible AI implementation. She is a recognized voice in the data and analytics community, frequently speaking at industry events and mentoring young professionals. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
A discussion with Steve Butler, North America Leader - Analytics, Insights, and Automation at global healthcare technology company Philips. Steve has had a balance of consulting and client roles over his career, which enables him to provide unique insights into the field of data science and analytics. Steve talks about the pros and cons of client versus consulting roles. He also discusses why he believes the challenge in the data science space isn't finding a job (there are plenty) but staying relevant and not becoming an antique in today's fast-changing market. He takes a dive into how working for a company focused on healthcare is different from his past employers. Then, he finishes with a discussion of the importance of being in person in an office to facilitate collaboration and his views on the future of customized large language models. #analytics #datascience #ai #artificialintelligence #generativeAI #philips #consulting #healthcare #technology
In this episode, we delve into the world of data science and analytics, uncovering key insights and trends. We also discuss the role of tech evangelism in shaping the future of technology and innovation. Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community: https://www.facebook.com/groups/739308654562189 Podcast Studio AZ: https://podcaststudio.com/az Podcast Studio Network: https://podcaststudio.com/network/
In this episode, we delve into the world of data science and analytics, uncovering key insights and trends. We also discuss the role of tech evangelism in shaping the future of technology and innovation. Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community: https://www.facebook.com/groups/739308654562189 Podcast Studio AZ: https://podcaststudio.com/az Podcast Studio Network: https://podcaststudio.com/network/
In this episode, we delve into the world of data science and analytics, uncovering key insights and trends. We also discuss the role of tech evangelism in shaping the future of technology and innovation. Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community: https://www.facebook.com/groups/739308654562189 Podcast Studio AZ: https://podcaststudio.com/az Podcast Studio Network: https://podcaststudio.com/network/
In this episode of ClickFunnels Radio, hosts Ben and Chris welcome Grant Cooper, co-founder of Cometly, to discuss his journey from selling products as an affiliate to becoming a data and analytics expert. Grant shares insights on his experience running ads on platforms like Facebook and Google, emphasizing the importance of tracking and analytics in driving results. Grant's expertise provides valuable lessons for listeners interested in leveraging data to enhance their marketing strategies. Tune in for an informative interview filled with actionable knowledge! Don't forget to check out https://cometly.com
Welcome to another exciting episode of Podcasting Smarter! In this podcast analytics event replay episode, you'll discover the surprising insights into maximizing your podcast's performance with this in-depth episode. Uncover the unexpected strategies and tools that are revolutionizing podcast analytics and audience engagement. Join Norma Jean Belenky, Podbean's Head of Events, on mastering podcast analytics where we'll dive deep into the metrics that matter with demos and expert insights from John Kiernan, Podbean's Director of Content, and Roni Gosch, Podbean's Podcast Specialist. Discover how to leverage key analytics tools, including audience geography, download sources, and listener engagement, to improve your podcast's performance. Learn how to use data to enhance listener experience, boost engagement, and grow your audience. "The more attention you get within the first 24 hours that it's live, that's when it boosts in the algorithm, and then that's when the discoverability happens". - Roni Gosch Are you ready to take your podcast to the next level? Don't miss out on these game-changing podcasting insights. Here are the main takeaways of today's episode: Master the art of decoding podcast analytics to unlock your show's full potential. Retain and captivate your podcast audience with proven engagement strategies. Uncover the hidden power of understanding download sources for your podcast's growth. Dive deep into analyzing your podcast episodes to elevate your content game. Harness the impact of releasing your podcast episodes at the optimal time of day. The key moments in this episode are: 00:03:07 - Key Podcast Analytics 00:07:06 - Utilizing Analytics for Growth 00:11:06 - Overview of Statistics Dashboard 00:12:47 - Understanding Graph Metrics 00:13:58 - Importance of Episode Breakdown 00:15:18 - User Interaction Metrics 00:16:12 - Understanding Audience Demographics 00:19:14 - Optimizing Release Schedule 00:25:00 - Meeting Your Audience's Needs 00:26:04 - Engagement and Algorithm Boost 00:28:37 - Understanding Download Sources 00:34:04 - User Retention and Daily Listeners 00:37:05 - Understanding Listener Metrics 00:37:47 - Episode Breakdown and Comparison 00:39:27 - Episode Download Comparison Chart 00:40:39 - Importance of Evergreen Content 00:41:47 - Wrapping Up and Resources Resources: Subscribe to our email newsletter to get industry updates: https://www.podbean.com/email-subscribe Watch the video of this event on YouTube: https://www.youtube.com/watch?v=IOuAzgy24c4 Sign up for all of Podbean's Free Live Events here: https://www.eventbrite.com/o/podbeancom-31329492977 Other episodes you'll enjoy: Podbean's Apple Subscription Integration Podbean's Descript Editing Integration About us: Podcast Smarter is the official in-house podcast by Podbean. Podbean is a podcast publishing and monetization service, hosting almost 640,000 podcasts. If you're looking to start your own podcast, monetize your podcast and livestream directly to your listeners, you can set up an account at podbean.com Connect with us: Subscribe to our email newsletter to get updates from the team head over to: https://www.podbean.com/email-subscribe Find us on socials: Instagram: https://www.instagram.com/podbean Facebook: https://www.facebook.com/podbeancom YouTube: https://www.youtube.com/channel/UC0H3hvTa_1_ZwFg6RjGNXGw/ Twitter: https://www.twitter.com/podbeancom LinkedIn: https://www.linkedin.com/company/podbean Website: https://podcast.podbean.com/ Email us: To contact Podcasting Smarter with questions get in contact at podcastingsmarter@podbean.com
Text us your thoughts on the episode or the show!Unlock the secrets to mastering marketing analytics with insights from Shivani Bhatt! As the Director of MarTech Solutions and Analytics at CloudFlare, Shivani shares her incredible journey and the pivotal moments that shaped her career. From building marketing ops teams at S&P and Adobe to addressing the critical gaps in understanding analytics in marketing and sales, this episode promises to elevate your data analysis skills.We tackle the misconceptions about dashboards and explore the often-overlooked value of well-prepared data sets. Through Shivani's experiences, we discuss the importance of creativity and clear communication in data analysis. Hear how she navigates the complexities of managing vast data volumes and why refining reports is essential to keep stakeholders confident in data quality. This conversation is packed with practical strategies to go beyond traditional dashboards.Finally, we delve into foundational steps in marketing analytics and essential learning strategies. Discover how mastering basic Excel functionalities and leveraging online resources can incrementally build your knowledge. We also explore the emerging role of AI in marketing and its collaborative potential. This episode is a treasure trove of real-world advice, equipping you to bridge the gap in marketing analytics and enhance your data analysis prowess.Episode Brought to You By MO Pros The #1 Community for Marketing Operations Professionals We've been HACKED! (just kidding)If you love our show, you gotta be sure to tune into Justin Norris' show: RevOps FMSupport the Show.
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In this episode, we delve into the world of data science and analytics, uncovering key insights and trends. We also discuss the role of tech evangelism in shaping the future of technology and innovation. Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community: https://www.facebook.com/groups/739308654562189 Podcast Studio AZ: https://podcaststudio.com/az Podcast Studio Network: https://podcaststudio.com/network/
Tina LowryHealthcare Data Analytics Expert and Educatorhttps://tinalowry.comTina Lowry is a seasoned healthcare IT professional with 20 plus years of experience and extensive expertise in data analytics. After experiencing a life-changing event, Tina decided to lean into her passions and transitioned out of the corporate world. Today she teaches data analytics and guides others through the field while exploring innovative data visualization techniques. Tina's career is marked by a dedication to making complex data accessible and understandable. During this talk, she offers invaluable advice for individuals seeking to pursue and/or further develop a career in healthcare data analytics. She continues to serve as a key mentor for many. Always seek opportunities to grow and adapt, and don't be afraid to step out of your comfort zone to explore new areas of interest.ResourcesCode AcademyFlipboardCourseraUnity LearnSubscribe to our podcast, and leave a reviewConnect with us on Instagram, FaceBook, Twitter , and LinkedInhttps://eima-inc.com/lets-talk-small-data@letstalksmalldatapodMusic credit: Yung Kartz
Kantar BrandZ Insights from Brand Builders 2024: A conversation with Kirti Singh, Chief Analytics, Insights and Media Officer, P&G. As part of the 2024 launch of the worlds most valuable brands, Martin Guerrieria, Head of BrandZ, talks with Kirti Singh, Chief Analytics, Insights and Media Officer, P&G about the increasing importance of brand in driving growth.In 2024, P&G brands Pampers, Gillette, Pantene Pro-V, and Olay once again feature in the Kantar BrandZ Most Valuable Global Personal Care Brands ranking.Listen in to hear Kirti talk about the role of marketing and brand-building today, and how P&G is strengthening its brands both globally and locally.Find out more: www.kantar.com/brandz Hosted on Acast. See acast.com/privacy for more information.
Highlights from this week's conversation include:David's Background and Journey in Data (0:30)Transition to Time Series Forecasting (2:03)Working on Time Series Forecasting at Amazon (2:55)Challenges and Experience in Time Series Forecasting (4:32)Transitioning to a New Role at Amazon (5:52)Tools and Methods for Time Series Forecasting (8:17)Forecasting Impact and Accuracy (15:30)Explaining Variance and Lessons Learned (18:58)Understanding Downstream Consumers and Empathy for Business Leaders (20:36)Amazon's Culture and Decision-Making Process (24:27)Assimilating into Amazon's Culture (26:04)Interpreting Data for Business Stakeholders (28:34)Consulting for Small Businesses (30:28)Challenges in Automation and Maintenance (32:18)Analyzing Financial Metrics for Small Businesses (34:51)Tooling and Data Solutions for Small Businesses (39:52)Empowering Small Businesses with Data (46:02)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.
Welcome to episode 72 of our Highway to Sell podcast! This week join our hosts Tom and Tara as they dive deep into the complexities of Amazon Advertising Analytics. Through expert discussion, they unravel the significance of analytics in crafting successful advertising campaigns, the intricacies of attribution modeling, and how brands can extract actionable insights to stay ahead in the competitive marketplace. If you enjoyed this deep dive into Amazon DSP, make sure to subscribe to Highway to Sell. We release a new episode every 2 weeks, each featuring a different guest expert who brings fresh perspectives. For more insights or to request an audit free of charge, visit us at clearadsagency.com. Join us on this journey and take the first step towards unlocking the full potential of your advertising efforts with Amazon DSP.
Unlock the transformative power of data as we embark on an exploration with Steve Clegg of Zintoro, where the convergence of business analytics and artificial intelligence takes center stage. Steve's remarkable journey from WR Grace to Avis, and eventually to EIA Investors, serves as our roadmap through the intricacies of predictive models, algorithms, and the potent net promoter score in customer retention strategies. This episode is a treasure trove of insights for those eager to understand the profound shift from raw data to actionable insights, and how they've reshaped business strategies from the 1970s to the digital age.Join us as we unravel the threads connecting customer service to business triumph. Hear firsthand how the art of conversation and the choice of words can dramatically influence sales, and discover the benefits of proactive customer engagement over the industry's traditional reactive stance. Steve's experiences illuminate the path to enhanced customer satisfaction and market penetration, offering valuable lessons on benchmark surveys and the art of aligning service delivery with market expectations. This chapter is essential listening for anyone committed to elevating their company's growth and customer experience.Venture with us into the future, where the narrative shifts to the ever-changing automotive dealership landscape and the revolution in workforce dynamics. From the potential impacts of Ford's electric vehicle contract to the rise of remote work, learn how analytics is reshaping the performance and operational skills within dealerships. This enlightening conversation with Steve doesn't just forecast the horizon of business practices and education—it equips you with the adaptability and knowledge to navigate and thrive in the dynamic marketplace of today and tomorrow. Visit us at LearningWithoutScars.org for more training solutions for Equipment Dealerships - Construction, Mining, Agriculture, Cranes, Trucks and Trailers.We provide comprehensive online learning programs for employees starting with an individualized skills assessment to a personalized employee development program designed for their skill level.
We are joined by Kunaal Goel, Vice President Analytics & Insights at Sentrics, as we explore the transformative power of data in senior living. Together, we unravel the intricacies of leveraging data to not only improve resident well-being but also streamline operational efficiencies within senior living communities. By the conclusion of our conversation, listeners will gain a comprehensive understanding of how data can be a catalyst for positive change in senior living, empowering them with the knowledge to foster better outcomes for residents and elevate operational excellence.
Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! Join us for part two of our conversation with Joe Reis, host of 'Monday Morning Data Engineering' and co-author of 'Data Engineering Fundamentals'. In this episode, we continue our exploration of the evolution of data engineering and the shift towards real-time analytics. We discuss the fine line between streaming and real-time processing, the transition from ETL to data engineering, and the significance of immediate data processing in user interactions.
