Podcasts about Personalization

Marketing leaders are being asked to drive more growth with less budget, fewer resources, tighter timelines, and more pressure from every direction while AI is being treated like the shortcut to replace entire marketing teams. But AI will not fix bad strategy, weak alignment, poor customer understanding, or broken marketing fundamentals. In part two of this master class conversation with Matt Hummel, CMO of Pipeline360, the focus moves into what it really takes to become the kind of CMO AI cannot replace. Not by chasing every new tool, adding more MarTech, or hiding behind automation, but by understanding the business as a whole, building trust across departments, speaking the language of revenue, and creating alignment between marketing, sales, product, leadership, and the customer. To lead marketing in a volatile market where expectations keep rising and the old playbook is no longer enough, you need to know how to: • Make sales an ally instead of your bitter rival • Build shared pipeline ownership across marketing and sales  • Communicate risk without becoming defensive • Connect marketing decisions to the larger goals of the business • Set clearer expectations with your team and leadership • Understand resource constraints without using them as excuses • Stay close to customers while leading strategy • Create momentum without pretending there is an easy button The best marketing leaders are not just managing campaigns, tools, reports, and dashboards. They are translating complexity into strategy the business can trust. The reminder is clear: AI will not fix bad strategy. More MarTech will not fix bad marketing. The CMO AI cannot replace is the one who understands the business, earns trust, aligns with sales, leads the team, knows the customer, and gets back to real marketing when everyone else is hiding behind tools. (P.S. If you haven't, listen to Ep. 149 for part one of this masterclass episode) Beyond The Episode Gems: Connect With Matt Hummel on LinkedIn Listen To Troy On Matt's Podcast, Pipeline Brew: The Evolving Role of CMOs & Community Building Visit Pipeline360 website to learn more about how they solve B2B marketers' biggest headaches Buy Troy's Book, Strategize Up: The Blueprint To Scale Your Business StrategizeUpBook.com Discover All Podcasts On The HubSpot Podcast Network Get Free HubSpot Marketing Tools To Help You Grow Your Business Grow Your Business Faster Using HubSpot's CRM Platform Support The Podcast & Connect With Troy:  Rate & Review iDigress: iDigress.fm/Reviews Follow Troy's Socials @FindTroy: LinkedIn, Instagram, Threads, TikTok Subscribe to Troy's YouTube Channel For Strategy Videos & See Masterclass Episodes Need Growth Strategy, A Keynote Speaker, Or Want To Sponsor The Podcast? Go To FindTroy.com  

Unlock what real luxury really means in short‑term rentals with hospitality and branding expert Katie Cline, former PR lead for brands like Ritz-Carlton, W Hotels, and St. Regis. In this episode of the podcast, we dive deep into how female real estate investors can elevate their STR, MTR, and LTR portfolios through hospitality, design, and guest experience—without overspending. In this episode, we cover: How Katie went from UK long-term landlord to profitable US short‑term rental investor Why hospitality is a non‑negotiable skill for real estate investors (even for long- and mid‑term rentals) The “art of the arrival” and how the first 10 minutes shape reviews, referrals, and repeat bookings Practical luxury: the small details that feel high‑end (think: linens, hair dryers, cookware, amenities) Smart policies for bachelorette groups and high‑energy stays that still protect your asset Personalization strategies that help you stand out on Airbnb and Vrbo Branding your property: naming, storytelling, and designing for your actual ideal guest If you're a female real estate investor building wealth with short‑term rentals, vacation homes, or mid‑term rentals, this conversation will show you how to think like a luxury hotelier while staying profitable.     Resources: Book your spot at WIIRE Summer Camp before it fills up Check out what Katie is up to next on Instagram  Listen to Katie's podcast Get the rest of the deets on Katie's ventures Simplify how you manage your rentals with TurboTenant Make sure your name is on the list to secure your spot in The WIIRE Community  Leave us a review on Apple Podcasts Leave us a review on Spotify Connect with us on Instagram

In this episode, we dive into how modern e-commerce brands are scaling faster and reducing risk using print-on-demand and AI-powered tools.David Hooker, Head of Brand at Printify, shares how the platform helps business owners launch global stores without buying upfront inventory or managing messy shipping logistics.He also reveals how AI is automating boring tasks like product tagging, creating photorealistic mockups in seconds, and helping sellers target the perfect niche audience. Topics discussed in this episode:  How AI automates repetitive store setup tasks. What print-on-demand means for modern inventory. Why photorealistic AI mockups save massive photography costs. How global fulfillment networks enable rapid local shipping.What traits separate successful sellers from failed stores.Why gathering direct audience feedback drives brand growth.How to launch an online business without risk.What custom products the rising Gen Z audience demands.Why text-based t-shirts often become massive bestsellers.How print-on-demand offers true location and time freedom. Links & ResourcesWebsite: https://printify.com/LinkedIn: https://www.linkedin.com/company/printify/LinkedIn: https://uk.linkedin.com/in/hookerdjInstagram: https://www.instagram.com/printify/X/Twitter: https://x.com/printifyGet access to more free resources by visiting the show notes at https://tinyurl.com/yd85xumbI'd love your feedback. Tap the the link to send me a text. ______________________________________________________LOVE THE SHOW? HERE ARE THE NEXT STEPS!Follow the podcast to get every bonus episode. Tap follow now and don't miss out!   Rate & Review: Help others discover the show by rating the show on Apple Podcasts at https://tinyurl.com/ecb-apple-podcasts   Join our Free Newsletter: https://newsletter.ecommercecoffeebreak.com/   Support The Show On Patreon: https://www.patreon.com/EcommerceCoffeeBreak   Partner with us: https://ecommercecoffeebreak.com/partner-with-us/

Personalization has become one of the most celebrated leadership skills of our time.But there's a version of personalization that quietly exhausts even the most capable leaders.In this episode of the Earning Conviction Series, Glenn Llopis explores the hidden danger of adapting so well to others that you eventually lose touch with yourself.

AI is shifting ecommerce from manual, broad marketing to fully autonomous, hyper-personalized systems…and if you don't get onboard soon, you could be left behind. Liam Millward, co-founder and CEO at Instant, returns to Limited Supply for a deep dive into how AI is completely reshaping ecommerce, retention marketing, and the way modern brands operate. Nik and Liam break down how brands are moving from generic flows and static campaigns to fully dynamic, individualized experiences where every shopper receives a unique email, message, and buying journey. They also discuss: Why AI-first brands are moving faster than legacy companies The evolution from ChatGPT interfaces to autonomous agents How retention marketing is becoming as important as acquisition This episode is packed with practical insights for founders, marketers, ecommerce operators, and anyone trying to understand where AI is taking business next.--- What's Instant? It's the secret weapon to triple your email revenue with AI-powered flows and campaigns. Instead of sending the same cart reminders to everyone, Instant gives every shopper a personalized email experience: Copy, products, and offers that adapt to your shopper's behavior and purchase history in real time. Emails sent at the exact moment each shopper is most likely to buy. 11+ abandonment flows and smart multi-step campaigns live in minutes. Built for DTC marketers. Made for revenue growth. See why brands are replacing their ESP with Instant: ⁠⁠⁠⁠instant.one/sharma⁠⁠⁠⁠. --- Want more DTC advice? Check out the⁠⁠⁠⁠ Limited Supply YouTube page⁠⁠⁠⁠ for more insider tips. And if you're looking for an instant stream of on-demand DTC gold, check out the⁠⁠⁠⁠⁠ Limited Supply Slack Channel⁠⁠⁠⁠⁠ for Nik's most unfiltered, uncensored thoughts. Check out the Nik's ⁠⁠⁠⁠⁠DTC newsletter⁠⁠⁠⁠⁠ Follow Nik on Twitter:⁠⁠⁠⁠⁠ https://www.twitter.com/mrsharma⁠⁠⁠

HELP US IMPROVE THE PODCAST - TAKE THIS 3 MIN SURVEY:https://forms.gle/fRTV2YiJqncKVpFh7WEBINAR LINK:https://shawnmoore.clickfunnels.com/optiniyvvg89sWant to learn more about Vodyssey or start your STR journey. Book a call here:https://meetings.hubspot.com/vodysseystrategysession/booknow?utm_source=vodysseycom&uuid=80fb7859-b8f4-40d1-a31d-15a5caa687b7FOLLOW US:https://www.instagram.com/vodysseyshawnmoorehttps://www.facebook.com/vodysseyshawnmoore/https://www.linkedin.com/company/str-financial-freedomhttps://www.tiktok.com/@vodysseyshawnmooreCONTACT US:support@vodyssey.comSOURCES:1) https://www.rentalscaleup.com/airbnb-ai-strategy-2026-summer-release/2) https://techcrunch.com/2026/05/20/airbnb-gets-into-hotels-expands-ai-for-host-onboarding-and-customer-support/3) https://thenextweb.com/news/airbnb-is-adding-hotels-car-rentals-and-luggage-storage-as-it-evolves-from-a-home-sharing-app-into-a-full-travel-platformPROPERTIES:https://www.airbnb.com/rooms/1632746088889966533?unique_share_id=2a1aa537-4be3-432b-a4f6-170610a889a8&viralityEntryPoint=1&s=76&source_impression_id=p3_1779824102_P3NeOUcAUTZ5ElWcChapters00:00:00 Intro00:00:29 Recap of Airbnb's Summer Update and Focus on AI00:01:25 AI Listing Creation and Personalization in Airbnb00:03:02 The Role of AI in Differentiating Professional Hosts00:04:23 Changes in Review Processes and Guest Experience00:06:01 Airbnb Leveling the Playing Field for Mom-and-Pop Hosts00:07:11 The Commoditization of Listings and Differentiation Strategies00:08:37 Airbnb's Focus on Experience Over Price00:09:59 Impact of AI on Property Differentiation and Reviews00:11:25 The Future of Reviews and Guest Feedback00:12:24 Market Positioning and the Bell Curve of Property Quality00:14:23 Expansion of Airbnb to Hotels and Experiences00:15:44 Supply and Entry Barriers in the Market00:16:51 Competitive Dynamics with Hotels and Boutique Properties00:17:23 The Validation Age and Risks of AI Reliance00:19:43 The Importance of Data Validation and Critical Analysis00:21:57 Challenges of AI Hallucinations and Misinformation00:23:37 The Impact of Rising Costs on Furniture and Supplies00:36:39 Rising Raw Material and Fuel Costs in Furniture Industry00:41:00 Effects of Fuel Prices on Freight and Delivery Delays00:43:48 The New Normal: Higher Costs and Market Adaptation00:45:52 Market Outlook and Strategic Adjustments00:47:12 Celebrating Success Stories and Peak Season Preparation00:48:31 The Importance of Realistic Expectations and Numbers00:50:42 Balancing Emotional and Logical Marketing Strategies00:53:07 The Role of Hard Work and Validation in Success00:55:04 Final Thoughts and Call to Action