On this episode of the Futurum Tech Webcast – Interview Series, host Daniel Newman welcomes Alteryx CIO Trevor Schulze for a conversation on the evolving landscape of IT decision-making, the importance of data analytics in modern IT strategy, and how IT leaders can “future-proof” their strategies by leveraging them. Their discussion covers: The role of AI in the shifting role of the CIO and what IT leaders should consider around AI when making technology decisions The importance of data analytics in shaping the decisions made by IT leaders and contributing to overall business success Some top predictions for the future of IT decision-making, especially concerning the increasing emphasis on predictive analytics, the integration of AI, and the democratization of data Insights on how IT leaders can “future-proof” their strategies by leveraging data analytics and what specific approaches can enhance these strategies for success
A recent Google bulletin sparked conversation within the Healthcare industry around data confidentiality. Matt Crowley joins the podcast to discuss that news from Google and review potential options for Healthcare providers to ensure all data within Google Analytics follows laws, rules and regulations. Looking for more tips or how we can help your analytics strategy? Contact us at info@morevisibility.com.
Guest: Kyle Smith, Washington State Head Coach Washington State head coach Kyle Smith joins the Basketball Podcast to share Nerdball analytics insights.In his first three seasons at Washington State, Smith has rebuilt the Cougars putting himself in rarified air at WSU as he has finished his first three seasons at .500 or better joining Tony Bennett as the only Cougars head coach to post .500 or better records in their first three seasons after taking over a sub-.500 club. In addition, he has posted back-to-back winning seasons, the first time in over a decade at WSU.Smith began his coaching career as an assistant at his alma mater Hamilton College. He then moved to Division I as an assistant at Air Force, Saint Mary's, and San Diego. He got his first head coaching job at Columbia University in 2010, where he led the Lions to three postseason appearances and a 101-83 record in six seasons.In 2016, Smith was hired as the head coach at the University of San Francisco, where he continued his success with three consecutive 20-win seasons and two postseason berths. He also implemented his signature "Nerdball" system, which uses analytics and data to evaluate players and opponents.Breakdown1:00 - Washington State Rebuild5:00 - Nerdball System9:00 - Data and Analytics14:00 - Removing Biases20:00 - Mindset Training23:00 - Improvement24:00 - 50 Categories28:00 - Idea of Turning Threes29:54 - 30:56 - Hoopsalytics Ad 334:00 - Watching the Process37:00 - Isolation Efficiency39:00 - Data46:00 - Shooting Ball Handling51:00 - Offensive Boards56:00 - NBA59:00 - Adopting the System1:01:00 - ConclusionKyle Smith's Bio:Bio: https://en.wikipedia.org/wiki/Kyle_Smith_(basketball)Twitter: https://twitter.com/Coach_kylesmithBasketball ImmersionWebsite: http://basketballimmersion.com/Twitter: https://twitter.com/bballimmersion?lang=enYouTube: https://www.youtube.com/user/basketballimmersionFacebook: https://facebook.com/basketballimmersionImmersion Videos:Check out all our all-access practice and specialty clinics: https://www.immersionvideos.com
Join AWS Startups' Eric Zimmerman and Apixio CEO, Sachin Patel, as they explore the transformative role of AI in healthcare analytics on the AWS Health Innovation Podcast. Sachin shares how Apixio is making patient care more personalized and cost-effective with actionable data.
In this episode, join the Analytics Insights and Reporting (AIR) team as they discuss their participation in the recent Gartner Conference. Gartner is a consulting organization that conducts research on technology and then shares the results through both private consulting and executive-level programs and conferences. The AIR team will share their insights and takeaways from the three-day event and review how IQVIA can leverage key themes to better enable data and analytics within healthcare life science organizations.Featured Speakers:John F. Jackowski III, MBA LSSGB, Product Owner, Analytics, Insights, and Reporting, IQVIAMarlin Strand, Principal, Enterprise Business Intelligence, Analytics, Insights, and Reporting, IQVIAAndy Barnes, Principal and Commercial Analytics Consultant, Analytics, Insights, and Reporting, IQVIA
Zara Zamani is the Chief Solutions Officer (CSO) of the leading Nordic blockchain company, ChromaWay, and Co-Founder of Neoki Metaverse. She has multiple years of experience in designing blockchain platforms in tourism, healthcare, logistics, foodchain, energy, gaming, DeFi, DAOs, and now metaverse and fashion industries. She has also been involved highly in the development of innovative investment models in the crypto space. She was featured as one of the 21 women in the blockchain you should know in 2021 by Fintech Review and the 10 most influential women in technology in 2021 by Analytics Insights. as well as 10 most inspiring women leaders in 2022 by SuccessPitcher. She has both practical and theoretical knowledge as she is also a Ph.D. researcher and lecturer in blockchain adoption at the university of Halmstad, Sweden. You can get in touch with her here: https://twitter.com/zilikezara
This week, Realcomm Live welcomes Arjun Kaicker, Co-Head, Analytics + Insights with Zaha Hadid Architects, one of the most respected and admired architectural design firms in the world. Kaicker discusses what these new workplaces might look like and the need to design buildings to be self-evolving and futureproof, with technology access that is seamless and frictionless.
Lisa is one of the pioneers of multi-platform measurement (with a patent to boot) and has built a reputation as a true industry collaborator. In this episode she share insights about all things multi-platform and takes us on a journey exploring her career across CBS, ABC and now NBC.
John King, Senior Manager, Analytics & Insights, Private Brands at Walmart reveals how the largest US retailer makes use of a “firehose” of data. This amounts (according to some estimates) to 2.5 petabytes of unstructured data from 1 million customers every hour. Walmart's FP&A unit ensures this potentially limitless information provide instant business decisions for the US retail giant. First John explains his path to being an FP&A leader, starting as a Geographic Information Systems (GIS analyst) at Tradewind Energy in Kansas. Here his love and appreciation for technology - which plays a central role at Walmart- was born. John's passion for technology saw him thrive at his roles at two of Walmart's FP&A divisions: Realty Market Strategy, and now Private Brands. Private brands - a good that is manufactured for and sold by Walmart competing with brand-name products is a huge source of data and revenue. In fact,18 of Walmart's private brands do more than $1 billion in sales and its largest name, Great Value does more than $27 billion a year globally. In this interview John speaks about the intersection of FP&A and business-decisions at Walmart and his career. His passion for technology and the tech stack used at Walmart The core metrics which Walmart judges FP&A on and what other retailers can learn How to get insights for the business “today or tomorrow” through dimensional modeling How to keep focus on the most important data in the face of potentially limitless consumer data As both a Python and Excel expert, whether FP&A leaders need to know Python, Excel or both? The importance of taking logic and analysis to the data - rather than the other way around The most important advice for anyone starting in FP&A to succeed Follow John on LinkedIn Follow Paul Barnhurst on LinkedIn Follow Datarails on LinkedIn FP&A Today is brought to you by Datarails. Datarails is the financial planning and analysis platform that automates data consolidation, reporting and planning, while enabling finance teams to continue using their own Excel spreadsheets and financial models. With Datarails you get improved data integrity and visibility helping your relationships with your internal business partners and external stakeholders; real-time latest version of all your company's data in one place, with version control, audit trail and records, ensuring errors and multiple versions are avoided; the ability to let your data tell your story through proprietary, built-in visualization of critical KPIs in real-down; and drill-downs to answer questions on underlying data on the spot. Get in touch at www.datarails.com Follow DataRails on LinkedIn to find out about upcoming episodes and the latest FP&A news Read the Full Program Transcript Watch the Full Episode on YouTube To suggest a great guest for the show, or if you would like to be the FP&A Leader being interviewed contact jonathan.m@datarails.com
Trent Anderson is Head of Rev Ops at Podchaser. Podchaser is the world's most comprehensive podcast database — collecting, enriching, and distributing podcast insights to power discovery for listeners, podcasters, and brands. Join the Facebook Group (B2B SaaS Cold Outreach Mastery): https://morgandwilliams.com/fbgroup --- Send in a voice message: https://anchor.fm/morgan-williams0/message
No matter what it's about, if you are serious about building something, like building a house / car / space ship or your mobile app product, you need a plan or what we call in IT - a stack. I'm sure you see where I'm going with it, I have Andy Carvell on the episode and I've just mentioned a stack… Right - on this episode we're to talk about The Mobile Growth Stack to be precise, 2022 edition. I bet for many of you this situation may look familiar - either you are at the beginning of your app project or its well underway, you wonder what are my ALL options to build and launch a successful app business? As it happens quite often with many concepts or ideas that go viral, the Mobile Growth Stack was conceived by Andy out of necessity when he needed to put together all his ideas for an app project, working at SoundCloud. Now, to be clear, do not think of this stack as your ultimate To Do list. It service a different function of being a depiction, if you will, of the entire landscape of possible activities you may be involved with, working on your app project, including such stages as app user acquisition & retention, app engagement, monetization, tech solutions to use and more. Today's Topics Include: Andy's background - from a software engineer at Nokia, Marketing Lead at SoundCloud to Partner & Co-Founder at Phiture - Berlin-based 100+ strong mobile growth consultancy Mobile Growth Stack: definition and its genesis The Acquisition section of the stack The Engagement & Retention section of the stack The Monetization section of the stack The Tech section of the stack The Strategic Management section of the stack The Analytics & Insights section of the stack The Management Insights section of the stack Android or iOS? Actually both :-), to be up-to-date with what's happening on both platforms What was Andy's first mobile phone? Nokia 7110 What features would Andy miss most leaving his smartphone at home? Google Maps :-) What's missing from mobile app technology? Even better Night Mode on an iPhone. Links and Resources: Andy Carvell LinkedIn profile Mobile Growth Stack 2022 Phiture website. Quotes from Andy Carvell: "Than I ended up joining SoundCloud, who were a growing platform at that point, I think I joined at 29, I was employee number 89, I think. That's one of the major misconception, actually, about the Mobile Growth Stack. There is lots of stuff in it, if you've seen it you can see at mobilegrowhstack.com, it's a quite a busy page, there is a lot of information on there. And I think one of the things that people often think is that they have to do all of these things in order to succeed and it's absolutely not the case. I really do believe that app analytics and insights should be the foundation on which you're building your whole growth efforts on.” Follow the Business Of Apps podcast Linkedin | Twitter | Facebook | YouTube
More and more companies are turning to podcasts as a way to reach their target audience. And it's not hard to see why: podcasts are an incredibly effective marketing tactic that can help you build relationships with your customers, generate leads, and grow your business. But that's not all. in fact, there are many more benefits to starting a podcast for your business. To discuss all the benefits of podcasts and why your business needs one, I invited Trent Anderson on the show. Trent is the Head of RevOps at Podchaser and in this episode of the Funky Marketing Show, we talk about everything you need to know about podcasts and why they're such a valuable marketing tool. If you're thinking about starting a podcast or are looking for ways to improve your existing show, this is the episode for you. Tune in now to learn more! Overview of what we went through: 0:00 - Intro 2:15 - Creating relationships through podcast 5:00 - Ideal tactic to get into conversations with ideal customers 7:59 - Why most podcasts fail 9:18 - Written content should support your podcast initiative 13:17 - Extracting the strategic narrative from companies that don't know what are they doing 17:10 - Books to get inspiration from 18:10 - Using podcasts as a research tool 24:01 - Be passionate about your content, people will trust you more 26:41 - Real conversations are happening in the audio format 29:00 - Podcasts are the best way to learn about specific subjects 31:32 - Using the podcast as a way to get into new markets 33:31 - Always try to get in the conversation with the right people 34:39 - There are no excuses for not starting a podcast 36:53 - Trent shares a real-life example of how he used research interviews and podcast appearances to get into new markets 41:47 - What do we need to measure when it comes to the podcast? 45:11 - Getting the feedback, topic ideas, and improving the quality of content 46:42 - Podcast advertising attribution 50:19 - Take the topic, digest it and see if it works for you 53:11 - Where you can find more about Trent 53:58 - Outro Find more about Trent: https://www.linkedin.com/in/growthtrent/ https://twitter.com/trentanders0n https://www.podchaser.com/ Enjoy! -------------------------------------------------------------------------------------- Funky Marketing Show is a podcast in which we're talking with entrepreneurs, marketers, advertisers, designers, artists, and all those people that are doing an amazing job for amazing people. Listen on: Anchor: https://anchor.fm/funky-marketing Spotify: https://open.spotify.com/show/136A3zxZ5JYCukvphVP56M Apple: https://podcasts.apple.com/us/podcast/funky-marketing-show/id1501543408?uo=4 Our website: https://www.funkymarketing.net/ Need help? Go to https://www.funkymarketing.net/contact-us/ and schedule a call with us! We offer a free 30-minute consultation! Let's talk and see how we can make your business GROW! #b2bmarketing #podcasttips #podcasting #demandgeneration #funkymarketing #inboundmarketing --- Send in a voice message: https://podcasters.spotify.com/pod/show/funky-marketing/message
In this episode we chat with Monica McEwen, Managing Director for Government and Public Services Strategy & Analytics team at Deloitte. She talks about use cases in government for AI & Analytics, how to make a career change in the pandemic, and how to find mentorship & sponsors. About Monica McEwen Monica is a Managing Director in the Government and Public Services Strategy & Analytics team at Deloitte. Monica is a thought leader with experience helping government agencies with their most challenging analytics & data challenges. Prior to joining Deloitte, Monica has spent the last her career working for software companies (ThoughtSpot, Qlik, Cognos) and supporting Government Agencies. Monica loves to work with clients on their most challenging data problems and brings the perspective of solving business problems combined with technology. Overview Learn the key points along Monica's journey that has gotten her to where she is today. She answers some questions like " What are the types of problems governments are looking to solve with analytics?" and "What advice do you have for those looking to work in the public sector? ". She shifts into the great resignation and how many people are searching for change. Monica shares how she has dealt with change in the past and gives advice for those facing a mid to quarter life crisis and want to make a change. In addition, Monica shares the power of mentorship and tips on how you can find a mentor. Seeking a mentor or mentee? We are here for you! Check out more information about our mentorship program below: https://www.womenindata.org/mentorship Social Handles Linkedin https://www.linkedin.com/in/mmcewen2006/ Learn more about our mission and become a member here: https://www.womenindata.org/ --- Support this podcast: https://anchor.fm/women-in-data/support
Janet Schijns is the CEO and Co-Founder of JS Group, a go to market consultancy dedicated to achieving results. She is the founder of the #digitalnormal movement in the industry driving profitable change in the partner community. Janet is a top 10 CEO Disruptor 2021, Top 10 Women in Technology 2020, 2021 (Analytics Insights), Top 50 Technology Influencers 2020, 2021 (Awards Magazine) and was named Channel Influencer of the year in 2019 beating out a slate of nominees from the top tech firms in the world. She has been in the top 5 influencers every year for the past decade. Janet was formerly EVP and CMSO at Office Depot, where she led a major transformation to drive traction in IT services, and prior to that, she was the Chief Channel Executive, Chief Marketing Technologist for Verizon Business. Janet is dedicated to the advancement of Women in Technology, founding her not for profit “Tech World's Half” in 2017 to address the issue of women dropping out of technology. She practices what she preaches having earned numerous industry channel awards, including four 5-Star Channel Program Awards. A native of the Garden state, she now resides in Florida with her husband Roy and has two children. In her spare time, she enjoys the beach and Star Wars. Connect with Janet on LinkedIn: https://www.linkedin.com/in/janetschijns/ Shout-out: Today's Diversity Leader Shout-out goes to: Patricia Watkins, Vice President of Partner and IoT Sales at T-Mobile - https://www.linkedin.com/in/patricia-watkins-964ab02/ John DeLozier, President, Intelisys, https://www.linkedin.com/in/ACoAABAtSgUBcuISflxF4hUVxh2XRgXdY3ZACjM/ Sammy Kinlaw, SVP of Customer Communities in North America, TD SYNNEX, https://ir.synnex.com/news/press-release-details/2021/TD-SYNNEX-Names-Sammy-Kinlaw-SVP-of-Customer-Communities-in-North-America/default.aspx Music: Intro - Vente by Mamá Patxanga is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License Outro - Amor Y Felicidad by SONGO 21 is licensed under a Attribution-NonCommercial-ShareAlike 3.0 International License --- Send in a voice message: https://anchor.fm/si-suite/message
Janet Schijns is the CEO and Co-Founder of JS Group, a go to market consultancy dedicated to achieving results. She is the founder of the #digitalnormal movement in the industry driving profitable change in the partner community. Janet is a top 10 CEO Disruptor 2021, Top 10 Women in Technology 2020, 2021 (Analytics Insights), Top 50 Technology Influencers 2020, 2021 (Awards Magazine) and was named Channel Influencer of the year in 2019 beating out a slate of nominees from the top tech firms in the world. She has been in the top 5 influencers every year for the past decade. Janet was formerly EVP and CMSO at Office Depot, where she led a major transformation to drive traction in IT services, and prior to that, she was the Chief Channel Executive, Chief Marketing Technologist for Verizon Business. Janet is dedicated to the advancement of Women in Technology, founding her not for profit “Tech World's Half” in 2017 to address the issue of women dropping out of technology. She practices what she preaches having earned numerous industry channel awards, including four 5-Star Channel Program Awards. A native of the Garden state, she now resides in Florida with her husband Roy and has two children. In her spare time, she enjoys the beach and Star Wars. Connect with Janet on LinkedIn: https://www.linkedin.com/in/janetschijns/ Shout-out: Today's Diversity Leader Shout-out goes to: Patricia Watkins, Vice President of Partner and IoT Sales at T-Mobile - https://www.linkedin.com/in/patricia-watkins-964ab02/ John DeLozier, President, Intelisys, https://www.linkedin.com/in/ACoAABAtSgUBcuISflxF4hUVxh2XRgXdY3ZACjM/ Sammy Kinlaw, SVP of Customer Communities in North America, TD SYNNEX, https://ir.synnex.com/news/press-release-details/2021/TD-SYNNEX-Names-Sammy-Kinlaw-SVP-of-Customer-Communities-in-North-America/default.aspx Music: Intro - Vente by Mamá Patxanga is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License Outro - Amor Y Felicidad by SONGO 21 is licensed under a Attribution-NonCommercial-ShareAlike 3.0 International License --- Send in a voice message: https://anchor.fm/si-suite/message
Cari Heibel-Briner is the President of Coaching at Achieve Freedom Coaching. Daniel Ramsey is the CEO and Founder of MyOutDesk. The #1 Rated virtual assistant services by Techradar and Analytics Insights. Learn more on how to know you have a good vision statement for your business and the proven process, the 'I - We - Do It'. Schedule a FREE Double Your Business strategy session now to get more details on how to scale your business using a virtual assistant: https://bit.ly/3pCgD6F Here are some links to important info about MOD
Audit Data Analytics Insights With Jason Miller by Wolters Kluwer
Canary Cry News Talk ep. 394 - 09.27.2021 - OCCULTECH MAGIK: Black Goo Greta, $ADA Mystery Religions, Anti-Jab Jocks, X-Men Nephilites - CCNT 394 Our LINK TREE: CanaryCry.Party SUBSCRIBE TO US ON: NewPodcastApps.com SUPPORT: CanaryCryRadio.com/Support MEET UPS: CanaryCryMeetUps.com Basil's other project: Ravel Podcast INTRO Biden gets booster on air Black Goo Greta Cover (Your Celeb Mag) Hamster has been trading crypto, out-performing S&P 500 (Biz Insider) FLIPPY 0:25:31 Can football (soccer) playing robots beat world cup winners by 2050? (BBC) GREAT RESET/CRYPTO0:37:03 Chinese News: China cuts power and production (Bloomberg) Note: Gov't must relieve supply chain turmoil (Financial Times) Huge news from Cardano $ADA during summit (Bloomberg) -Hoskinson donates $20 mil to Carnegie Mellon (Carnegie was a Mason, Mellon ties) -Partnership with Dish Network (Dishfire of NSA, $5 bil deal w/ AT&T, AWT 5G partnership) -Acala PRISM to offer DID on $ADA (PRISM of the NSA, DID traditional) -COTI to issue Djed algorithmic stablecoin on $ADA (Djed ancient Egypt) -Ouroboros PoS protocol for $ADA Australia order $14 million Ouroboros sculpture COVID 19 JINGLE/PANDEMIC SPECIAL 1:09:46 Clip: More tyranny in Australia (Brisbane military airplane drive by) Clip: Even more tyranny in Australia (photo of mask police going viral) Clip: “Get him, he's leaving his house!” Clip: UK protest with thousands Clip: NY Gov says NG to replace unvaccinated medical Ohio State HP “aware and monitoring” possible trucker protest (Fox19) R1 Variant starting to spread (SF Chronicle) Thousands dying but not from C19 (Yahoo/Telegraph) Clip: Natural Immunity potential legal challenge to federal mandates (Yahoo) I AM WACCINE 1:54:44 Clip: Scientist Dr. Ryan Cole, jab autopsies Headline: Court blocks NY city school mandates (WHBI) Unjabbed clash in NBA, Kyrie Irving satanic conspiracy (Sports Illustrated) Headline: GS Warriors, Andrew Wiggins denied religious exemption (NY Times) BREAK (producer party) 2:15:31 POLYTICKS 3:20:26 Newsom Science/Alien: CA to replace the word “Alien” for “noncitizen” and “immigrant” Harry Legs: Biden purchases drones from China (National Pulse) NEPHILIM UPDATE 3:30:55 X-Men could join MCU as Nephilites, not mutants (Screen Rant, MCL) ADDITIONAL STORIES Church at planned parenthood permanently ordered away from clinic (Spokesman Review) Joe Rogan says Trump will probably win 2024 (DailyMail) Robot Arm milking cows (ChipPewa) Not financial advice, Airport robots to rise to $2.5 bn by 2030 (Global News Wire) Mental Health: Use economic, medical, and social data for policy (WEF) India covering up snake bite massacres (Daily Beast) Proud Boys in contact with FBI on Insurrection Day (Yahoo) Inside CIA secret war against Wikileaks (Yahoo) Facebook Ray-Ban Smart Glasses solves problem, but privacy issue (Yahoo) Will robots be able to have children, celebrate mothers day? (Analytics Insights) The Army is modeling future robots on…squirrels (Pop. Mechanics) Robot designs inspired by nature (Design Boom) Scientists create genetically modified coffee (DailyMail) Waccines C19 death toll surpasses 1918 Spanish flu estimates (Smithsonian) National Guard ready to replace health care workers (Reuters) Gates Foundation, NIH, CDC funded C19 jab effectiveness study (MedRxiv) PRODUCERS ep. 394: Scott K**, Brian D, Anonymous, Aaron J, Sam W, Sir Sammons Knight of the Fishes, Sir Casey the Shield Knight, JC, Heatheruss, Sarah P, GiantsBane16, Brandt W, Veronica D, Big Tank, Juan A, Gail M, Doughty the Coyote, Runksmash, Ciara, Rob TIMESTAMPS: Christine C ART: Dame Allie of the Skillet Nation Sir Dove, Knight of Rustbeltia Ryan N Mike B Christine C
Join us for an insightful conversation with Ben Reiter, host of the podcast THE EDGE and the author of ASTROBALL: The New Way to Win It All, a New York Times bestseller.
Jacky Mampana is the Head of Analytics, Insights and BI at Holland Insurance. Jacky runs the end to end data strategy for a South African insurance company.
In a recent Inside Higher Ed piece co-written by leaders at Huron Consulting and Whiteboard Higher Education, the authors noted that “as a rising chorus of higher education leaders and commentators is observing, now may be the time for a great many institutions to set aside hope as the foundation of their strategies going forward and fundamentally reimagine their business models.” On this week's episode, Zach is joined by Rob Bielby, the Vice President of Analytics & Insights at Whiteboard Higher Education, to discuss how enrollment marketers can more effectively leverage data to make informed enrollment planning and financial aid strategic decisions.In light of the higher ed challenges brought on by COVID-19, Rob and Zach dive into the following topics and questions:Where does friction exist when it comes to the business model of higher education and how should individuals working in enrollment management, marketing, and graduate admissions have productive conversations about reexamining business models?What indicators can enrollment marketers use this fall and into next spring to discern inquiry and applicant quality when they can't rely as much on some of the things they're used to depending on, like campus visits?How can marketing and communications and enrollment management professionals cultivate, as Rob puts it, a "longer-term enrollment perspective" while managing short-term demands?Tune in for an exciting discussion about making the best use of your resources to find and attract your best-fit, high-quality applicants, even (and especially) amidst a pandemic.