In this episode, we sit down with Samantha Lander, Functional Diagnostic Nutrition practitioner and founder of See Fit Living, to explore her powerful journey from addiction, PCOS, and chronic fatigue to becoming a leader in precision health and performance optimization. Samantha shares the emotional and nervous system roots of addiction, the lessons she learned through recovery, and how those experiences shaped her approach to healing the whole person. We also dive into biological dentistry, the role of peptides in supporting recovery and resilience, and her favorite peptide tools for optimizing health and longevity. Samantha breaks down the differences between GLP-1 and emerging GLP-3 therapies, discusses the importance of personalization through HRV and wearable technology, and offers practical guidance on peptides. Finally, she highlights the risks of purchasing peptides online and provides essential tips for sourcing high-quality products safely and responsibly.Samantha Lander is a Functional Diagnostic Nutrition (FDN) practitioner and health coach who specializes in high-performance precision health. As the founder of See Fit Living, she moves beyond standard care to help clients decode their "whole health story" through advanced functional lab testing—identifying the root cause of systemic "glitches" like hormonal redlining, gut dysbiosis, and metabolic stalling.Samantha's clinical expertise is rooted in a remarkable personal history of overcoming complex health challenges, including addiction, PCOS, and heavy metal toxicity. Today, she is a leader in integrating cutting-edge protocols—utilizing peptides and bioregulators to optimize the body's biological hardware. When she isn't polishing clinical masterminds for healthcare clinicians, Samantha is a professional DJ and keynote speaker who brings "Electric Movement" to health and wellness conferences across the country. She is dedicated to helping high-achievers fix their terrain and reclaim their biological edge.SHOW NOTES:0:39 Welcome to the show!2:49 About Samantha Lander3:30 Welcome her to the podcast!4:29 The emotional underpinnings of addiction8:15 Getting to her rock bottom11:55 The through line of all addictions 13:05 Nervous system & addiction15:27 Her womb healer16:50 The 12 Steps18:29 Her greatest biohack22:20 The silver lining of addiction23:37 Her history of tooth trauma26:12 Finding biological dentistry31:19 *APOLLO NEURO*33:32 How she discovered peptides36:19 How peptides support addiction 39:01 GLP-1 vs GLP-342:47 Her favorite healing peptides44:06 Personalization with HRV & wearables47:45 Her words of caution + advice51:30 If you want a growth hormone peptide53:29 Dangers of buying online55:12 How to source safely56:47 Where to find her59:35 Her final piece of advice1:00:16 Thanks for tuning in!RESOURCES:Apollo Neuro - code: BIOHACKERBABES for $90 off + Smart Vibes bundleSee Fit Living WebsiteInstagram: @seefitlivingSupport this podcast at — https://redcircle.com/biohacker-babes-podcast/donationsAdvertising Inquiries: https://redcircle.com/brands

Send us Fan MailBeth and Lisa talk with literary agent Nikki Carrero, from The Rights Factory, to break down the querying process from the agent's side — what gets you noticed, what gets a pass, and how to make the most of The Call. Highlights•       Nikki runs monthly pitch parties on Threads — currently her only open submission path since she's closed to general queries.•       Biggest query red flags: word count outside genre norms, miscategorized genre, and sending to agents who don't rep your category.•       Personalization? Just use her name. “Dear Agent” won't auto-reject you, but do double-check pronouns.•       Multiple passes don't always mean bad writing — it may be wrong agents, weak hook, or pacing issues in the opening pages.•       Nikki is an editorial agent: developmental notes, line edits, and reader reactions all in the margins.•       Watch your social media. Agents notice public venting in the query trenches — keep it in private group chats. Questions to Ask on The Call (with prospective agent!)•       How editorial are you, and what does your revision process look like?•       Will I see the pitch letter and submission list?•       What happens if the book doesn't sell — do you stay with me?•       Always ask for a boilerplate contract before signing. Nikki's Parting AdvicePatience and persistence. Self-publishing is not a fallback. Keep writing new books — voice and craft develop over time, and the writers who stick with it are the ones who break through. Nikki Carerro SubstackNikki Carerro Threads Support the show Visit the WebsiteFind Full Episodes on YouTube!Writers with Wrinkles Link Tree for socials and more!

Send us Fan MailGoogle just revealed the future of Search at Google I/O and Google Marketing Live — and it's far more personal, predictive, and invasive than most people realize.In this episode of Near Memo, Mike Blumenthal and Greg Sterling break down:A massive fake Google Business Profile fraud case involving 15,000 fake listings and $79 million in alleged fraudHow Google already infers your income, politics, preferences, and buying habits — even WITHOUT opting into “Personal Intelligence”The rise of AI-native advertising formatsWhy AI Mode may fundamentally reshape search, local discovery, Google Maps, and online commerceHow Google's ecosystem strategy could create unprecedented user lock-inThey also discuss:AI-powered personalizationGoogle's “good enough” AI strategyAI agents and transactional searchWhy antitrust rulings may have changed nothingThe future of ads inside AI search experiencesSubscribe to our newsletters and other content at https://www.nearmedia.co/subscribe/

AI isn't just another marketing tool—it's the operating system of modern marketing. In 2026, the businesses that win aren't the ones “using AI”—they're the ones building on it. From answer engines to agentic AI and lightning-fast speed-to-lead, this episode shows you what's changed, what matters now, and how to take action—without getting lost in the hype.

Most loyalty programs are designed around rewards: points and discounts that give customers a reason to return. That design produces a specific problem. When the incentive expires or another brand matches it, the customer accepts the offer and does not return. The companies that retain customers year over year treat loyalty as a relationship, not a transaction. Most companies have not made that shift yet. In this episode of Doing CX Right℠, Stacy Sherman examines that problem with Martin Villanueva, Global Head of Personalization and Loyalty at IKEA. They explain why most loyalty budgets stay underfunded, what that costs in long-term revenue, and the specific steps leaders need to take first. You will learn how to: Position loyalty as a growth engine, not a cost center, when making the financial case to the C-suite Use customer journey mapping to align CX, support, personalization, and marketing teams around a single experience Design a value exchange that gives customers a clear reason to share their data Apply AI to personalization in a way that increases relevance, not just message volume Measure customer loyalty through repeat purchase rate, active member rate, CLTV, and whether customers are sharing more data over time as a signal that they believe the brand delivers value Final Thoughts Customer acquisition costs rise every year. A loyal customer base reduces dependence on that spending. The leaders who make that investment first hold an advantage that competitors cannot easily replicate. Have a question or thoughts to share? Leave a voice message: https://www.speakpipe.com/StacySherman Learn more at DoingCXRight.com and subscribe to the newsletter for more actionable strategies.

Personalization is still reactive, and that is why it stopped working. In this episode of Content Amplified, Katie Carroll, VP of Product Strategy at Businessolver, makes the case for moving past variable tags and behavioral triggers into anticipation: helping people before they know what to ask. Katie walks through findings from Businessolver's eighth annual Benefits Insights Report, including the counterintuitive idea that "quiet" (no clicks, no engagement, no support tickets) might be the real success metric, and how an in-house AI hit 91% instant resolution by reading the path a user is already on. She uses concrete examples (an HSA nudge after a pediatrician visit, an auto-enrollment in a prescription management program, a Social Determinants of Health lookup that connects a parent to childcare) to show what anticipation looks like in practice. She also explains why AI SDRs flopped, why marketers have to lean hard into data analytics in 2026, and why the easiest brand to interact with is the one that wins. If you want a practical starting point for building anticipation into your marketing, this one is for you.About KatieKatie Carroll is the VP of Product Strategy at Businessolver, a benefits administration tech company that powers the platform employees use to enroll in their benefits. She has spent her whole career in tech, starting on the consumer side at companies like eBay before moving into B2B, which gives her a rare cross-pollinated view of how people actually want to interact with software. Katie sees the healthcare and benefits space as a personal mission, drawing on the universal frustration most Americans have with the system to push her team toward more anticipatory, helpful user experiences.Show Notes- Connect with Katie on LinkedIn: https://www.linkedin.com/in/katie--carroll/- Businessolver Benefits Insights Report: https://businessolver.com/benefits-insights/Text us what you think about this episode!