Revenue Generator Podcast: Sales + Marketing + Product + Customer Success = Revenue Growth
Today we're rebroadcasting our conversation with Michael Lukich, an Executive Leader of Marketing and Analytics Insights at Booz Allen Hamilton, which is a management technology and engineering consulting firm that provides services to leading Fortune 500 companies, governments, and large non-profits. Prior to his role at Booz, Michael worked with consumer brands including GE, Marriott, and Total Wine. He is also an Adjunct Professor of Digital Analytic Measurement at Georgetown. Show NotesConnect With: Michael Lukich: Website // LinkedIn The MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Today we're rebroadcasting our conversation with Michael Lukich, an Executive Leader of Marketing and Analytics Insights at Booz Allen Hamilton, which is a management technology and engineering consulting firm that provides services to leading Fortune 500 companies, governments, and large non-profits. Prior to his role at Booz, Michael worked with consumer brands including GE, Marriott, and Total Wine. He is also an Adjunct Professor of Digital Analytic Measurement at Georgetown. Show NotesConnect With: Michael Lukich: Website // LinkedIn The MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // Twitter
Data Futurology - Data Science, Machine Learning and Artificial Intelligence From Industry Leaders
Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016. In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams. Enjoy the show! We speak about: [01:20] How Tony got started in data [08:20] Tony’s skills come from the commercial side [11:10] Linking data science and the business [14:30] Communicating how data science works [17:00] Steps to getting others to understand data science [20:40] Getting the best talent for your team [24:00] Structuring teams and the department [28:10] Transiting from analytical roles to commercial roles [35:30] Working on global expansion [38:10] Solving with artificial intelligence [42:30] Passionate about using numbers to reach an outcome [44:00] Modelling failures with Sportsbet [47:50] Imposter syndrome in data science [50:05] Data science is rapidly changing and exciting Resources: Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/ Sportsbet: https://www.sportsbet.com.au Tony’s Twitter: https://twitter.com/gruebz?lang=en Quotes: “There is no one path that always works.” “There are literally thousands of things data scientists couldn’t potentially tackle in any business.” “If you’re not making mistakes, then you aren’t pushing the envelope hard enough.” “Not having imposter syndrome is a sign of lack of knowledge.” Now you can support Data Futurology on Patreon! https://www.patreon.com/datafuturology Thank you to our sponsors: UNSW Master of Data Science Online: studyonline.unsw.edu.au Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message
Jason Krantz is the Director of Business Analytics & Insights for the 135-year old company, Weil McLain and Marley Engineered Products. While the company is responsible for helping keeping homes and businesses warm, Jason is responsible for the creation and growth of analytical capabilities at Weil McLain, and was recognized in 2017 as a “Top 40 Under 40” in the HVAC industry. I’m not surprised given his posts on LinkedIn; Jason seems very focused on satisfying his internal customers and ensuring that there is practical business value anchoring their analytics initiatives. We talked about: How Jason’s team keeps their data accessible and relevant to the issue they need to solve for their customer. How Jason strives to keep the information simple and clean for the customer. How does Jason help drive analytics in a company culture with a lot of legacy (from its people to its parts) The importance of focusing on context How Jason drives his team to be business partners, and not report generators Resources and Links: Jason Krantz on LinkedIn Quotes from Jason Krantz: “You realize that small quick wins are very effective because, at its core, it’s really important to get executive buy-in.” “I’m a huge fan of simplicity. As analytics pros, we could very easily make very complex, very intricate models, and just, ‘Oh, look at how smart we are.’ It doesn’t help our customers. …we only use about two or three different visual types and we use mostly the exact same visual set-up. I can train a sales rep for probably five minutes on all of our reporting because if you understand one, you’re going to understand everything. That gets to the theme again of just simplicity. Don’t over complicate, keep it simple, keep it clean.” “…To get buy-in, you really got to have your business case, even to your internal customers, really dialed in. If you just bring them a bunch of crap, that’s how you’re going to lose credibility. They’re going to be like, “I don’t have the time to waste with you,” even though we’re trying to be helpful.” “What my team and I do is we really help companies weaponize their data assets.” Episode Transcript Brian: Jason, are you there? Jason: I’m here my friend. Brian: Sweet. How’s it going? Jason: It’s going very well today. How’s your Friday going? Brian: I’m doing awesome. We’re going to talk a little bit about analytics. Is it Wile McLain or Weil McLain? Jason: I say Weil McLain. If I’ve been saying it wrong, I’ve been saying it wrong for a while. Brian: As I recall from my musical training, I think in German, the second syllable is the one that says its name. I guess it would be Wile McLain, like if it was W-I-E-L it would be ‘Weil.’ But I don’t know. Its anglicized as they come over the pond. Jason: I’m going to go with you on that when you sound like an expert. Brian: Nice. Well, you sound like an expert in analytics at Weil McLain. Tell us about what you’re doing over there. We met on LinkedIn, I’ve been enjoying your postings on the social feed about your approach. You seem really passionate about what you’re doing and I’m like, “I don’t know who this guy is, but that was really interesting.” I just have. Tell us about the company, what they do. I know they’re in heating, right? Jason: Yes, absolutely. The company I work for, and I work in the HVAC space, we’re a 135-year-old boiler manufacturer. Whether you realize it or not, you probably have one of our products in your house or building or very close to where you live. What my team and I do is we really help companies weaponize their data assets. As you know, a lot of companies are very skilled at acquiring data since the Big Data Movement. But the reality is that a lot of these companies don’t know what to do with all this data. That’s where we really come in. What I always tell my team and our business partners that we work with internally and externally is that our focus is on solving business problems. In order to do that, you have to identify what is the business problem that you’re trying to solve or strategic agenda that you’re trying to address. In order to do that, you really have to be anchored in the biz. Again, that’s just my perspective, but if you’re in the business day in, day out, you develop this very keen stand of what the business would need to accomplish its objectives. Just like right now, we are based in the marketing group and it’s a great spot to be. I’m a firm believer that every analytics team should be based in the business for a reason that I just talked about. But what that does being business-first is that gives us a great lens to look at data from. Sometimes analytics people would be IT-centric and they can do a lot of academic work against the data set or different data sets. But the business might look at the output and be like, “Yeah, that doesn’t help us.” We always, always, always start with, “What is the business problem we’re trying to solve or strategy we’re looking to address?” It also helps us when it comes to curating data also. That’s one of our primary response [00:03:21] this too, is to look for different data sets both internal and external that can help us identify strategic opportunities. It sounds really unsexy, I’m not going to lie. I think some of my LinkedIn post just say that data is boring. It really is. It’s mind-numbing, too, about 85% of my customers. But that’s the important part is understanding what do our customers need and that’s really the lens that we look at this through. We are a service provider, our customers are internal and external, we have customers just like any other business. We have to take this really boring, but really potent product in data and make it accessible to them. That’s really where we use design to really try to make that magic happen. Brian: I love that you said, “Trying to understand what the problem is.” This is something we talk about on the mailing list quite a bit. In fact, falling in love with the problem is a good basis for doing good work instead of kind of jumping to solutions or feeling […]. As I tell my clients sometimes like, “Our job is not to go and visualize the data. It’s not […] available for someone to put into another tool or whatever the heck it is. The job is to find an insight that already is used. Probably they’re already in your job and you’re there to make […] if you’re doing internal analytics. Help them do a better job at what they’re doing, offer more value. You need to figure out how to work that into their life.” For example, for you guys then, your customer, I assume is it primarily sales people that you’re working with? Who are your customers and your […]? Jason: Yes. Great question. One of our biggest customers is sales. Sales has been one of my biggest customers for the past 10 years of my career. I’m very intimately involved with the sales team, sales operation, sales optimization, insight gathering, pricing, things like that, but also marketing. We do a lot in terms of competitive intelligence gathering, market research. We also do a lot of operations in finance obviously related to the prices, that sort of thing. We really touch all areas of the business, but without question, our biggest customers are going to be sales and marketing. Brian: If you were to bring a new initiative like, “Hey, we have access to…” I don’t know what it might be but for you maybe your point, [might be a line 00:05:49] of data that could actually give them more leverage. We know what the negotiation brings, better than […], we know we kind of have an idea now from what the industry is doing for their sales such that we can now tell the CRM like, “This is your […] or something.” When do you get that sales person involved? Do you deliver a solution and get feedback? Do you bring […] early and say, “Hey, we think we can tell you more about how to do better pricing on the spot with this thing.” Do you bring them in or when do they fit into your process? Jason: Great question. A lot of times because we spend so much time actually in the trenches, that’s one of things I think is unique about the way that I design my teams to do analytics. It’s not like hand off product and we’re like, “Godspeed. Good luck.” Once we deliver a solution, we’re actually in the trenches with the business trying to implement what we’re talking about because it just works better. The team work is just more effective and they know that they’ve got back up, they know they’ve got air support. Really, a lot of times when we come up with something new, a lot of times we will frame it from the lens like, “Hey, we know that we’ve got opportunity A or issue B, or whatever it is. This has been an issue or an opportunity for months or years or whatever.” We think that we’ve identified something that could help us in solving that issue or realizing the potential of that opportunity and then it becomes, “Okay, let’s sit down and talk about, do you agree that this might actually help us in this process?” Because the one thing that I’ve learned is, in order to get buy-in, you really, really got to have your business case, even to your internal customers, really dialed in. If you just bring them a bunch of crap, that’s how you’re going to lose credibility. They’re going to be like, “I don’t have the time to waste with you,” even though we’re trying to be helpful. What we found out is if you really dial in what are we trying to address with this, just as you would with any business case, and you bring that to them, I have found that they tend to be much more receptive. It’s not to say there’s not going to be resistance—resistance comes with any change—but we found that typically framing it from that lens and saying, “We’re trying to solve a problem that you have, we think that this data will help,” that’s a great starting point. Brian: Do you have an example of a before/after with that? I don’t want you to get into proprietary stuff you can’t talk about but is there like a, “Before they did it this way,” and then we brought them in and said, “Hey, we think we can get […].” and how you went [00:08:19]. Jason: Yeah. What I can talk about is just the manner in which we distribute sales information, specifically insights. I think that, for your listeners, this is going to ring true to a lot of sales forces. I know for all them that I’ve been in or worked with, this case was true 100% of the time. But one of the things that, again, keeping the customer-centric focus, that if you look at your sales reps, a lot of time is you’re going to be what I call casual data consumers. By that, I mean that these are guys and gals that aren’t really into data day in and day out like guys like you and I or some of the listeners maybe. What we have to do is, as I always encourage my team to take empathetic lens and look at, “Okay, if we give them what our first […] is going to look like, how are they going to interpret this?” A lot of times, to be honest, it’s not very good. Now that’s where we have to look at internally and kind of rationalize and say, “Okay, let’s find this. One of us will find [00:09:14].” But one example of that is traditionally, sales reps and sales teams will get the information in a flat Excel table. Just lots of rows and columns and just gibberish everywhere. That’s a very financial-centric view of sales data. But the reality is—I don’t know about the rest of mankind but I know for myself—I can’t remember much more than 10 numbers. The mental computational cost of extracting insights is just gargantuan. What happens is, I just don’t even bother to do it. I’m just like, “Yeah, whatever.” An equivalent of that is, you know if you get a big block of text in email? Even though if you took that same block of text and broke it up into two paragraphs or two sentence segments which is very easy to read when you put the effort in, but for me, if I get a big block of text, I’m not even going to read that. It’s kind of one of the same things that we see on the sales side. What we do is just say, “You know what? There’s a lot of really good information here and we need to make it digestible for our customers.” That’s where we found traditionally, visualization can be an incredibly effective tool to communicate insights to this casual data audience, to this casual data consumer. Brian: Do you have to work through the visuals with them? Do they tend to get it the first time? Is it a process of you share, “Here’s a report or here’s some new view on X.” How do you know if the visualization is actually allowing them to pull the insight out of what other [00:10:46] broad data? How do you know they’re actually “getting it”? Jason: That’s a great question. I’m a huge fan of simplicity. As analytics pros, we could very easily make very complex, very intricate models, and just, “Oh, look at how smart we are.” It doesn’t help our customers. It doesn’t help anything. Really what we do—this is going to get to the theme of simplicity—is we only use about two or three different visual types and we use mostly the exact same visual set-up. Just to kind of frame it, what I’m a big fan of is a simple bar chart. There’s more details attached to it but to the right of the bar chart, we’ll typically put a tabular data set. What we do is, as you think in US at least, we start in that left-hand side of the page or we […]. What we do is we look at the visual real estate. We say, “Our customers are going to start in the left-hand side. We want them to look at the bar chart because it allows them to very rapidly assimilate it at a high-level what’s going on.” It’s great at communicating at top-level churn very quickly but the trade-off is, is this horrible imprecision. You have no precision at all. What we like to do is then we address that issue by putting a simple table, very clean, very simple table over to the right. What that does is that then provides the precision that the customers are seeing in most financial-centric tables. What we found that does is that we have to train our sales team on one set-up and then that set-up is used virtually universally on all of our solutions. As an example, I can train a sales rep for probably five minutes on all of our reporting because if you understand one, you’re going to understand everything. That gets to the theme again of just simplicity. Don’t over complicate, keep it simple, keep it clean. Brian: I think those are good. A lot of times, when I work with engineering clients, they fall in love with consistency. I guess one point to maybe just the contrary of this is that, I think consistency is generally a good rule with design. We want to minimize unnecessary change but at the same time, I would recommend to listeners is to always look at context first, and context should always come in. Let’s say Jason comes up with report number 12 and they have 11 now or whatever, and it doesn’t feel right for number 11. That’s a place where a designer would probably push for, “Well, no. The 12th one actually needs to be different because it’s not […] 11th and even though it’s not consistent, in this context, we don’t need it to win. This version will deliver the usability and the utility that we’re looking for better than the other 11 will.” In general, I think it’s smart to not get creative unnecessarily with meaningless ink on the screen like, “Let’s try it this way. Let’s change the color palette. I’m tired of this.” Those are not good reasons for […], you’re just introducing noise and it’s unnecessary. But I like that you guys are thinking about simplicity and trying to reuse templates and not looking at it as a creative tableau. Ironically, people think it’s a creative “design” tool, but at the same time with all those weapons, you have a lot of different weapons you can use in that toolkit and part of that is knowing how to use this. It’s the same thing with Photoshop, a million buttons and all this stuff. The Photoshop doesn’t make you a designer. It’s being aware of your customer’s pain and the problems they need and knowing when to use all those filters and all those different things that it can do. I like that you guys are looking into that simplicity and reusing templates when it’s meaningful to do so. Jason: You bring up some great points and I 100% agree. My team that’s listening there, they’ll laugh because I beat it in their heads, “Context. Context, context, context.” Both in design as you’ve talked but especially with numbers in general. Like, “If I give you a number, a billion, that doesn’t mean anything, you got to have context.” I’d say the same is true for design just as you articulated. Great point. Brian: Where does the impetus for “everybody is a data company, everybody wants to do analytics”? But then there’s operationalizing that, there’s getting buy-in, leadership behind it. Where does that come from in your org? Where is the interest in taking a 130-year-old company and getting it to care about this? Where does that come from, your influence and all of that? Jason: That was driven by our current president because he saw it as part of a digital transformation. Obviously, this was an essential component of that. Obviously, we do a lot with analytics, but we’re also involved in a lot of other digital components that lead to that overall digital maturation. Analytics is a very, very big part of what we do but it’s not all that we do. We serve as kind of that quarterback for a lot of the digital initiatives to help basically, guide them through the process. Because even though some of the nuances of each of this project, each one will have its own nuances, they all come back to data. Data is the currency. We found out pretty quickly that if you want to stay relevant in this day and age, you need to be digitally evolved but more importantly, as you look at it, do you [compare the 00:16:02] advantage that you can derive from analytics? I would argue that gap is slowly closing known certain industries like manufacturing, but we probably have a little bit more runway [00:16:10] it. But for a lot of industries, analytics is becoming table stakes. It’s one of those where you can certainly expect incremental value and competitive advantage, but the question becomes how much longer. That was kind of the impetus of saying, “Hey, we got to get this going sooner rather than later.” Brian: Do you have people in sales that are resistant to using the reporting or taking advantage of your information or is it pretty ingrained in the company culture that it’s like, “This is a tool. Why would you not want to use it?” Or did you guys have a […] getting adoption? Jason: Yeah. I would say anytime you’re going through a transformation of this magnitude, it’s hard and I would say especially for other manufacturers. I found in general, manufacturing in general, tends to be one of the laggards industry-wise in analytical maturity. Unquestionably, it’s tough for no other reason than change is tough. You’re taking legacy plants, legacy steer pieces, legacy process, and some people has been around the company for decades potentially, and we’re asking them to change almost on a dime on their time scale how they do business. It’s not that it’s right or wrong but what we try to point out is that, as I always say, we have to acknowledge the past. We’ve been where we’ve been, we’ve been successful at where we been. But there’s been more change in the past two or three years than maybe you’ve seen in the past 15-20 years. In order to stay relevant, you really have to be ready to evolve, not only evolve but evolve quickly. But I have to openly acknowledge that that’s hard. It’s a hard proposition for a lot of people. Again, it comes down to change management and managing not only expectations but supporting that change. Change doesn’t happen by itself, we have to support that. That’s really what we try to coach through. The way that we try to do that is by developing a product with our customers. I’m sure as you can […], if you force something upon somebody, it doesn’t get received too well. But if you develop it in conjunction with them and do tie it around their needs, it tends to get better adaptation. Brian: You used the word product in there and I’m interested, do you see the outputs of your efforts? Primarily, it’s BI reporting as I understand it. Do you look at that as the product that you offer to sales? Is that kind of how you see it? Jason: Yeah. We offer a product in the form of the insight packages but it’s also the service. Service that goes with it where again, we serve as essentially internal consultant to help them along. If you take just the product-centric approach, you just deliver an insight package and you’re like, “Good luck. It’s [00:19:35]. Have at it.” What we do is we deliver the product and then we partner with them and say, “Okay, here’s what we see. Now, remember you’re talking about this going on in the channel last year and our note show that there’s been a lot of competitive activity in this area. Here’s some of the question that we have. You’re the expert, so what do you think?” What we found is that working together like that, we tend to get pretty good results versus just leaving these guys on an island to kind of figure it out themselves because they virtually always know the answer but sometimes it’s up to us using these products and then offering the service is to ask question that maybe aren’t getting asked. A lot of times, we find out that they know the answer it’s just that you kind of have to ask the question. Brian: Is that often like, “I was using XYZ report. Could you break this down by county instead of just by whatever because I feel there’s more people living in the East side of town and the average is here or […] the whole county. I really just need this one county because that’s where everyone lives. Is that really underserved? Blah, blah, blah,” that kind of stuff and then you guys will go off and work with them for more of that detail then maybe you release that back into the product as a feature if it seems like a one-off or something. Is that how it works? Jason: It’s actually a very fluid process. An example of what you just described is exactly what happens if hey come to us with questions. But we also do it where we flip it around because a lot of products that we create are more aggregate discussion tools. We don’t design a lot within our primary visualization package. To really get into the weeds and everything just becomes overwhelming. We have other tools like your traditional [00:21:22] pivot table to kind of dig into that stuff. But the exact example that you just gave, they will ask us those questions, but we will also flip the script and say, “Hey, we saw that the mechanical chain in the Northeast is up 50%,” I’m just making up a number, “and at a higher level, you can see that but when we segment it out, here’s what we see. Not only when we break this down to this level, we see that’s specifically being driven by A, B and C.” That gets to where I push heavier at my team to do root cause analysis. That’s really where we provide value is by digging into it and asking questions like that. Again, operating from the lens of trying to solve a problem or answer a question or root cause something in conjunction with the business. A lot of times, we will ask those questions and at the same time, they will ask us, which is great. It’s amazing because you get the better solution faster. Brian: I think that’s great. I’ve worked on several different tools that have varying sophisticated means of doing root cause analysis and I think it’s a really powerful way to bring some why to a what that has happened in the past. Most of the time, why is really where the money is at. The value comes in being able to understand why. A lot of times, we don’t have all the data. You can’t know for sure but a lot of times I tend to say, “Our guess, if they’re just going to make a WAG—a wild ass guess—then our guess, as long as we qualify what ingredients went into the pie, our guess may be better than any WAG.” They’re going to make one already. If they’re going to make a decision here and go off gut, there is maybe a chance they’re right and their experience will say something. But maybe our elementary root cause analysis, which we can improve over time, will actually be better and we can get out of the total guessing game and start with something that’s kind of a macro ballpark thing. Then overtime, you can improve that analysis as new data becomes available or maybe learn about how two variables are related in the business and you can bring that knowledge into the system. I totally hear what you’re saying. It’s a nice mix of internal product plus services and also, it sounds like it gets you guys do good discovery work as well. You guys are not just responding to questions but you’re maybe asking them questions together as a group. You kind of work through what opportunities maybe latent that no one’s talking about by asking questions using data to do that. Jason: Yeah. In the lens that we’ve been talking through, this is really sales-centric, but this applies to any group that we interact with. We have the same level of proactive discussion with any group that we interact with. In some of these, in our market research side, it’s 100% proactive. We’re going out there scouring for information and trying to see the other things that we see. That one it’s completely proactive and now we bring insights to the business and say, “What do you guys think?” The sales one is the most fun because, let’s be honest, there’s no business if you’re not selling anything and nothing happens until a sale is made. Brian: Right. I get that. You talked about other clients, do you work at all with the actual hardware, is there any IoT type of analytics going on with the boilers and machinery that you guys create? Jason: We’re early in that process. We actually are getting ready to go down that task very soon. On the hardware side, we tend to not have as much involvement. That’s really more on the engineering group. I think for any manufacturer product or engineering groups probably going to be the most involved in that. But obviously, we get involved into the discussions of answering the fundamental question. What are we actually going to do with this data when we collect it? Because as you can imagine, IoT can spit out a lot of data real quick. They can become incredibly burdensome very quickly if you don’t have a plan on how to manage it. But then, if you’re going to go through the effort of managing that, you got to be able to say, “What are we going to do with this?” Brian: Yeah. I guess the first thing that would come to mind for me would be predictive maintenance, like, “Is it going to break down soon?” I worked on a cooling company that does cooling and really as the guy told me is like, “We’re not selling refrigeration. We’re selling consistent temperature to our clients. It’s not really about coolers and all of that, so we need to deliver consistent temperature. If we don’t do that, they lose products, they can lose whatever is being stored in cold storage.” That is significant business. I’m sure for you guys, it’s heat, you want to sell heat so how do you get in front if there’s a maintenance plan or whatever, how do you stay on top of that kind of stuff? Jason: Absolutely. Brian: [00:26:13] IoT. One of my clients used this word one time, which I now use all the time which is like, “We don’t want a metrics toilet.” An example of you can get to a metrics toilet really quickly with every stat under the gun and how many ounces of water per minute through this pipe, that’s great because that’ll help me do, as a sales guy or as a technician, how am I going to use that information just because there’s a sensor on that pipe. It’s working something around like, “Oh, there’s a sensor. Put the data in the grid.” Jason: I’m going to have to borrow that. I’ll give credit whenever I use ‘metrics toilet,’ that’s a pretty good one. I may actually [00:26:56]. Brian: Nice. Tell me, where does it go from here? You had mentioned like, “Oh, the competitive edge, maybe it’s closing.” Or maybe you guys feel your competitors are all kind of maybe they’re doing the same thing that you guys are doing and we are all aware of where the data can be used to drive the business. Are there other places where you see design or technology like predictive analytics or machine learning and some of these other new technologies that are out there to help drive predictions and things like that? Are you guys leveraging any of that or have plans to look to the future? What does that look like? I know you probably can’t talk about everything but maybe broadly. Jason: Absolutely. I would say that that’s content that’s definitely, if it’s not already being done then it’s on our radar. We’ve got a pretty talented team here that goes a lot of your traditional data science turf. As you can probably surmise in this conversation, is in addition to having all skills, we’re probably the most heavily focused on the business side. As we say, we explore opportunities for a lot of this. We always look at it, again, like machine learning. Great, but we got to make sure it’s very powerful stuff. We got to make sure that whatever we’re embarking upon, because we have finite work capacity, if you pursue something, machine learning, it means we’re not doing something else. It’s not to say that it’s not important, but we really have to be able to answer to that question. Again, come back to, “This is our anchor. What are we going to do with it?” I love this stuff. I love the stats. I love machine learning, AI, all that stuff. If you’re not careful, you can really quickly get into an academic exercise that we think is really cool. “Oh wow, look at this. We’ve got this awesome algorithm here. It does all this magical stuff,” and then the business looks at it and goes, “Yeah, so what? I don’t care. How does that generate revenue? How does that improve our margins? How does that reduce our cost? How does that enable to build the sales pipeline?” If we can’t answer those base questions and we don’t get alignment, that’s probably the most important thing is executive buy-in on exactly what we’re going to be working on, why it’s important. No, we don’t pursue it but those things are most definitely, as with any analytics teams today, I think that that content is definitely being done and/or on your radar. Brian: You make a really good point. Sometimes I almost hesitate to ask the question. But I think it’s an exciting space in terms of predictive capability and removing viable analysis and what we call time tool time in the design world, there is there. But at the same time, you make a really good point which is again, these are tools that need to be leveraged to service an opportunity or a problem. The goal is not to go do the machine learning, the goal is to solve a business problem by which machine learning maybe applied a better […] do it, reduce cost or reduce effort, speed, something like that. I completely respect that. I’m glad to hear that you guy are looking that as not a leading step. I know there’s conflicting signals out there. I’ve been talking to people in the International Institute for Analytics about this and at the same time you hear a lot of stuff which is, “If AI is not part of your strategy, you’re going to be missing out,” and boards just want to hear that people are doing AI. At the same time, you’ve got academic exercises going on, you’ve got people trying to take on massive like, “We’re going to shoot for the moon,” and it’s like, “You don’t even have an airplane and you’re trying to go to the moon with this thing. Show us a small win if you’re going to do an investment in AI.” It’s okay to go try it out and say, “Let’s do a small thing but let’s try to solve a business problem or have some definable output that we’re looking to do here such that we’re not just writing code and doing experiments.” I hear there’s a problem with people putting this on their resume. It’s like people just want to have machine learning. Everyone’s a data scientist now that used to stay in analytics. [00:30:47] It’s scary in the sense of just wasting opportunity and wasting money because at some point, your smarter competitors are going to be saying, “This is a new hammer. Let’s find some nails that we can use for it. But we think […] right nails and it needs to be the right application before we whack at it. It’s not just […].” Jason: I really like your point because again, if my peers were listening to this they will laugh because they say, “We are professionals of this trade and the tools that we might want to use might not be the right tool to use for a specific job.” I couldn’t agree more with that sentiment. It’s one of those core philosophies that I have and share with my team. Also to it is with the AI. I think that you truly made a very astute observation here and comment in that, I think a lot of companies do feel compelled to have to make significant investment in AI like today. It’s not to say that there’s not merit. There clearly is plenty of merit and plenty of potential there, but kind of your point, I really believe that it’s much more beneficial when you really minimize the risk of project and budget flow and minimize overall project risk. You take that small bite and try a little bit, then try a little bit more. When you get to win, socialize the win, and your executives feel comfortable because I’ve done it on the analytics side. I went