At Marketecture Live, Brad Fox, SVP, Health Media, dentsuX, joins Josh Walsh, Co-Founder & CEO, BranchLab, and Zach Rodgers, Founder, Sensical, to explore how AI and neural networks are reshaping healthcare advertising. From the rise of AI-driven patient behavior to the decline of traditional targeting methods, the conversation dives into privacy-safe audience strategies, evolving patient journeys, and what the future holds for pharma marketers in an AI-first world. Takeaways AI is rapidly changing how patients search for and consume health information Healthcare advertising is shifting away from search, contextual, and retargeting Neural networks enable privacy-safe prediction of patient populations Audience targeting must be rebuilt using AI-driven models Patient journeys are complex and require more nuanced segmentation AI is making both patients and doctors more informed True one-to-one personalization remains limited due to regulation AI platforms may eventually monetize healthcare interactions Chapters 00:00 Introduction to healthcare, AI, and advertising 00:18 Overview of the panel and discussion focus 01:04 AI as a primary tool for health information 01:53 Surge in AI-driven health queries and behavior shift 02:45 Changing role of search and health websites 03:40 Adoption of AI tools by physicians 04:31 Impact of AI on patient and doctor outcomes 06:33 Decline of traditional targeting and need for new strategies 07:40 Future of pharma ad spend in an AI-driven world 08:45 Neural networks and privacy-safe targeting explained 10:31 AI-driven audience targeting and patient lifecycle 12:28 Predictive modeling for healthcare populations 13:03 Importance of understanding patient journeys 15:57 Scaling AI audiences across media channels 17:11 Faster audience creation and activation 18:40 Personalization limits in healthcare marketing 20:12 Future of AI platforms and healthcare ads 22:01 Regulation and the future of pharma advertising Learn more about your ad choices. Visit megaphone.fm/adchoices

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Braze (BRZE) is leaning into AI-driven personalization, using first-party data to move beyond basic automation. Co-founder and Chief Technology Officer, Jon Hyman, explains how the platform enables scalable, real-time customer engagement while maintaining authenticity. As AI reshapes communication, the focus shifts to how autonomous agents can drive loyalty and revenue.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Send us Fan MailAI in nonprofit fundraising strategy is transforming how organizations operate—but using it incorrectly can damage donor relationships and trust. In this conversation, Katie Gaston of Bloomerang opens the box with practical guidance on how to use AI effectively while avoiding the most common pitfalls.Nonprofit professionals are increasingly turning to AI tools for donor research, reporting, and communications. The opportunity is clear: faster workflows, better insights, and increased capacity. But as Katie explains, AI is not a replacement for human judgment—it's a tool to enhance it. “AI should be a supportive arm… but it should never replace your judgment as a fundraiser.”From donor asks to personalized stewardship, the human connection remains at the core of successful fundraising. AI can prepare you for meetings, surface insights, and even recommend strategies—but it cannot replicate the emotional intelligence required in critical moments.This episode also addresses key operational risks. Sending AI-generated content without review, relying too heavily on automated insights, and failing to maintain clean data can all create serious challenges. As Katie reminds us, “The quality of your data is what AI will know—garbage in, garbage out.”You'll also learn how AI can dramatically improve efficiency—reducing hours of reporting work to minutes—while freeing your team to focus on relationship-building and strategic thinking.The takeaway? AI isn't replacing fundraising—it's redefining how effective fundraisers work. 00:00:00 Introduction to AI in Fundraising00:03:10 Meet Penny: AI Fundraising Assistant00:06:00 Why AI Should NOT Make Donor Asks00:09:00 Reviewing AI Output to Avoid Risk00:11:30 AI vs. Human Donor Knowledge00:14:30 Data Quality and CRM Accuracy00:17:30 Protecting Your Nonprofit Voice00:22:00 Personalization vs. Automation in Donor Care00:25:45 Using AI to Save Time and Increase Capacity00:27:00 How Fast Should Nonprofits Adopt AI?00:30:00 Final Thoughts on AI Strategy#TheNonprofitShow #NonprofitEfficiency #FundraisingStrategyFind us Live daily on YouTube!Find us  Live daily on LinkedIn!Find us Live daily on X: @Nonprofit_ShowOur national co-hosts and amazing guests discuss management, money and missions of nonprofits!  12:30pm ET   11:30am CT  10:30am MT  9:30am PTSend us your ideas for Show Guests or Topics: HelpDesk@AmericanNonprofitAcademy.comVisit us on the web:The Nonprofit Show

American Express is using AI-powered personalization to help frontline teams deliver faster, more relevant support while preserving the human connection behind premium service. This week on The Modern Customer Podcast, Anthony Devane, EVP and Head of Global Support Enablement and Control, Global Servicing at American Express, shares how his team is scaling AI and GenAI across servicing, travel, chat, and digital experiences while balancing personalization, trust, compliance, and customer expectations. A thoughtful conversation on how leading brands are using AI to reduce friction, strengthen customer relationships, and scale premium service across global operations.

Melissa, Amanda, and Molly take over the OPT studio to share Q1 industry benchmarks, discuss key trends in online sales, and offer actionable advice for OSCs and leaders navigating a lower-volume, higher-quality lead environment.HousekeepingOnline Sales and Marketing Summit - Oct 1-2 - Austin, TX - We encourage OSCs to attend, with new interactive programming planned.TITO ShoutoutMonica Fikany at New Home Inc - Monica had great lead-to-appointment conversions in Q1.Danielle Evans at Bishard Holmes - 38% of Danielle's appointments came from aged leads.Key TakeawaysLead volume is down, but quality is up: The top of the funnel remains constrained, volume hasn't fully bounced back. However, conversion rates are improving, meaning OSCs are doing more with less.Prospecting is the biggest win of Q1: Normally, prospecting dips in Q1 as new leads come in during selling season. This quarter, prospecting stayed consistent and even increased -- a major highlight. The 22% age lead appointment rate is a direct result of sustained prospecting effort.Your CRM is your most valuable asset: Maximize CRM usage by logging detailed notes after every interaction - aged leads hold untapped opportunity, and leadership relies on quality CRM data to make strategic decisions, so consistency matters.Personalization wins appointments: Buyers are on a longer journey right now. Personalizing outreach and follow-up, based on good notes and CRM data, is what sets top OSCs apart.Skills CheckFor Leaders:Stop measuring program success purely on lead volume. Conversion percentages are the more meaningful metric right now. Scorecards should reflect this shift.For OSCs:Be disciplined and consistent with prospecting.Treat every lead like it's gold.Respond to new leads fast, speed to first response beats the competition.Stay process-driven: no lead left behind.

Eric Mayhew, Chief Innovation Officer and Co-Founder of Fluency, shares how his experience in automotive advertising inspired the creation of Fluency and its mission to eliminate repetitive AdOps work through automation. Eric dives into the difference between automation and AI, the future of agentic systems, and why human creativity still matters most in advertising. From scaling ad operations to building compliant AI workflows, this conversation explores where marketing technology is headed next. Takeaways • Automation should eliminate repetitive tasks so teams can focus on strategy and creativity. • AI and automation are complementary, but they are not the same thing. • Human oversight remains critical for compliance, governance, and brand safety. • AI is powerful, but context quality determines the reliability of outputs. • Personalization in advertising may finally become practical with AI and automation. • Agencies want customizable workflows, not one-size-fits-all automation. • Fluency focuses on deterministic workflows that execute advertiser strategies at scale. • Agentic systems will combine rule-based automation with probabilistic AI decision-making. Chapters 00:00 Introduction to Eric Mayhew and Fluency 01:20 How Dealer.com Inspired the Creation of Fluency 04:07 The Real Problem with Manual AdOps Workflows 05:45 Fluency's Approach to Automation and AdOps Efficiency 07:45 Why AdOps Professionals Should Embrace Automation 10:41 The Difference Between Automation and AI 15:21 AI Risks, Hallucinations, and Governance Challenges 19:21 Where Humans Still Outperform AI 22:54 How Fluency Onboards and Automates Campaign Workflows 27:02 The Future of Agentic AI and Advertising Personalization Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of “At Your Convenience,” CSP Editor Diane Adam talks to Nick Triantafellou, director of marketing at Weigel's.On this podcast, recorded April 15 at the annual Midwest Fuel and Convenience Trade Show, M-PACT, in Indianapolis, the two discussed foodservice and how Powell, Tennessee-based Weigel's is measuring success beyond sales. They also discussed loyalty and digital engagement and what role personalization plays in the c-store retailer's loyalty strategy.Key Takeaways:Foodservice is booming: Breakfast and lunch have always been strong at Weigel's, but third-party delivery is now driving dinner sales.Loyalty done right: Weigel's refreshed app interface has increased engagement, especially among Gen Z customers.Personalization isn't the silver bullet: It's a tool, not the end-all-be-all. The key is meeting customers where they are—without being creepy.C-stores can compete with QSRs: With better locations and high-quality food, the industry just needs to treat marketing like the big players do.Data, data, data: From consumer data platforms to retail media networks, Weigel's is building a smarter, more targeted marketing engine.