for a big bang approach and after nine months they were like, “Hey, man. Where’s the output?” All you need is to get bit by that once and then you realize that small quick wins are very effective because at its core, it’s really important to get executive buy-in. A lot of executives are not willing to wait nine months or a year for something when they’re expecting to see at three months. I totally agree with your sentiment. Brian: When you talked about the wins, I totally understand if you’re close to it and maybe hard to remember those, but is there a particular story or time where something in the product and the insights that you guys put out to your customers that it was like a real win, like a sales guy said something to you or maybe an executive said something to you about how this moved the needle, like this was a memorable moment for us. Like, “I changed a customer’s mind with this,” or, “We closed the sale that we never would have been looking over here if we didn’t do it.” Do you have any anecdotes like that that you can share? Jason: One that we had recently, again, just for confidentiality purposes I can’t get too deep. Brian: Sure. Jason: We did have one recently where we just basically revamped our insights packages that we distribute to our internal team. We really, really gathered feedback. We had version one, we gathered ton of feedback, kind of refined, iterated, got the feedback without making it a major release. Got feedback, refined it, refined it, and then what we did was, with a small group, we got that beta in their hand, they look at it and they’re like, “This is great. This is exactly what we need.” Because what we were doing, what we found was—I’m sure you’ve experienced this—everybody wants their own part of things. Everybody wants certain view of a report or they want certain insights or whatever it is, and it’s great. But if you have limited resources, really high-powered resources like an analytics team or data science team, you’re going to look at the opportunity cost of trying to do one of these one-offs, we were getting a ton of report flow. Again, what I tell my team, I don’t mean to be derogatory to the DI guys in this comment, but my team’s side, I always tell them, “We don’t create value if we’re just creating reports. We create value when we’re actually partnering with business to extract insights, identify opportunities amidst all that stuff that goes well with it.” What we realized though is that, what started out as a nice, clean, three- or four-page insights package and blow it up to like 20 and [34:21] doesn’t that meet our original criteria? Essentially, what we do is once we have the rationalization enough to say, “Okay, we’ve got all these stuffs right across 20 pages. We can actually distill it down to four pages.” It will give you the exact same information, but it might not look the exact way that you wanted it to look. The question becomes, are you willing to deal with less stuff and maybe have it look a little different, but you’ll get it in a much more concise package that you’re actually able to use and process? What we found out is that a lot of people were doing these packages and getting the reports that they want but they weren’t actually using them to drive decision-making because they can’t see the paragraph or the block of text story before. They look at it and they’re like, “I don’t know what the hell to do with this.” We would dial that in and it just been a screaming success. It’s really nice to have it where something like that you see the evolution of it. This is just one of those things that we had, and this was kind of a side package or wasn’t a primary, but it’s become a primary now because it’s so effective. Brian: Would you say that when you talk about reducing this, is the report like a PDF or do they access it through a browser the insights package? Jason: Yeah, we have the options to do both. We distribute it initially via PDF, sometimes along with our comments if there’s really, really big stuff in there. We’ll say, “Hey, we see this. Here’s a driver. Here’s a supplemental package.” A lot of times it’s PDF first and then if they want to go on the web, start interacting with it, they can do that. Those are nice, but the reality is a lot of them don’t do that which is understandable. Brian: You took it from 20 pages down to 4, is that what you’re saying? Jason: Yeah. Same information. Brian: This is a really good point. I’ve frequently had clients come in and they’re with data products and their concern is information overload. We’ve heard this a lot of times and the irony is that, the issue is usually not information overload. It’s usually a design problem that the information is not presented properly because sometimes, it can increase the density and increase the utility and usability, not the other way around. In fact, removing data can actually make it worse. A basic example of that is when you’re trying to compare A and B. If A and B are not on the same, what we call a viewport like in a browser world, it would be within the browser window there. When you require someone to toggle between two screens, they have to change context and visually, your eye can process the information a lot better when it’s within proximity. Sometimes, increasing the density actually will give you a better design. It takes more care in how you do it, but it’s not always about information overload, “Oh, it’s too crazy.” They may not get it on the first time but your sales people, if they’re looking at this stuff weekly or monthly, at some point they’re going to be pretty comfortable with this. I always tell my clients, “You need to look at the switch frequency as well because if it’s going to be used a lot, you can actually get more detailed and you can really push the, what you might see as complexity or the information density, can go up because they’re going to get familiar with the formatting. Typically, the density is actually going to probably improve the utility as long as care is given to the choices. But having that eyeball comparison without having to change pages and all of that, typically you’re going to give a better story as a broad rule. I like hearing that you guys went down in page count, up in density and in turn a better user experience at the end so that’s great. I think we’re about done here. I don’t have too many questions for you, but this is super great. One of the reasons I contacted Jason is because I remember seeing this quote, “Jason is like a category five hurricane in the data analytics world.” I’m like, “Who the hell is this guy? No one talks like that.” I started reading your stuff and I enjoyed watching your LinkedIn social posts and things like that. Where can people find out more about you? You’re obviously on LinkedIn, I can put LinkedIn in the show notes and stuff, but are you on Twitter, any social media places they can follow you? Jason: No, actually, I’m not on Twitter. But the best place unquestionably is going to be LinkedIn. I’m pretty involved there. I do like to engage. If you want to direct message me with questions, just talk, meetup, connect, whatever it is, I welcome that. I love the platform, it’s a great family. I just really started using it maybe nine months ago, really getting into it. It’s been great meeting guys like yourself. It’s actually phenomenal. Brian: Cool. I’ll put a link to Jason’s LinkedIn profile on there and you guys can find him. I recommend, especially if you’re in an internal analytics type of role at your company, to follow Jason and then check out what he has to say on there. This has been great. Thanks for coming on the show. I look forward to meeting you at some point in person. Jason: Dude, thank you for having me on here. I really appreciate it.
Jason Krantz is the Director of Business Analytics & Insights for the 135-year old company, Weil McLain and Marley Engineered Products. While the company is responsible for helping keeping homes and businesses warm, Jason is responsible for the creation and growth of analytical capabilities at Weil McLain, and was recognized in 2017 as a “Top 40 Under 40” in the HVAC industry. I'm not surprised given his posts on LinkedIn; Jason seems very focused on satisfying his internal customers and ensuring that there is practical business value anchoring their analytics initiatives. We talked about: How Jason’s team keeps their data accessible and relevant to the issue they need to solve for their customer. How Jason strives to keep the information simple and clean for the customer. How does Jason help drive analytics in a company culture with a lot of legacy (from its people to its parts) The importance of focusing on context How Jason drives his team to be business partners, and not report generators Resources and Links: Jason Krantz on LinkedIn Quotes from Jason Krantz: "You realize that small quick wins are very effective because, at its core, it’s really important to get executive buy-in." "I’m a huge fan of simplicity. As analytics pros, we could very easily make very complex, very intricate models, and just, 'Oh, look at how smart we are.' It doesn’t help our customers. …we only use about two or three different visual types and we use mostly the exact same visual set-up. I can train a sales rep for probably five minutes on all of our reporting because if you understand one, you’re going to understand everything. That gets to the theme again of just simplicity. Don’t over complicate, keep it simple, keep it clean.” "…To get buy-in, you really got to have your business case, even to your internal customers, really dialed in. If you just bring them a bunch of crap, that’s how you’re going to lose credibility. They’re going to be like, “I don’t have the time to waste with you,” even though we’re trying to be helpful.” "What my team and I do is we really help companies weaponize their data assets." Episode Transcript Brian: Jason, are you there? Jason: I’m here my friend. Brian: Sweet. How’s it going? Jason: It’s going very well today. How’s your Friday going? Brian: I’m doing awesome. We’re going to talk a little bit about analytics. Is it Wile McLain or Weil McLain? Jason: I say Weil McLain. If I’ve been saying it wrong, I’ve been saying it wrong for a while. Brian: As I recall from my musical training, I think in German, the second syllable is the one that says its name. I guess it would be Wile McLain, like if it was W-I-E-L it would be ‘Weil.’ But I don’t know. Its anglicized as they come over the pond. Jason: I’m going to go with you on that when you sound like an expert. Brian: Nice. Well, you sound like an expert in analytics at Weil McLain. Tell us about what you’re doing over there. We met on LinkedIn, I’ve been enjoying your postings on the social feed about your approach. You seem really passionate about what you’re doing and I’m like, “I don’t know who this guy is, but that was really interesting.” I just have. Tell us about the company, what they do. I know they’re in heating, right? Jason: Yes, absolutely. The company I work for, and I work in the HVAC space, we’re a 135-year-old boiler manufacturer. Whether you realize it or not, you probably have one of our products in your house or building or very close to where you live. What my team and I do is we really help companies weaponize their data assets. As you know, a lot of companies are very skilled at acquiring data since the Big Data Movement. But the reality is that a lot of these companies don’t know what to do with all this data. That’s where we really come in. What I always tell my team and our business partners that we work with internally and externally is that our focus is on solving business problems. In order to do that, you have to identify what is the business problem that you’re trying to solve or strategic agenda that you’re trying to address. In order to do that, you really have to be anchored in the biz. Again, that’s just my perspective, but if you’re in the business day in, day out, you develop this very keen stand of what the business would need to accomplish its objectives. Just like right now, we are based in the marketing group and it’s a great spot to be. I’m a firm believer that every analytics team should be based in the business for a reason that I just talked about. But what that does being business-first is that gives us a great lens to look at data from. Sometimes analytics people would be IT-centric and they can do a lot of academic work against the data set or different data sets. But the business might look at the output and be like, “Yeah, that doesn’t help us.” We always, always, always start with, “What is the business problem we’re trying to solve or strategy we’re looking to address?” It also helps us when it comes to curating data also. That’s one of our primary response [00:03:21] this too, is to look for different data sets both internal and external that can help us identify strategic opportunities. It sounds really unsexy, I’m not going to lie. I think some of my LinkedIn post just say that data is boring. It really is. It’s mind-numbing, too, about 85% of my customers. But that’s the important part is understanding what do our customers need and that’s really the lens that we look at this through. We are a service provider, our customers are internal and external, we have customers just like any other business. We have to take this really boring, but really potent product in data and make it accessible to them. That’s really where we use design to really try to make that magic happen. Brian: I love that you said, “Trying to understand what the problem is.” This is something we talk about on the mailing list quite a bit. In fact, falling in love with the problem is a good basis for doing good work instead of kind of jumping to solutions or feeling […]. As I tell my clients sometimes like, “Our job is not to go and visualize the data. It’s not […] available for someone to put into another tool or whatever the heck it is. The job is to find an insight that already is used. Probably they’re already in your job and you’re there to make […] if you’re doing internal analytics. Help them do a better job at what they’re doing, offer more value. You need to figure out how to work that into their life.” For example, for you guys then, your customer, I assume is it primarily sales people that you’re working with? Who are your customers and your […]? Jason: Yes. Great question. One of our biggest customers is sales. Sales has been one of my biggest customers for the past 10 years of my career. I’m very intimately involved with the sales team, sales operation, sales optimization, insight gathering, pricing, things like that, but also marketing. We do a lot in terms of competitive intelligence gathering, market research. We also do a lot of operations in finance obviously related to the prices, that sort of thing. We really touch all areas of the business, but without question, our biggest customers are going to be sales and marketing. Brian: If you were to bring a new initiative like, “Hey, we have access to…” I don’t know what it might be but for you maybe your point, [might be a line 00:05:49] of data that could actually give them more leverage. We know what the negotiation brings, better than […], we know we kind of have an idea now from what the industry is doing for their sales such that we can now tell the CRM like, “This is your […] or something.” When do you get that sales person involved? Do you deliver a solution and get feedback? Do you bring [...] early and say, “Hey, we think we can tell you more about how to do better pricing on the spot with this thing.” Do you bring them in or when do they fit into your process? Jason: Great question. A lot of times because we spend so much time actually in the trenches, that’s one of things I think is unique about the way that I design my teams to do analytics. It’s not like hand off product and we’re like, “Godspeed. Good luck.” Once we deliver a solution, we’re actually in the trenches with the business trying to implement what we’re talking about because it just works better. The team work is just more effective and they know that they’ve got back up, they know they’ve got air support. Really, a lot of times when we come up with something new, a lot of times we will frame it from the lens like, “Hey, we know that we’ve got opportunity A or issue B, or whatever it is. This has been an issue or an opportunity for months or years or whatever.” We think that we’ve identified something that could help us in solving that issue or realizing the potential of that opportunity and then it becomes, “Okay, let’s sit down and talk about, do you agree that this might actually help us in this process?” Because the one thing that I’ve learned is, in order to get buy-in, you really, really got to have your business case, even to your internal customers, really dialed in. If you just bring them a bunch of crap, that’s how you’re going to lose credibility. They’re going to be like, “I don’t have the time to waste with you,” even though we’re trying to be helpful. What we found out is if you really dial in what are we trying to address with this, just as you would with any business case, and you bring that to them, I have found that they tend to be much more receptive. It’s not to say there’s not going to be resistance—resistance comes with any change—but we found that typically framing it from that lens and saying, “We’re trying to solve a problem that you have, we think that this data will help,” that’s a great starting point. Brian: Do you have an example of a before/after with that? I don’t want you to get into proprietary stuff you can’t talk about but is there like a, “Before they did it this way,” and then we brought them in and said, “Hey, we think we can get […].” and how you went [00:08:19]. Jason: Yeah. What I can talk about is just the manner in which we distribute sales information, specifically insights. I think that, for your listeners, this is going to ring true to a lot of sales forces. I know for all them that I’ve been in or worked with, this case was true 100% of the time. But one of the things that, again, keeping the customer-centric focus, that if you look at your sales reps, a lot of time is you’re going to be what I call casual data consumers. By that, I mean that these are guys and gals that aren’t really into data day in and day out like guys like you and I or some of the listeners maybe. What we have to do is, as I always encourage my team to take empathetic lens and look at, “Okay, if we give them what our first […] is going to look like, how are they going to interpret this?” A lot of times, to be honest, it’s not very good. Now that’s where we have to look at internally and kind of rationalize and say, “Okay, let’s find this. One of us will find [00:09:14].” But one example of that is traditionally, sales reps and sales teams will get the information in a flat Excel table. Just lots of rows and columns and just gibberish everywhere. That’s a very financial-centric view of sales data. But the reality is—I don’t know about the rest of mankind but I know for myself—I can’t remember much more than 10 numbers. The mental computational cost of extracting insights is just gargantuan. What happens is, I just don’t even bother to do it. I’m just like, “Yeah, whatever.” An equivalent of that is, you know if you get a big block of text in email? Even though if you took that same block of text and broke it up into two paragraphs or two sentence segments which is very easy to read when you put the effort in, but for me, if I get a big block of text, I’m not even going to read that. It’s kind of one of the same things that we see on the sales side. What we do is just say, “You know what? There’s a lot of really good information here and we need to make it digestible for our customers.” That’s where we found traditionally, visualization can be an incredibly effective tool to communicate insights to this casual data audience, to this casual data consumer. Brian: Do you have to work through the visuals with them? Do they tend to get it the first time? Is it a process of you share, “Here’s a report or here’s some new view on X.” How do you know if the visualization is actually allowing them to pull the insight out of what other [00:10:46] broad data? How do you know they’re actually “getting it”? Jason: That’s a great question. I’m a huge fan of simplicity. As analytics pros, we could very easily make very complex, very intricate models, and just, “Oh, look at how smart we are.” It doesn’t help our customers. It doesn’t help anything. Really what we do—this is going to get to the theme of simplicity—is we only use about two or three different visual types and we use mostly the exact same visual set-up. Just to kind of frame it, what I’m a big fan of is a simple bar chart. There’s more details attached to it but to the right of the bar chart, we’ll typically put a tabular data set. What we do is, as you think in US at least, we start in that left-hand side of the page or we […]. What we do is we look at the visual real estate. We say, “Our customers are going to start in the left-hand side. We want them to look at the bar chart because it allows them to very rapidly assimilate it at a high-level what’s going on.” It’s great at communicating at top-level churn very quickly but the trade-off is, is this horrible imprecision. You have no precision at all. What we like to do is then we address that issue by putting a simple table, very clean, very simple table over to the right. What that does is that then provides the precision that the customers are seeing in most financial-centric tables. What we found that does is that we have to train our sales team on one set-up and then that set-up is used virtually universally on all of our solutions. As an example, I can train a sales rep for probably five minutes on all of our reporting because if you understand one, you’re going to understand everything. That gets to the theme again of just simplicity. Don’t over complicate, keep it simple, keep it clean. Brian: I think those are good. A lot of times, when I work with engineering clients, they fall in love with consistency. I guess one point to maybe just the contrary of this is that, I think consistency is generally a good rule with design. We want to minimize unnecessary change but at the same time, I would recommend to listeners is to always look at context first, and context should always come in. Let’s say Jason comes up with report number 12 and they have 11 now or whatever, and it doesn’t feel right for number 11. That’s a place where a designer would probably push for, “Well, no. The 12th one actually needs to be different because it’s not […] 11th and even though it’s not consistent, in this context, we don’t need it to win. This version will deliver the usability and the utility that we’re looking for better than the other 11 will.” In general, I think it’s smart to not get creative unnecessarily with meaningless ink on the screen like, “Let’s try it this way. Let’s change the color palette. I’m tired of this.” Those are not good reasons for […], you’re just introducing noise and it’s unnecessary. But I like that you guys are thinking about simplicity and trying to reuse templates and not looking at it as a creative tableau. Ironically, people think it’s a creative “design” tool, but at the same time with all those weapons, you have a lot of different weapons you can use in that toolkit and part of that is knowing how to use this. It’s the same thing with Photoshop, a million buttons and all this stuff. The Photoshop doesn’t make you a designer. It’s being aware of your customer’s pain and the problems they need and knowing when to use all those filters and all those different things that it can do. I like that you guys are looking into that simplicity and reusing templates when it’s meaningful to do so. Jason: You bring up some great points and I 100% agree. My team that’s listening there, they’ll laugh because I beat it in their heads, “Context. Context, context, context.” Both in design as you’ve talked but especially with numbers in general. Like, “If I give you a number, a billion, that doesn’t mean anything, you got to have context.” I’d say the same is true for design just as you articulated. Great point. Brian: Where does the impetus for “everybody is a data company, everybody wants to do analytics”? But then there’s operationalizing that, there’s getting buy-in, leadership behind it. Where does that come from in your org? Where is the interest in taking a 130-year-old company and getting it to care about this? Where does that come from, your influence and all of that? Jason: That was driven by our current president because he saw it as part of a digital transformation. Obviously, this was an essential component of that. Obviously, we do a lot with analytics, but we’re also involved in a lot of other digital components that lead to that overall digital maturation. Analytics is a very, very big part of what we do but it’s not all that we do. We serve as kind of that quarterback for a lot of the digital initiatives to help basically, guide them through the process. Because even though some of the nuances of each of this project, each one will have its own nuances, they all come back to data. Data is the currency. We found out pretty quickly that if you want to stay relevant in this day and age, you need to be digitally evolved but more importantly, as you look at it, do you [compare the 00:16:02] advantage that you can derive from analytics? I would argue that gap is slowly closing known certain industries like manufacturing, but we probably have a little bit more runway [00:16:10] it. But for a lot of industries, analytics is becoming table stakes. It’s one of those where you can certainly expect incremental value and competitive advantage, but the question becomes how much longer. That was kind of the impetus of saying, “Hey, we got to get this going sooner rather than later.” Brian: Do you have people in sales that are resistant to using the reporting or taking advantage of your information or is it pretty ingrained in the company culture that it’s like, “This is a tool. Why would you not want to use it?” Or did you guys have a […] getting adoption? Jason: Yeah. I would say anytime you’re going through a transformation of this magnitude, it’s hard and I would say especially for other manufacturers. I found in general, manufacturing in general, tends to be one of the laggards industry-wise in analytical maturity. Unquestionably, it’s tough for no other reason than change is tough. You’re taking legacy plants, legacy steer pieces, legacy process, and some people has been around the company for decades potentially, and we’re asking them to change almost on a dime on their time scale how they do business. It’s not that it’s right or wrong but what we try to point out is that, as I always say, we have to acknowledge the past. We’ve been where we’ve been, we’ve been successful at where we been. But there’s been more change in the past two or three years than maybe you’ve seen in the past 15-20 years. In order to stay relevant, you really have to be ready to evolve, not only evolve but evolve quickly. But I have to openly acknowledge that that’s hard. It’s a hard proposition for a lot of people. Again, it comes down to change management and managing not only expectations but supporting that change. Change doesn’t happen by itself, we have to support that. That’s really what we try to coach through. The way that we try to do that is by developing a product with our customers. I’m sure as you can […], if you force something upon somebody, it doesn’t get received too well. But if you develop it in conjunction with them and do tie it around their needs, it tends to get better adaptation. Brian: You used the word product in there and I’m interested, do you see the outputs of your efforts? Primarily, it’s BI reporting as I understand it. Do you look at that as the product that you offer to sales? Is that kind of how you see it? Jason: Yeah. We offer a product in the form of the insight packages but it’s also the service. Service that goes with it where again, we serve as essentially internal consultant to help them along. If you take just the product-centric approach, you just deliver an insight package and you’re like, “Good luck. It’s [00:19:35]. Have at it.” What we do is we deliver the product and then we partner with them and say, “Okay, here’s what we see. Now, remember you’re talking about this going on in the channel last year and our note show that there’s been a lot of competitive activity in this area. Here’s some of the question that we have. You’re the expert, so what do you think?” What we found is that working together like that, we tend to get pretty good results versus just leaving these guys on an island to kind of figure it out themselves because they virtually always know the answer but sometimes it’s up to us using these products and then offering the service is to ask question that maybe aren’t getting asked. A lot of times, we find out that they know the answer it’s just that you kind of have to ask the question. Brian: Is that often like, “I was using XYZ report. Could you break this down by county instead of just by whatever because I feel there’s more people living in the East side of town and the average is here or […] the whole county. I really just need this one county because that’s where everyone lives. Is that really underserved? Blah, blah, blah,” that kind of stuff and then you guys will go off and work with them for more of that detail then maybe you release that back into the product as a feature if it seems like a one-off or something. Is that how it works? Jason: It’s actually a very fluid process. An example of what you just described is exactly what happens if hey come to us with questions. But we also do it where we flip it around because a lot of products that we create are more aggregate discussion tools. We don’t design a lot within our primary visualization package. To really get into the weeds and everything just becomes overwhelming. We have other tools like your traditional [00:21:22] pivot table to kind of dig into that stuff. But the exact example that you just gave, they will ask us those questions, but we will also flip the script and say, “Hey, we saw that the mechanical chain in the Northeast is up 50%,” I’m just making up a number, “and at a higher level, you can see that but when we segment it out, here’s what we see. Not only when we break this down to this level, we see that’s specifically being driven by A, B and C.” That gets to where I push heavier at my team to do root cause analysis. That’s really where we provide value is by digging into it and asking questions like that. Again, operating from the lens of trying to solve a problem or answer a question or root cause something in conjunction with the business. A lot of times, we will ask those questions and at the same time, they will ask us, which is great. It’s amazing because you get the better solution faster. Brian: I think that’s great. I’ve worked on several different tools that have varying sophisticated means of doing root cause analysis and I think it’s a really powerful way to bring some why to a what that has happened in the past. Most of the time, why is really where the money is at. The value comes in being able to understand why. A lot of times, we don’t have all the data. You can’t know for sure but a lot of times I tend to say, “Our guess, if they’re just going to make a WAG—a wild ass guess—then our guess, as long as we qualify what ingredients went into the pie, our guess may be better than any WAG.” They’re going to make one already. If they’re going to make a decision here and go off gut, there is maybe a chance they’re right and their experience will say something. But maybe our elementary root cause analysis, which we can improve over time, will actually be better and we can get out of the total guessing game and start with something that’s kind of a macro ballpark thing. Then overtime, you can improve that analysis as new data becomes available or maybe learn about how two variables are related in the business and you can bring that knowledge into the system. I totally hear what you’re saying. It’s a nice mix of internal product plus services and also, it sounds like it gets you guys do good discovery work as well. You guys are not just responding to questions but you’re maybe asking them questions together as a group. You kind of work through what opportunities maybe latent that no one’s talking about by asking questions using data to do that. Jason: Yeah. In the lens that we’ve been talking through, this is really sales-centric, but this applies to any group that we interact with. We have the same level of proactive discussion with any group that we interact with. In some of these, in our market research side, it’s 100% proactive. We’re going out there scouring for information and trying to see the other things that we see. That one it’s completely proactive and now we bring insights to the business and say, “What do you guys think?” The sales one is the most fun because, let’s be honest, there’s no business if you’re not selling anything and nothing happens until a sale is made. Brian: Right. I get that. You talked about other clients, do you work at all with the actual hardware, is there any IoT type of analytics going on with the boilers and machinery that you guys create? Jason: We’re early in that process. We actually are getting ready to go down that task very soon. On the hardware side, we tend to not have as much involvement. That’s really more on the engineering group. I think for any manufacturer product or engineering groups probably going to be the most involved in that. But obviously, we get involved into the discussions of answering the fundamental question. What are we actually going to do with this data when we collect it? Because as you can imagine, IoT can spit out a lot of data real quick. They can become incredibly burdensome very quickly if you don’t have a plan on how to manage it. But then, if you’re going to go through the effort of managing that, you got to be able to say, “What are we going to do with this?” Brian: Yeah. I guess the first thing that would come to mind for me would be predictive maintenance, like, “Is it going to break down soon?” I worked on a cooling company that does cooling and really as the guy told me is like, “We’re not selling refrigeration. We’re selling consistent temperature to our clients. It’s not really about coolers and all of that, so we need to deliver consistent temperature. If we don’t do that, they lose products, they can lose whatever is being stored in cold storage.” That is significant business. I’m sure for you guys, it’s heat, you want to sell heat so how do you get in front if there’s a maintenance plan or whatever, how do you stay on top of that kind of stuff? Jason: Absolutely. Brian: [00:26:13] IoT. One of my clients used this word one time, which I now use all the time which is like, “We don’t want a metrics toilet.” An example of you can get to a metrics toilet really quickly with every stat under the gun and how many ounces of water per minute through this pipe, that’s great because that’ll help me do, as a sales guy or as a technician, how am I going to use that information just because there’s a sensor on that pipe. It’s working something around like, “Oh, there’s a sensor. Put the data in the grid.” Jason: I’m going to have to borrow that. I’ll give credit whenever I use ‘metrics toilet,’ that’s a pretty good one. I may actually [00:26:56]. Brian: Nice. Tell me, where does it go from here? You had mentioned like, “Oh, the competitive edge, maybe it’s closing.” Or maybe you guys feel your competitors are all kind of maybe they’re doing the same thing that you guys are doing and we are all aware of where the data can be used to drive the business. Are there other places where you see design or technology like predictive analytics or machine learning and some of these other new technologies that are out there to help drive predictions and things like that? Are you guys leveraging any of that or have plans to look to the future? What does that look like? I know you probably can’t