Nicolas Alejandro Bogliolo is the AI PM at Despegar, the largest online travel agency in Latin America, and the engineer-product-hybrid behind Sofia, the GenAI travel concierge that beat most of the OTA world to a working multi-agent system. Before MCP was a standard and before LangChain was widely adopted, his team had already shipped their own orchestration layer and tool protocol in production. This conversation is a rare look at what it takes to build an agentic system that actually books trips, runs on WhatsApp, and keeps adding capabilities without falling over.What we cover:- Chappi, the brain of Sofia: how Despegar built an internal orchestration layer when there was nothing off the shelf- Building "MCP before MCP": the custom tool-calling protocol that predated the Anthropic standard- Multi-agent architecture by vertical: flights, hotels, activities, and cars each own their own flow- Decentralized agent ownership: how any squad in the company can build a flow with central supervision- Sofia on WhatsApp: making messaging the consumer control center, the way Slack became it for the enterprise- The five-phase travel arc Sofia covers: dreaming, planning, anticipation, in-trip, and post-trip- KPI evolution: why "in-scope conversation rate" topped out near 96 percent and what they measure now- The flight-delay-claim use case and why filing claims through a chatbot is a perfect agent task- Group trip planning in WhatsApp groups: the next frontier for travel agents- Sofia as channel of choice: the WeChat-style vision for an agent that handles your entire trip- Why Despegar held off on giving Sofia the ability to bargain with customers, for now. Whether you are building production agents, running an OTA, or just curious about how an AI travel concierge actually works under the hood, this episode is full of grounded, in-production lessons from a team that had to invent the patterns the rest of us are now adopting.Links and Resources:- Despegar: https://www.despegar.com- Sofia announcement: https://investor.despegar.com/news-presentations/news-releases/news-details/2024/Despegar-revolutionizes-the-tourism-industry-introducing-the-regions-first-Generative-AI-Travel-Assistant- Sofia coverage on PhocusWire: https://www.phocuswire.com/despegar-debuts-genai-travel-assistant-remembers-previous-interactions- MLOps Community: https://mlops.community- Subscribe for more agent and AI infra deep divesTimestamps 00:00 - Intro: Nicolas, Sofia, and Despegar in LatAm01:30 - Chappi as the brain of Sofia and the squad model04:00 - Anyone in the company can build a flow07:00 - Why airline check-in still exists and what agents could replace09:30 - The flight-delay refund story and the chatbot gap13:00 - File-the-claim-for-me as a perfect agent use case16:00 - The dreaming phase: helping users who do not know where to go yet19:00 - In-scope conversation KPI hitting 96 percent and what comes next22:00 - Beating the traditional flight search UI with conversation25:00 - WhatsApp group trip planning and the ski trip example28:00 - Personalization at scale and the new gateway to the internet31:00 - WhatsApp as the consumer control center, like WeChat in China34:00 - Sofia as gateway: complaints, customer service, and verticalized agents37:00 - Building MCP before MCP and the custom orchestration layer40:00 - Why Sofia does not negotiate prices, yet#AIAgents #MCP #AgenticAI

Most football clubs are built around matchday but not Arsenal. As it enters its 140th year, the team in a lot of ways, is only just getting started.  In this episode of Frontier CMO, Josh travels to London to meet Juliet Slot, Chief Commercial Officer at Arsenal Football Club, on home turf to go inside the playbook behind one of the most engaged global fanbases in sports.  From the rapid rise of the women's league to the clubs' staying power of “cool,” Juliet shares why serving your fanbase is better than trying to sell them.  This conversation lands at a historic moment with the club reaching its first UEFA Champions League Final in 20 years and is within reach from capturing the Premier League title. As FIFA tournaments kick off around the globe, this is your front row ticket to exploring how one of the world's biggest clubs is making its mark across platforms, geographies, and generations. 00:00 Arsenal's Global Brand Philosophy 02:00 Serving Fans Beyond Match Day 04:10 Building a Modern Global Football Brand 06:00 Fashion, Culture & the Arsenal Identity 07:30 Personalization, Content & AI Strategy 09:40 Understanding Different Types of Fans 10:45 Measuring Real Fan Engagement 12:30 The Viral Women's Football Dating Campaign 14:00 Fans as Co-Creators of the Arsenal Brand 16:30 Authentic Marketing vs. Over-Selling 18:40 Growing Arsenal Women Into a Global Business 22:45 Choosing the Right Brand Partnerships 24:20 Purpose-Driven Campaigns & “No More Red” 25:45 Creating One Unified Club Culture 27:00 Winning Beyond Trophies 29:20 The Future of Sports, Tech & Virtual Viewing 32:00 Why Arsenal Resonates Around the World 34:00 Final Lessons on Community & Long-Term Growth

Most CMOs think personalization requires complex AI and endless customer segments. Elizabeth Maxson proves otherwise. She breaks down actionable personalization strategies — from geo-tagging that drove a 51% lift in event attendance to playful homepage tricks that captured marketers' attention and boosted engagement. The takeaway: Stop overcomplicating personalization. Start with big buckets and simple, contextual changes. Chapters 00:00 - Why Personalization Should Be Simple 00:32 - First-Time vs Returning vs Current Customers 01:07 - Marketer vs Developer Homepages 03:01 - Geo-Tagging: +51% Attendance 04:28 - The Big Buckets Approach 05:28 - The Lorem Ipsum Attention Hack 06:22 - Ruggable Cat vs Dog Personalization ----Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Learn how personalization transforms ordinary necklaces into deeply emotional keepsakes. From interlocking hearts to custom handwriting jewelry, find the secrets to choosing meaningful romantic gifts your wife will treasure forever. Jewelry Gifted City: Hackettstown Address: 999 Willow Grove St Website: https://jewelrygifted.com/

Send us Fan MailIf you've ever thought…“I'm doing everything right… so why isn't this working?”This episode is going to connect some major dots.Because what if it's not that your body is broken……it's that your body needs something different?In today's conversation, Cody sits down with Becca Roses, founder of Mind Body Genes, to break down epigenetics in a way that actually makes sense—and actually applies to your life.You'll learn how your genes act as a blueprint… but your lifestyle, stress, and environment determine how that blueprint is expressed.And why two women can follow the exact same plan—and get completely different results.In This Episode:What epigenetics actually means (in real life, not textbook language)Why “doing everything right” can still leave you feeling stuckHow your genes influence your stress response and nervous systemThe connection between epigenetics and hormone balanceWhy some women struggle more with PMS, heavy periods, or perimenopauseHow to start supporting your body without overwhelmKey Takeaways:Your body is not random—it's respondingYour genes are not your destiny, they're your blueprintStress sensitivity is not a weakness—it's informationHormone symptoms often point to deeper patternsPersonalization is the missing piece for so many womenLINKSConnect with Becca:Instagram: @mindbodygenesWebsite: https://www.mindbodygenes.comWork with Cody:CALM ResetHTMA TestingInstagram @codyjeansandersMixhers:https://www.mixhers.comUse code CODY for a discountDid you learn something new today? Be sure to subscribe to this podcast and share this episode with all the girls you love. We would appreciate it if you'd also leave us a rating and review on iTunes.Want to join our Mixhers Girl community and keep this conversation going? We'd love to hear your thoughts, feelings and experiences! Join us HERE!Join Mixhers email list and be the first to have access to new products and be the girl in the know!Follow Cody Instagram:@codyjeansanders

“…what you need to know is this. And that is the difference between a conventional farmer and a sustainable farmer is that a conventional farmer grows plants and a sustainable farmer grows soil; and very simple in it's difference, and yet incredibly profound in how it takes you down two different paths, because, what are you doing if you're growing plants?”In this episode of Personalization Outbreak, Glenn Llopis sits down with Dr. Britt Yamamoto, author of The Soil of Leadership, to explore a powerful idea: great leadership is like sustainable farming. It is not about chasing quick wins, it is about cultivating the conditions for long-term growth.They unpack the difference between “growing plants” and “growing soil,” why trust cannot be forced, how burnout is a systems issue, and what leaders can do to rebuild healthier cultures through reflection, self-awareness, and sustainable leadership practices.----------------------------------------------------------------------------------------------------Video Content:   0:00 - Intro   03:44 - Leadership Starts with Soil, Not Strategy   16:09 - Reclaiming the Right Soil   20:17 - Why Most Workplaces Are Broken at the Root   27:23 - Leadership 3.0: The Shift Leaders Must Make Now   30:16 – The Soil of Leadership: Cultivating the Conditions for Transformation   33:13 - The Personalization of Leadership   38:44 - We Can't Practice What We Can't Imagine   41:22 – The Hyakusho Way: Japanese Wisdom for a Flourishing Life   43:26 - Rebuilding Trust Starts Within   45:36 - Shifting from Paradigm to Purpose   50:31 - Community First, Corporate Second   ----------------------------------------------------------------------------------------------------   

Beyond Vanilla AI: The Era of Agentic Commerce The retail industry has moved past experimentation. We are now in the realization phase where AI agents deliver frictionless, store-like expertise to digital channels. Vic Miles from Microsoft and Crispin Lowery of Rezolve AI join Jeremy Goldman to discuss why the early adopter advantage is now a requirement for brand survival. Key Discussion Points: - The Expertise Bridge: How agents bring the nuance of an in-store associate to the digital checkout. - The Tesla Analogy: Why retail tech is shifting from rigid engineering to fluid, software-first experiences. - Personalization as a Killer App: Using AI to move from broad demographics to solving singular problems for individual shoppers. - Choosing the Right Foundation: Why no-hallucination platforms and transparent brand voice control are the new vendor table stakes. Stop managing metrics. Start mastering the human experience.

What makes an unboxing moment feel unforgettable and why are so many companies still getting it wrong?

Most enterprise AI delivers generic responses when you need role-specific intelligence. Peter Day, General Partner at super{set} with a PhD in machine learning and 8 years leading product at Quantcast, breaks down why agentic personalization creates unbreachable competitive moats. He reveals the architectural patterns for building software that learns individual preferences in real-time, explains why role collapse will eliminate 60-80% of traditional engineering positions, and details the shift from seat-based SaaS pricing to outcome-driven models that compound user value.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Summary In this episode of the AI for Sales podcast, Chad Burmeister interviews Carlos Corredor, CEO of Condor, about the intersection of AI and marketing. They discuss how AI can enhance customer engagement, the importance of quality touch points, and the misconceptions surrounding AI. Carlos emphasizes the need for human oversight in AI processes and the importance of finding the right audience for marketing messages. They also touch on emerging technologies, the ethical implications of AI, and the skills necessary for success in the evolving landscape of AI-driven marketing. Takeaways Marketing can drive revenue, not just incur costs. Quality touch points enhance customer engagement. The increase in touch points often sacrifices quality. AI is a tool, not a magic solution. Effort and resources are needed for effective AI use. Human oversight is crucial in AI applications. Finding the right audience is key to marketing success. AI governance requires collaboration across sectors. Skills in AI are essential for future marketing roles. Industry expertise enhances AI effectiveness. Chapters 00:00 Introduction to AI in Sales 02:37 The Role of AI in Customer Engagement 05:33 Challenges of Personalization in Marketing 08:23 Misconceptions About AI 11:09 Balancing Automation and Human Touch 14:48 Emerging Technologies in Marketing 18:38 Ethics and Governance in AI 22:36 Skills for Success in AI Marketing The AI for Sales Podcast is brought to you by BDR.ai, Nooks.ai, and ZoomInfo—the go-to-market intelligence platform that accelerates revenue growth. Skip the forms and website hunting—Chad will connect you directly with the right person at any of these companies.

Most people don't struggle with sales because they lack skill. They struggle because something in them feels… off. If you've ever felt icky about selling, if your DMs feel like a graveyard of unanswered pitches, or if you know you have something that can change people's lives, but you can't figure out how to talk about it without feeling like a used car salesman, this episode is your wake-up call. Jake and Emily unpack what most people never get taught about sales: that the problem usually isn't the offer, the price, or even the audience. It's the internal disconnect between belief, communication, and conviction. They explore the difference between influence and manipulation, why marketing can silently make sales easier or harder, and how trust is actually built long before the “close” happens. By the end of this conversation, you'll start to notice the small, overlooked patterns in how you communicate that may be making selling harder than it needs to be,  and what shifts when you finally see them clearly. Learn more at https://discover.fordivine.com/ Take a free brand quiz at https://quiz.fordivine.com/  What You'll Learn: Why feeling “icky” about selling is usually a signal of misalignment The difference between influence and manipulation How to close without feeling pushy or transactional Why your marketing might be making sales harder Follow-up strategies that actually convert Timestamps: (03:09) - Why Selling Feels Uncomfortable (10:16) - The Real Reason People Aren't Closing (11:04) - Influence vs Manipulation (12:45) - How to Know If You're Being Transactional (18:16) - The “Mirror Before the Door” Principle (20:33) - Marketing vs Sales (27:58) - The Emotional Intelligence of Being Sold (29:56) - Why Congruence Beats Confidence in Sales (35:12) - The Power of Voice Notes and Personalization (42:55) - Follow-Up Strategies That Actually Work (54:34) - Message to the Woman Who Knows She's Called, But Can't Bring Herself to Sell Connect with Jake: Instagram | https://www.instagram.com/jakehavron     YouTube | https://www.youtube.com/channel/UCxG3bKqLK_M_HZpOgiVrtng  More from Emily & FORDIVINE: Website |  https://meetemilyford.com   Instagram | https://www.instagram.com/itsemily         Facebook | https://www.facebook.com/itsemilymethod    YouTube | https://www.youtube.com/c/ITSEMILYFORD  Called & Crowned Podcast | https://www.instagram.com/calledandcrowned/  FORDIVINE | https://www.fordivine.com/ 

Laura Jones explains that generative AI is raising the bar for creativity. When everyone can produce “pretty good” content, the real challenge is creating something that actually stands out. The risk is not poor output, but settling too quickly for what already works. She argues that as products become more similar, brand becomes a signal of trust. Not in a visual sense, but in the experience behind it. At Instacart, that shows up in details like how a banana is selected. With over a billion bananas delivered and millions of orders including notes on ripeness, customers are expressing very specific preferences. That behavior led to both new product features and the creative idea behind their Super Bowl campaign. The conversation also explores how teams should work with AI. While it can automate repetitive tasks and speed up iteration, it can also create a tendency to agree with what's generated, especially when working alone. Laura emphasizes that the best ideas still come from people challenging each other, building on different perspectives, and pushing beyond the first acceptable answer.Key takeaways:  Mediocre is easier than ever, which raises the bar for originality When AI gets everyone to “pretty good,” the work that stands out has to go further. The bar is not lower. It is higher. Brand becomes trust when products converge As functionality becomes easier to replicate, the question becomes who you trust to get it right. Brand is the answer to that. Only do what only you can do Use AI to take on repetitive work, then spend your time on judgment, insight, and decisions that require a human point of view. Need-finding still requires real people Synthetic research can help, but it cannot replace observing real behavior. The banana insight came from what customers actually did. Human plus bot plus human Working only with AI makes it easy to agree and move on. The best ideas come from people challenging each other, with AI in the middle, not as the whole process. Instacart: instacart.com Super Bowl ad: Super Bowl (Instacart ad) Laura LinkedIn: linkedin/laurajones ro's post: ro.co/perspectives/super-bowl-economics   00:00 Intro: Originality vs AI Complacency00:27 Meet Laura Jones01:23 Brand as trust when products converge03:50 Personalization and reducing mental load06:24 What still matters in marketing10:33 Why need-finding cannot be shortcut14:09 Using AI without losing judgment16:33 New channels and where customers actually are21:35 Why “dopey ideas” matter25:42 Human plus bot plus human28:44 Inside the Super Bowl ad31:47 From banana insight to product34:49 Taking creative risks at scale37:34 Fear, pressure, and team chemistry46:24 AI and faster prototyping53:26 The debrief

Where Empathy Meets Artificial Intelligence in Customer Service    Shep interviews Jen Grant, Chief Marketing Officer at Quiq. She talks about how intentionally designed AI, combined with human empathy, can create effortless, personalized experiences at scale that customers embrace.  This episode of Amazing Business Radio with Shep Hyken answers the following questions and more:   How can businesses prevent AI from making customer service mistakes?  How can personalization through technology enhance customer loyalty?  Why is an effortless customer experience crucial for building loyalty?  How can technology help anticipate customer needs?  How has customer willingness to interact with AI changed over time?  Top Takeaways:    Artificial intelligence is a powerful tool for improving the customer and employee experience when it is designed with intention. Just as human agents need training, AI tools need enterprise controls, verification checks, and simulations to make sure they provide the right information and respond appropriately to customer needs.   More customers now consider interactions with AI to be part of the norm. But some are still hesitant to embrace it because of past frustrations with outdated systems, like confusing phone trees or long waits in loops they associate with all types of technology.  When interactions with technology, such as AI tools, create effortless experiences, customers are more willing to use them. Companies need to educate customers on how to use their technology and self-service solutions to improve their experiences.   Make it easy for customers to do business with you. This applies to every part of the customer's journey, not just technology.  Customers want answers and help fast. Give it to them with as little effort as possible.  Human empathy remains important, especially in high-stress situations. Customers need to be comfortable with how they are being helped and supported, whether it is AI, a human agent, or both working together. In stressful situations, customers appreciate the reassurance that a human agent will be there to provide empathy and assist them when tech-powered solutions are not working.  Technology should not replace people. Good technology makes it easier for employees to help customers and faster for customers to get solutions.  Personalization and empathy can be scaled through technology. Human employees can only remember so much. Technology can help them have instant access to a customer's data and history. When technology recognizes returning customers, anticipates their needs, and connects their history to current interactions, it replicates the feeling of being known and valued.   Plus, Shep and Jen talk about what could happen if companies began customer service interactions with a human for empathy, then transitioned to AI for efficiency, rather than forcing customers through automation before reaching a person. Tune in!  Quote:   "AI agents need clear guidance and verification checks to stop 'hallucination.' It is important that if AI goes off track, companies need to catch and correct it before it gets to customers."  About:    Jen Grant, Chief Marketing Officer at Quiq. She has held executive leadership roles, including CEO, COO, and CMO, as well as senior marketing positions at Appify, Box, Cube, Dialpad, Elastic, Google, and Looker.  Shep Hyken is a customer service and experience expert, New York Times bestselling author, award-winning keynote speaker, and host of Amazing Business Radio.   Learn more about your ad choices. Visit megaphone.fm/adchoices

Personalization at scale gets pitched like a switch you flip, but the reality is messier and way more human. From Adobe Summit, Jeannie Walters' sits down with IBM leaders to unpack what it takes to deliver customer experience that feels consistent, connected, and genuinely helpful across digital touchpoints even when different teams, tools, and timelines are involved.First, Betsy Rohtbart, VP, Digital Experience & IBM.com, shares a simple reframing with big implications: start with the task your visitor is trying to complete, then design the experience to make that “pay off” every time. We talk about why customers often feel the gaps instead of the beautiful moments we intended, how secret shopping your own trial and onboarding flow exposes breakpoints fast, and why chasing problems like a “toddler soccer game” creates more friction. The standard is brutal but fair: customers compare you to their last best experience anywhere.Then Jay Trestain, EMEA Marketing Transformation Lead & Client Partner, breaks down agentic orchestration in plain terms: AI agents that act as domain experts and work together across an enterprise workflow. We dig into what leaders miss when they rush to deploy AI, how a clear North Star vision sharpens decisions about martech, process redesign, and KPIs, and why agentic technology is changing digital discovery by bypassing traditional web real estate. The punchline: governance is not red tape, it's the engine for rapid, high-quality decisions that help good pilots scale into real value.Subscribe for more practical customer experience strategy, share this with a teammate leading transformation, and leave a review.Follow Betsy Rohtbart on LinkedIn: www.linkedin.com/in/betsymorserohtbart/Follow Jay Trestain on LinkedIn: www.linkedin.com/in/jay-trestain/Resources Mentioned:Order your copy of Experience Is Everything -- experienceiseverythingbook.comLearn more about CXI Membership™ and apply -- CXIMembership.comExperience Investigators -- experienceinvestigators.comEnjoyed the show? Subscribe, share with your team, and leave a quick review to help others find us. Leave your review at ratethispodcast.com/xact.Want to ask a question? Visit askjeannie.vip to leave Jeannie a voicemail! (And don't forget to follow Jeannie Walters, CCXP, CSP on LinkedIn!)