talk about everything but maybe broadly. Jason: Absolutely. I would say that that’s content that’s definitely, if it’s not already being done then it’s on our radar. We’ve got a pretty talented team here that goes a lot of your traditional data science turf. As you can probably surmise in this conversation, is in addition to having all skills, we’re probably the most heavily focused on the business side. As we say, we explore opportunities for a lot of this. We always look at it, again, like machine learning. Great, but we got to make sure it’s very powerful stuff. We got to make sure that whatever we’re embarking upon, because we have finite work capacity, if you pursue something, machine learning, it means we’re not doing something else. It’s not to say that it’s not important, but we really have to be able to answer to that question. Again, come back to, “This is our anchor. What are we going to do with it?” I love this stuff. I love the stats. I love machine learning, AI, all that stuff. If you’re not careful, you can really quickly get into an academic exercise that we think is really cool. “Oh wow, look at this. We’ve got this awesome algorithm here. It does all this magical stuff,” and then the business looks at it and goes, “Yeah, so what? I don’t care. How does that generate revenue? How does that improve our margins? How does that reduce our cost? How does that enable to build the sales pipeline?” If we can’t answer those base questions and we don’t get alignment, that’s probably the most important thing is executive buy-in on exactly what we’re going to be working on, why it’s important. No, we don’t pursue it but those things are most definitely, as with any analytics teams today, I think that that content is definitely being done and/or on your radar. Brian: You make a really good point. Sometimes I almost hesitate to ask the question. But I think it’s an exciting space in terms of predictive capability and removing viable analysis and what we call time tool time in the design world, there is there. But at the same time, you make a really good point which is again, these are tools that need to be leveraged to service an opportunity or a problem. The goal is not to go do the machine learning, the goal is to solve a business problem by which machine learning maybe applied a better […] do it, reduce cost or reduce effort, speed, something like that. I completely respect that. I’m glad to hear that you guy are looking that as not a leading step. I know there’s conflicting signals out there. I’ve been talking to people in the International Institute for Analytics about this and at the same time you hear a lot of stuff which is, “If AI is not part of your strategy, you’re going to be missing out,” and boards just want to hear that people are doing AI. At the same time, you’ve got academic exercises going on, you’ve got people trying to take on massive like, “We’re going to shoot for the moon,” and it’s like, “You don’t even have an airplane and you’re trying to go to the moon with this thing. Show us a small win if you’re going to do an investment in AI.” It’s okay to go try it out and say, “Let’s do a small thing but let’s try to solve a business problem or have some definable output that we’re looking to do here such that we’re not just writing code and doing experiments.” I hear there’s a problem with people putting this on their resume. It’s like people just want to have machine learning. Everyone’s a data scientist now that used to stay in analytics. [00:30:47] It’s scary in the sense of just wasting opportunity and wasting money because at some point, your smarter competitors are going to be saying, “This is a new hammer. Let’s find some nails that we can use for it. But we think […] right nails and it needs to be the right application before we whack at it. It’s not just […].” Jason: I really like your point because again, if my peers were listening to this they will laugh because they say, “We are professionals of this trade and the tools that we might want to use might not be the right tool to use for a specific job.” I couldn’t agree more with that sentiment. It’s one of those core philosophies that I have and share with my team. Also to it is with the AI. I think that you truly made a very astute observation here and comment in that, I think a lot of companies do feel compelled to have to make significant investment in AI like today. It’s not to say that there’s not merit. There clearly is plenty of merit and plenty of potential there, but kind of your point, I really believe that it’s much more beneficial when you really minimize the risk of project and budget flow and minimize overall project risk. You take that small bite and try a little bit, then try a little bit more. When you get to win, socialize the win, and your executives feel comfortable because I’ve done it on the analytics side. I went for a big bang approach and after nine months they were like, “Hey, man. Where’s the output?” All you need is to get bit by that once and then you realize that small quick wins are very effective because at its core, it’s really important to get executive buy-in. A lot of executives are not willing to wait nine months or a year for something when they’re expecting to see at three months. I totally agree with your sentiment. Brian: When you talked about the wins, I totally understand if you’re close to it and maybe hard to remember those, but is there a particular story or time where something in the product and the insights that you guys put out to your customers that it was like a real win, like a sales guy said something to you or maybe an executive said something to you about how this moved the needle, like this was a memorable moment for us. Like, “I changed a customer’s mind with this,” or, “We closed the sale that we never would have been looking over here if we didn’t do it.” Do you have any anecdotes like that that you can share? Jason: One that we had recently, again, just for confidentiality purposes I can’t get too deep. Brian: Sure. Jason: We did have one recently where we just basically revamped our insights packages that we distribute to our internal team. We really, really gathered feedback. We had version one, we gathered ton of feedback, kind of refined, iterated, got the feedback without making it a major release. Got feedback, refined it, refined it, and then what we did was, with a small group, we got that beta in their hand, they look at it and they’re like, “This is great. This is exactly what we need.” Because what we were doing, what we found was—I’m sure you’ve experienced this—everybody wants their own part of things. Everybody wants certain view of a report or they want certain insights or whatever it is, and it’s great. But if you have limited resources, really high-powered resources like an analytics team or data science team, you’re going to look at the opportunity cost of trying to do one of these one-offs, we were getting a ton of report flow. Again, what I tell my team, I don’t mean to be derogatory to the DI guys in this comment, but my team’s side, I always tell them, “We don’t create value if we’re just creating reports. We create value when we’re actually partnering with business to extract insights, identify opportunities amidst all that stuff that goes well with it.” What we realized though is that, what started out as a nice, clean, three- or four-page insights package and blow it up to like 20 and [34:21] doesn’t that meet our original criteria? Essentially, what we do is once we have the rationalization enough to say, “Okay, we’ve got all these stuffs right across 20 pages. We can actually distill it down to four pages.” It will give you the exact same information, but it might not look the exact way that you wanted it to look. The question becomes, are you willing to deal with less stuff and maybe have it look a little different, but you’ll get it in a much more concise package that you’re actually able to use and process? What we found out is that a lot of people were doing these packages and getting the reports that they want but they weren’t actually using them to drive decision-making because they can’t see the paragraph or the block of text story before. They look at it and they’re like, “I don’t know what the hell to do with this.” We would dial that in and it just been a screaming success. It’s really nice to have it where something like that you see the evolution of it. This is just one of those things that we had, and this was kind of a side package or wasn’t a primary, but it’s become a primary now because it’s so effective. Brian: Would you say that when you talk about reducing this, is the report like a PDF or do they access it through a browser the insights package? Jason: Yeah, we have the options to do both. We distribute it initially via PDF, sometimes along with our comments if there’s really, really big stuff in there. We’ll say, “Hey, we see this. Here’s a driver. Here’s a supplemental package.” A lot of times it’s PDF first and then if they want to go on the web, start interacting with it, they can do that. Those are nice, but the reality is a lot of them don’t do that which is understandable. Brian: You took it from 20 pages down to 4, is that what you’re saying? Jason: Yeah. Same information. Brian: This is a really good point. I’ve frequently had clients come in and they’re with data products and their concern is information overload. We’ve heard this a lot of times and the irony is that, the issue is usually not information overload. It’s usually a design problem that the information is not presented properly because sometimes, it can increase the density and increase the utility and usability, not the other way around. In fact, removing data can actually make it worse. A basic example of that is when you’re trying to compare A and B. If A and B are not on the same, what we call a viewport like in a browser world, it would be within the browser window there. When you require someone to toggle between two screens, they have to change context and visually, your eye can process the information a lot better when it’s within proximity. Sometimes, increasing the density actually will give you a better design. It takes more care in how you do it, but it’s not always about information overload, “Oh, it’s too crazy.” They may not get it on the first time but your sales people, if they’re looking at this stuff weekly or monthly, at some point they’re going to be pretty comfortable with this. I always tell my clients, “You need to look at the switch frequency as well because if it’s going to be used a lot, you can actually get more detailed and you can really push the, what you might see as complexity or the information density, can go up because they’re going to get familiar with the formatting. Typically, the density is actually going to probably improve the utility as long as care is given to the choices. But having that eyeball comparison without having to change pages and all of that, typically you’re going to give a better story as a broad rule. I like hearing that you guys went down in page count, up in density and in turn a better user experience at the end so that’s great. I think we’re about done here. I don’t have too many questions for you, but this is super great. One of the reasons I contacted Jason is because I remember seeing this quote, “Jason is like a category five hurricane in the data analytics world.” I’m like, “Who the hell is this guy? No one talks like that.” I started reading your stuff and I enjoyed watching your LinkedIn social posts and things like that. Where can people find out more about you? You’re obviously on LinkedIn, I can put LinkedIn in the show notes and stuff, but are you on Twitter, any social media places they can follow you? Jason: No, actually, I’m not on Twitter. But the best place unquestionably is going to be LinkedIn. I’m pretty involved there. I do like to engage. If you want to direct message me with questions, just talk, meetup, connect, whatever it is, I welcome that. I love the platform, it’s a great family. I just really started using it maybe nine months ago, really getting into it. It’s been great meeting guys like yourself. It’s actually phenomenal. Brian: Cool. I’ll put a link to Jason’s LinkedIn profile on there and you guys can find him. I recommend, especially if you’re in an internal analytics type of role at your company, to follow Jason and then check out what he has to say on there. This has been great. Thanks for coming on the show. I look forward to meeting you at some point in person. Jason: Dude, thank you for having me on here. I really appreciate it. We hope you enjoyed this episode of Experiencing Data with Brian O’Neill. If you did enjoy it, please consider sharing it with #experiencingdata. To get future podcast updates or to subscribe to Brian’s mailing list where he shares his insights on designing valuable enterprise data products and applications, visit designingforanalytics.com/podcast. Never forget to look up the online HTML CheatSheet when you forget how to write an image, a table or an iframe or any other tag in HTML! [bws_google_captcha] Subscribe for Podcast Updates Get updates on new episodes of Experiencing Data plus my occasional insights on design and UX for custom enterprise data products and apps. Email Address [text-blocks id="eu-consent-checkbox-textblock" plain="1"]
You’ve just started a new job as the Analytics/Insights manager. You’re coming into a position where an analytics solution was designed and implemented by one of your predecessors. What are the critical first steps that you take now that you own it? This week, Jim, Jon, and Jason sit down to discuss what they see as the important items to tackle first along with the common missteps with this kind of scenario. Website: www.33sticks.com Email: Podcast@33sticks.com Twitter: https://twitter.com/33Sticks Facebook: https://www.facebook.com/33sticks/
Revenue Generator Podcast: Sales + Marketing + Product + Customer Success = Revenue Growth
Sponsored By Knit. Click to get your free consultation today. Welcome to Career Day on the MarTech Podcast. Today we're going to learn about the journey of a great marketer who started his career as an engineer. Joining us today is Michael Lukich, who recently took a role as Executive Leader of Marketing and Analytics Insights at Booz Allen Hamilton, which is a management technology and engineering consulting firm that provides services to leading fortune 500 companies, governments, and large nonprofits. Prior to his role at Booz, Michael worked with consumer brands including GE, Marriott, and Total Wine. He is also an adjunct professor of Digital Analytic Measurement at Georgetown. Episode Transcript Connect with: Michael Lukich: Website // LinkedIn The MarTech Podcast: Email // LinkedIn // Twitter Benjamin Shapiro: Website // LinkedIn// Twitter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Sponsored By Knit. Click to get your free consultation today. Welcome to Career Day on the MarTech Podcast. Today we're going to learn about the journey of a great marketer who started his career as an engineer. Joining us today is Michael Lukich, who recently took a role as Executive Leader of Marketing and Analytics Insights at Booz Allen Hamilton, which is a management technology and engineering consulting firm that provides services to leading fortune 500 companies, governments, and large nonprofits. Prior to his role at Booz, Michael worked with consumer brands including GE, Marriott, and Total Wine. He is also an adjunct professor of Digital Analytic Measurement at Georgetown. Episode Transcript Connect with: Michael Lukich: Website // LinkedIn The MarTech Podcast: Email // LinkedIn // Twitter Benjamin Shapiro: Website // LinkedIn// Twitter
Episode 10 of The Redirect Podcast: Details on live tests of new Google “see more” in mobile SERPs, and Analytics Intelligence rolls out inside Google Analytics with machine learning, powering Q&A for data gathering. Tips and methodologies for optimizing old blog posts, Google for Jobs is live, and perspective on the new AdWords redesign. Show notes at https://blacktruckmedia.com/podcast/redirect-podcast-episode-10/
The role of delivery is clear: it must earn sales the right to ask for more business. This means it must deliver to scope, to budget and on time, in others words, with excellence. How can you set yourself up to make delivery with excellence the norm and not the exception? Robbin Steif - Founder and CEO of Lunametrics Lunametrics is a Pittsburgh PA based Digital Marketing and Analytics/Insights agency and a Google Premier Partner. Like many entrepreneurs, Robbin has been an overachiever from the start, with not one but two degrees from Harvard. She is a recipient of the BusinessWomen First award, the Diamond Award for Business Leadership, and was a member of the Board of Directors of the Digital Analytics Association. Host: Alex Langshur
Establishing connectivity between devices and the cloud is the first step to building great IoT applications. Generating insights with these data streams, and acting on it in real-time, that creates value for your business. Join us and learn how to expand the operational picture of your IoT solution with AWS IoT. We will show you how to use metrics to enable new possibilities in generating insights and decision making engines. And we will connect to Salesforce to leverage your CRM data and empower service agents in this IoT context, using a solar panel monitoring and maintenance as a demonstration of AWS IoT features paired with Salesforce IoT Cloud.
Tim Ash speaks with the popular Google Analytics blogger about common analytics mistakes, the three essentials for doing analytics correctly, and even gets Simo to perform on his ukulele live!
Holiday season is fast approaching and maketers and analytics professionals are getting ready to take advantage of the consumer buying spree. Are you and your company ready to increase the average order value, conversion and bottom line revenue? Jump on this informative podacst to learn how you can prepare for the holiday season.
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