This week, Ian and Cathy McKnight discuss a recent piece of research from Cathy on her Bear Essentials series on the Seventh Bear blog - Bear Essentials: You Can't Personalize What You Don't Understand  They discuss:  Audience segmentation beyond job titles Building and operationalizing personas The role of AI and content in marketing Customer journey mapping and needs analysis Ian then joins Robert Rose in the virtual bar, The Rose & Rockstar, for a classic cocktail and a chat.  This week, Ian and Robert discuss Robert's latest article in his Rose Tinted Glasses column on the Content Marketing Institute blog: The Career Hedge: Why Even Your 'Satisfied' Employees Are Running an Exit Strategy Enjoy! — The Links The people: Ian Truscott on LinkedIn  Cathy McKnight on LinkedIn Robert Rose on LinkedIn Mentioned this week: Bear Essentials: You Can't Personalize What You Don't Understand The Career Hedge: Why Even Your 'Satisfied' Employees Are Running an Exit Strategy Robert's podcast: This Old Marketing  Robert's newsletter: Lens, his websites, robertrose.net and seventhbear.com Rockstar CMO: The Beat Newsletter that we send every Monday Rockstar CMO on the web and LinkedIn Previous episodes and all the show notes: Rockstar CMO FM. Track List: We'll be right back by Stienski & Mass Media on YouTube Piano Music is by Johnny Easton, shared under a Creative Commons license You can listen to this on all good podcast platforms, like Apple, Amazon, and Spotify. Learn more about your ad choices. Visit megaphone.fm/adchoices

What if one of the most popular health trends in the world, fasting, is actually being done wrong by millions of people? In this powerful and deeply grounded conversation, Darin sits down with world-renowned longevity expert Dr. Valter Longo to cut through the noise surrounding fasting, dieting, and modern health trends. From the dangers of prolonged fasting and skipping breakfast to the science behind the Fasting Mimicking Diet, this episode delivers a reality check rooted in decades of clinical research, not social media hype. They explore the intersection of longevity, cancer, metabolism, and modern lifestyle, unpacking why extreme protocols fail, why simplicity wins, and why aligning with your biology is the true key to a long, disease-free life.     What You'll Learn Why most fasting trends are misapplied and potentially harmful The safest and most sustainable fasting window for longevity Why skipping breakfast is linked to increased mortality risk The science behind the Fasting Mimicking Diet (FMD) How fasting impacts cancer cells vs healthy cells The hidden risks of GLP-1 weight loss drugs Why "easy solutions" often lead to worse long-term outcomes The importance of circadian rhythm in metabolism The truth about protein intake and long-term health risks Why slow, consistent change beats every "quick fix"     Chapters 00:00:00 – Opening: SuperLife mission and framing the conversation 00:00:32 – Sponsor: Therasage infrared sauna and heat therapy benefits 00:03:16 – Introduction: Dr. Valter Longo and longevity research 00:03:40 – The fasting craze: what's misunderstood 00:04:05 – Documentary discussion: science vs entertainment 00:05:13 – Why education must outweigh entertainment 00:06:19 – The danger of social media health advice 00:07:00 – Food systems, pharma, and systemic health issues 00:07:58 – Why clinical trials matter more than anecdotes 00:08:15 – Framing fasting: trends vs real science 00:08:59 – The problem with DIY fasting 00:10:03 – The safest fast: 12-hour daily fasting explained 00:10:38 – Risks of long fasting: cholesterol, gallstones, mortality 00:11:09 – Why skipping breakfast increases health risks 00:11:49 – 12-hour fasting as the most sustainable protocol 00:12:13 – Modern eating habits: 15+ hour eating windows 00:12:34 – Why extreme diets fail long-term 00:13:37 – Feasibility: why most people won't sustain extremes 00:13:56 – Introducing the Fasting Mimicking Diet (FMD) 00:14:53 – Risks of fasting without personalization 00:15:20 – Why fasting can do more harm than good 00:15:45 – Sponsor: Alkemis Paint, indoor toxicity and non-toxic paint 00:18:42 – Clinical trials: meal timing and metabolic health 00:19:03 – Morning vs evening calorie intake study 00:19:56 – Why late eating disrupts metabolism and sleep 00:20:21 – Epidemiology: skipping breakfast increases mortality 00:21:18 – Circadian rhythm and digestion explained 00:22:11 – Evolutionary biology of eating patterns 00:22:41 – Circadian violations and long-term consequences 00:23:11 – Short-term benefits vs long-term risks 00:24:01 – Why slow progress leads to real results 00:24:49 – Realistic timelines: years, not weeks 00:25:19 – The modern system pushing unhealthy behaviors 00:25:48 – GLP-1 drugs: convenience vs consequences 00:26:13 – The danger of "effortless health" 00:27:13 – Exercise analogy: why effort still matters 00:28:06 – The "pill for everything" mentality 00:28:48 – Finding balance between extremes 00:29:25 – Sponsor: Our Place, non-toxic cookware and health 00:31:12 – Personalization vs one-size-fits-all health 00:31:51 – GLP-1 risks: depression, anxiety, muscle loss 00:32:36 – Natural vs drug-induced weight loss differences 00:33:29 – Rebound weight gain and hormonal suppression 00:34:14 – Supplements vs fixing root causes 00:34:37 – What is the Fasting Mimicking Diet 00:35:05 – Cancer research: fasting and treatment synergy 00:36:13 – How FMD mimics fasting while protecting the body 00:37:06 – Gut health and microbiome benefits 00:38:32 – FMD vs water-only fasting outcomes 00:39:26 – Clinical trials: Crohn's and colitis remission 00:40:03 – Importance of independent research 00:41:20 – Longevity through the lens of fatherhood 00:42:23 – Concerns about AI and children's development 00:43:25 – Social isolation vs digital addiction 00:44:25 – The need for balance in technology use 00:45:10 – AI overdependence and cognitive decline 00:46:18 – Mental health crisis and modern technology 00:47:10 – Reclaiming creativity and human agency 00:48:43 – Fasting and cancer: immune system activation 00:49:53 – Why cancer cells resist fasting signals 00:51:10 – The "desert analogy" for cancer vulnerability 00:52:54 – Combining fasting with therapies 00:54:07 – Future of treatment: precision targeting 00:55:14 – Early detection and personalized interventions 00:56:12 – Where fasting fits in cancer care today 00:57:31 – The protein debate: how much is too much 00:58:17 – Protein intake guidelines explained 00:59:07 – Quality vs quantity of protein 01:00:18 – SuperLife Patreon and accessing exclusive content 01:01:21 – The protein obsession problem 01:02:00 – Children consuming excessive protein 01:03:18 – Portion control and dietary awareness 01:04:07 – Risks of excessive protein intake 01:05:04 – Minimal benefits vs long-term risks 01:06:12 – Longevity populations and low protein intake 01:08:00 – The future of nutrition science 01:12:00 – Final reflections on longevity and health 01:15:00 – Closing thoughts: aligning with biology     Thank You to Our Sponsors Therasage: Go to www.therasage.com and use code DARIN20 at checkout for 20% off Alkemis: Go to alkemispaint.com and use code DARIN10 for 10% off your order. Our Place: Toxic-free, durable cookware that supports healthy cooking. Use code DARIN for 10% off at fromourplace.com.     Join the SuperLife Community Get Darin's deeper wellness breakdowns — beyond social media restrictions: Weekly voice notes Ingredient deep dives Wellness challenges Energy + consciousness tools Community accountability Extended episodes Join for $7.49/month → https://patreon.com/darinolien     Find More from Dr. Valter Longo Website: valterlongo.com Instagram: @prof_valterlongo Get His New Book: Fasting Cancer     Find More from Darin Olien: Instagram: @darinolien Podcast: SuperLife Podcast Website: superlife.com Book: Fatal Conveniences New Show: Roadmap to Happiness     Key Takeaway "The path to longevity isn't found in extreme protocols or quick fixes, it's found in consistency, alignment, and understanding your biology. When you stop chasing shortcuts and start working with your body instead of against it, that's when real transformation happens, not just in how long you live, but in how well you live."

In this episode of Insurance Town, Kevin Deutsch joins the show to unpack what's really happening in the health insurance space right now. From the growing role of AI to the emergence of ICHRA as a disruptive model, Kevin brings a practical perspective on where the industry is headed and what it means for agents, brokers, and agencies.The conversation digs into how technology is reshaping distribution, why data management is becoming a competitive advantage, and how personalization is moving from buzzword to reality. If you've ever felt like health insurance is overly complex, this episode helps simplify what matters and where to focus.Key Takeaways: Health insurance remains overly complex, but simplification is coming through better technology and data use  AI is only as effective as the data behind it, making data organization critical for agencies  ICHRA is creating new opportunities and challenges for brokers in the benefits space  Commission structures and distribution models are continuing to evolve  Personalization is not a future idea, it is actively shaping how coverage is designed and delivered today  Agencies that understand and adapt to these shifts will be better positioned for long-term growth  00:00 – Introduction 09:02 – Changes in Health Insurance Distribution 12:05 – The Role of Brokers in Health Insurance 15:06 – The Shift in Commission Structures 18:10 – The Impact of ICRA on Health Insurance 24:10 – Future of P&C Agencies in Health Insurance 29:57 – Understanding ICRA and Its Value 32:29 – Adoption of AI in Health Insurance 38:05 – Data Management for AI Utilization 40:26 – Trends in Personalization of Health Insurance 43:21 – Preparing for Personalized Benefits 44:42 – The Rise of Provider Sponsored Health Plans  48:59 – Softheon Mission and ServicesSponsorsCanopy ConnectMav1Fort AI Goli 

In this Omni Talk Retail episode, recorded live at Retail Technology Show 2026 in London from the Vusion podcast studio, Chris Walton speaks with Meriel Neighbour, Director of Technology Delivery and Transformation at River Island, about what it really takes to execute large-scale transformation in retail today. Meriel shares her unique career journey from hospitality into retail technology, and how that foundation shaped her deeply customer-centric approach to transformation. She explains why retail has shifted from technology-led initiatives to business- and product-led strategies, with customer experience now at the center of every decision. The conversation explores how River Island is replatforming its ecommerce experience, why data quality is becoming critical for AI-driven discovery, and how emerging technologies like voice and AI agents could reshape how customers shop across channels. Meriel also outlines the importance of integrations, real-time data, and seamless systems in enabling modern retail experiences, while calling out one of the industry's biggest unresolved challenges: returns. Key Topics Covered: • Why transformation must be driven by business strategy, not technology alone • How River Island is replatforming ecommerce for the future • The role of data in powering AI, search, and product discovery • Why voice commerce and AI agents could reshape shopping behavior • The importance of integrations and real-time information • How customer expectations are evolving across channels • Why returns remain one of retail's biggest unsolved problems • The opportunity for personalization, loyalty, and dynamic pricing Thank you to Vusion for supporting Omni Talk Retail's live coverage from Retail Technology Show 2026! #RTS2026 #RetailTechnologyShow #OmniTalkRetail #RiverIsland #RetailTransformation #Ecommerce #AIinRetail #CustomerExperience #RetailInnovation #Vusion

In this week's news and comment episode, we break down three major trends shaping podcasting right now, from AI-driven discovery changing how shows get surfaced, to platform shifts from companies like Apple that could redefine how content is experienced, to the growing importance of transcripts and search visibility. As the conversation unfolds, we explore how these changes are influencing who gets found, how audiences discover new shows, and what it means for creators trying to stay competitive in a more crowded space. At the center of it all is a bigger question: as discovery becomes more algorithm-driven and platforms continue to evolve, what actually determines which podcasts rise and which ones get left behind?Episode Highlights:[01:56] Obsession Worthy Podcasts Preview[04:23] Active Podcast Stats[06:18] Indie Chart Talk[07:58] Webby Winners Rundown[11:32] Upcoming Creator Events[15:05] Apple Leadership Shift[22:27] Panel Reactions Debate[26:23] Hardware Pivot at Apple[27:52] Innovation You Don't See[28:51] Show Rebrand and New Time[31:14] AI Search Picks Winners[33:53] Transcript Visibility Tactics[40:17] Personalization and Prompt Flaws[47:50] SEO Landing Pages Strategy[52:46] Wrap Up and Tomorrow's TechLinks & Resources: Riverside YouTube Event on April 23:https://podcastingmorningchat.com/riversideytFemale Entrepreneurs Podcasting Event on April 28:https://podcastingmorningchat.com/femalepodsEmpowered Podcasting Conference on April 21-23:https://empoweredpodcasting.comThe Podcasting Morning Chat: ⁠⁠www.podcastingmorningchat.com⁠⁠Ways to Watch or Listen:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠https://www.podcastingmorningchat.com/joinus/Meet the PMC Cast and Crew:⁠⁠https://podcastingmorningchat.com/people⁠⁠Join The Empowered Podcasting Facebook Group:⁠⁠www.facebook.com/groups/empoweredpodcasting⁠⁠⁠Book A Free Call With Marc: https://calendly.com/ironickmedia/freestrategycallApplication To Submit Your Show For Evaluation: ⁠⁠⁠⁠⁠https://podcastingmorningchat.com/eval⁠⁠Join us every other Monday at 7 AM ET for the Obsession Worthy Podcasts:⁠⁠⁠http://podcastingmorningchat.com/owp/⁠⁠Join us LIVE every weekday morning at 7 am ET (US) on ⁠Clubhouse⁠: ⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠https://podcastingmorningchat.com/clubhouse⁠⁠EPC3 Speaker Application:⁠⁠ ⁠https://empoweredpodcasting.com/speakersPowered by⁠⁠⁠ ⁠iRonickMedia.com⁠⁠⁠⁠ and⁠ ⁠ContentCreatorsAccountant.com⁠⁠Send in your mailbag questions:⁠⁠⁠⁠ https://www.podcastingmorningchat.contact/⁠⁠⁠⁠ or ⁠marc@ironickmedia.com⁠Want to be a guest on The Podcasting Morning Chat? Send me a message on PodMatch, here: https://podmatch.com/hostdetailpreview/1729879899384520035bad21b

What actually separates a luxury wedding brand from everyone else charging less?Spoiler: It is not just prettier work.In this episode of The Level Up Podcast, we are breaking down the real differences between standard service and true luxury hospitality by pulling examples from our own experiences with Disney VIP tours, five-star hotels, and other high-end brands outside the wedding industry.Because if you want to book luxury clients, you need to understand what luxury actually feels like.We unpack how elite brands create trust, exclusivity, and white glove experiences from the very first touchpoint, and how you can apply those same principles to your wedding photography or filmmaking business.Inside this episode, we cover:How concierge-style booking creates instant perceived valueWhy luxury clients expect you to lead with confidenceThe small hospitality details that make a massive differenceHow social etiquette impacts your ability to book higher-end clientsWhy luxury brands never nickel and dime their customersHow to study other industries to improve your own client experienceIf you want to attract higher-end clients, raise your rates, and create an experience that makes your brand unforgettable, this episode is a must-listen.Because luxury is not just about what you deliver.It is about how you make people feel every step of the way.This episode was sponsored by Wanderlust Videos, use LEVELUP100 for $100 off your first edit at https://www.wanderlust-videos.com/And thank you to VidFlow for sponsoring today's episode. Level up your delivery experience at https://vidflow.co/thelevelupcoTimestamps:00:00 - 05:24 | Intro to Car Chats & Why Luxury Experiences Matter05:25 - 10:22 | Disney VIP Booking Experience & The Power of Concierge-Level Sales10:23 - 15:27 | Leading Clients With Confidence & Going Above and Beyond15:28 - 21:14 | Hospitality, Social Etiquette, and Reading the Room as a Professional21:15 - 28:34 | Ritz-Carlton Lessons, Saying Yes, and Expanding Your Offerings28:35 - 34:44 | Hotel Hospitality Examples & Personalization in Luxury Service34:45 - 39:19 | Why Luxury Brands Don't Nickel and Dime Their Clients39:20 - 43:19 | Building a White Glove Client Experience in Your Wedding BusinessThe next round of The Luxury Mastermind will start in Spring 2026! We are thrilled to welcome you inside our signature 8 week program. Learn more + save your seat here >> https://thelevelupco.com/mastermind

In this roundtable conversation, a diverse group of interior designers and kitchen specialists discuss how kitchen design has transformed in the post-pandemic era. Rising costs, shifting client expectations, and new technologies are forcing designers to rethink how kitchens function and how they are delivered to clients. The conversation explores everything from appliance innovation and zoning strategies to the emotional role of kitchens as gathering spaces. Designers also confront difficult realities such as escalating budgets, supply chain issues, and the need to guide clients through increasingly complex decisions. We gathered at the Pacific Sales Kitchen & Home showroom in San Diego. A beautiful and well appointed space with so much to see and the room to enjoy it. Designer Resources Pacific Sales Kitchen and Home. Where excellence meets expertise. TimberTech – Real wood beauty without the upkeep Shelter Republic – Request your membership invitation At its core, the discussion highlights a broader truth about the design profession today: kitchens are no longer simply rooms for cooking. They are ecosystems that reflect lifestyle, culture, wellness, and the evolving way people live in their homes. Ginger Rabe “During Covid everyone was home all day. Now I design for what happens when people come home after being gone all day.” “The hardest conversation now is telling clients that what cost $50,000 five years ago might be $185,000 today.” “Sometimes the challenge of designing a luxury kitchen for $22,000 is actually fun—it forces creativity.” “I build kitchens around how people really cook, not how kitchens are supposed to work.” “Designers today are often the first people explaining what a project actually costs.” Kendra Araujo “Clients are overwhelmed by information now—our job is guiding them through the process.” “The price conversation is happening much earlier than it used to.” “People want their dream kitchen, but the cost realities have changed dramatically.” “We're constantly helping clients prioritize what actually matters most.” “There's so much analysis paralysis today that designers have become translators.” Kaylee Blaylock “Function comes first—our job is to make the kitchen work for the client before it looks beautiful.” “We start with questionnaires because every person in a household uses the kitchen differently.” “Appliances today allow us to personalize kitchens in ways we couldn't before.” “We're designing zones now—smoothie stations, coffee stations, prep areas.” “The kitchen has become much more individualized.” Taylor Troia “We usually start with appliances because they dictate the entire layout.” “Once clients understand their appliance choices, the kitchen design almost begins to solve itself.” “There are so many new appliance innovations that we're constantly learning.” “Travel and design shows open our eyes to things that haven't even reached the U.S. yet.” “Knowing what's possible globally helps us serve our clients better locally.” Rachel Moriarty “Covid activated more users in the kitchen—people learned to cook.” “I think about kitchens as stations—charcuterie stations, prep zones, cooking zones.” “Circulation patterns are the first thing we think about when designing a kitchen.” “Professional kitchen thinking is influencing residential design more than ever.” “The best kitchens are ecosystems where people can work without colliding.” Jules Wilson “We try to let clients talk first because what they say initially is always the most important.” “You learn far more by listening than by running through a checklist.” “Many younger clients have huge wish lists—but they're often unrealistic.” “Part of our role is helping clients narrow their priorities.” “Kitchen design today is as much about psychology as it is about layout.” Nate Fisher “Appliances have become central to how we design kitchens.” “Technology is evolving so quickly it's hard to keep up with everything available.” “Every cabinet now has a specialized insert or storage function.” “Clients want everything organized and hidden away.” “A clean kitchen visually creates peace in the home.” Concepts The Post-Covid Kitchen Shift Price Shock and the New Budget Reality Kitchen Zoning and Multi-User Design Appliance Innovation and Technology Personalization Through Storage and Organization Aging in Place and Accessibility Outdoor Kitchens as Lifestyle Extensions

Key  topics Impact of AI on retail customer experience Supply chain and logistics transformation through AI Measuring ROI and success metrics in AI deployments   Chapters 00:00 Introduction to Crescendo and AI in Customer Service 04:31 The Evolution of Customer Experience and Returns 09:33 AI Implementation Challenges and Expectations 14:09 Defining Customer Experience Across Channels 18:36 The Role of AI in Enhancing Customer Experience 26:06 Navigating AI's Potential and Realistic Applications 27:47 Transforming Customer Experience with AI 29:02 Personalization in Customer Engagement 30:59 The Evolution of Consumer Behavior 32:52 The Synergy of Digital and In-Store Experiences 34:39 Empathy in Customer Service 36:38 The Importance of Small Gestures 39:43 Lightning Round: Insights on AI and CX

Megan chats with Adam Sobel about advanced email personalization strategies that increase conversions and create a more tailored experience for your audience.   Adam Sobel is the chef and owner of The Cinnamon Snail, a vegan food truck, restaurant, and catering company serving New York and New Jersey. Adam has cooked at the James Beard House, represented the USA at the World Street Food Congress in the Philippines, and teaches cooking at the Institute for Culinary Education, De Gustibus Cooking School, and independently online. Adam has appeared on the food network, Cooking Channel, PBS, and several networks, and is the author of the popular cookbook Street Vegan.   If your email strategy feels flat or underperforming, this episode shows how to turn your list into a revenue driver. Personalization goes far beyond first names. It is about delivering the right content, offer, and message based on behavior and intent. This is the next level for bloggers ready to monetize smarter.   Key Topics Discussed: - Personalization should be based on behavior and preferences, not just names. - Segmenting your audience allows you to send more relevant content and offers. - Email sequences should adapt based on user actions and purchase history. - Selling becomes easier when offers match specific audience needs. - Testimonials and messaging should align with the reader's experience level. - Small optimizations in email flows can significantly increase revenue.   Guest Details Connect with Adam Sobel Website | Instagram

LEARN MORE at http://teach4theheart.com/383 Whether you are a veteran or a rookie teacher, planning and organization is key! There are so many teacher planners out there, but how do you know which one will actually work for you? Listen in as Linda and Sarah walk through what to look for, what to avoid, and how to choose a teacher planner that truly fits your style, schedule, and season of teaching! 00:00 Choosing the Right Teacher Planner 05:00 Digital vs. Physical Planners 08:26 Budget Considerations for Planners 15:44 Features and Personalization in Planners 22:28 Final Thoughts on Planner Selection Resources/Links Mentioned: Pray & Plan ( + Quiz! ): https://teach4theheart.com/planner  

Midjourney finally dropped v8.1, so Drew and Rory did what any responsible adults would do: generated way too many images, argued with style codes, stress-tested text, and immediately started asking whether the edit model is the part that actually matters.In episode 66 of Midjourney Fast Hours, the boys dig into why Midjourney v8.1 feels way better than v8, where it still falls short, and why this release feels less like a victory lap and more like Midjourney finally arriving at the version v8 probably should've been in the first place. They get into faster generations, native 2K output, mood boards, prompt depth, describe, personalization profiles, text rendering, image weight, --exp behavior, old v6 style-code weirdness, and the growing sense that the real make-or-break feature is still the edit model.They also get into how Midjourney stacks up against tools like Nano Banana, Grok, Reve, and Luma, why image generation still feels fragmented across platforms, and whether Midjourney should even bother chasing video or just go all-in on images, editing, and control.Then, because this is still Midjourney Fast Hours, the episode somehow ends with a deeply important discussion about custom Mac folder icons.If you care about Midjourney v8.1, prompting strategy, style references, AI image workflows, generative art tools, or where Midjourney is actually headed next, this one's got the goods.---⏱️ Fast Hour00:00 Intro and v8.1 arrives01:05 Is v8.1 actually better than v8?04:09 The edit model is the real test06:22 Should Midjourney even chase video?10:07 v8.1 needs more prompt depth12:07 Mood boards feel usable again14:28 Testing the new describe tool21:00 Personalization profile matters most22:09 Text tests and object recognition23:57 “Photo” vs art and stylize tests29:54 Why v8.1 feels a bit like v630:43 Old style codes hit differently now42:08 --sv7 issue and style-code confusion45:47 Missing parameters and what still works47:26 Hidden text tests and image weight49:57 --exp tests and behavior shifts52:35 Grid view and the alpha site54:50 Office Hours, 8.2, and edit timing01:03:27 Custom Mac folder icon detour01:09:01 Wrap-up and parting nonsense

Top Retail Expert Joshua Rockoff was joined by Yuliya Samsonava from EPAM and Vic Miles from Microsoft onsite at Shoptalk Spring to explore how agentic AI is being applied within luxury retail. In this episode, we dive into connecting data across systems, supporting decision-making in real time, and elevating the role of store associates. RETAILERS WILL GAIN INSIGHTS INTO:

Personalized ads outperform generic ones. But the effect is smaller than most marketers expect, and the hidden costs can quietly undercut your brand.This episode, Elena, Angela, and Rob dig into a meta-analysis of 53 studies on ad personalization and what the research actually says about when it works. They're joined by Chief Analytics Officer Matt Hultgren and Director of AI Audio Josh Wilson to discuss the real tradeoffs of personalization and why a new tool called the Mass Customizer could change what's possible for TV advertisers.Topics covered:[01:00] What the meta-analysis reveals about personalization's modest impact[03:00] The reach trap and why narrowing audiences shrinks mental availability[06:00] Why marketers overestimate audience differences[09:00] How to test personalization cleanly using geography and control groups[14:00] Traditional roadblocks in TV production when scaling creative versions[18:00] How the Mass Customizer uses AI to swap voiceover and graphics at scale[21:00] Why CTV breaks the false choice between mass reach and customization  To learn more, visit marketingarchitects.com/podcast or subscribe to our newsletter at marketingarchitects.com/newsletter.  Resources: Yeo, T. E. D., Chu, T. H., & Li, Q. (2025). How Persuasive Is Personalized Advertising? A Meta-Analytic Review of Experimental Evidence of the Effects of Personalization on Ad Effectiveness. Journal of Advertising Research, 65(4), 616–631. https://doi.org/10.1080/00218499.2025.2467763Get more research-backed marketing strategies by subscribing to The Marketing Architects on Apple Podcasts, Spotify, or wherever you listen to podcasts.

Get the full Salon Consumer Behavior Report HERE!In this episode of The Modern Hairstylist Podcast, host Hunter Donia and guest Jodie Brown share highlights from Hunter's 2026 Salon Consumer Behavior Survey, and this year's results are different. Instead of surveying general salon-goers, Hunter went straight to the clients of his Mastermind members, all stylists earning $100K or more, to find out exactly what high-paying clients expect, what keeps them loyal, and what makes them leave. This episode is for independent stylists who are already doing well and want to keep growing without guessing at what their ideal clients actually want.Hunter walks through three key findings from the survey and what each one means for how you show up in your business. You will also hear how to get the full report, including the complete results and tools to help you apply them.Key Takeaways:

What makes a simple branded mug, sweatshirt, or handwritten note unforgettable? Nikki welcomes promotional strategy expert Crissy Manwaring for a fascinating conversation on the deeper psychology behind swag and why meaningful brand experiences matter more than ever. Together, Nikki and Crissy unpack the science of sensory triggers, emotional connection, and thoughtful gifting, revealing how leaders can turn everyday items into powerful cultural touchpoints. From onboarding moments to milestone celebrations, they explore how intentional brand artifacts can strengthen human connection, reinforce belonging, and elevate the employee and client journey. If you lead people, build culture, or care about making your brand truly felt not just seen, this conversation will shift how you think about impact. Get ready to rethink swag as a strategic tool for meaningful work and meaningful lives.

Merriam-Webster named "slop" the 2025 word of the year - and it explains everything about where AI-generated content is heading. Elizabeth Maxson, CMO of Contentful, reveals the uncomfortable truth about AI adoption: 96% of CMOs say it's a top priority, but only 65% are making meaningful investments. The gap between enthusiasm and execution is costing teams momentum. She breaks down research conducted with The Atlantic, surveying 425 global marketing leaders. The findings expose why teams are stuck in experimentation mode, what top performers do differently, and why soft skills matter more than prompt engineering. We explore evidence-based creativity, the rise of the full-stack marketer, and simple personalization strategies that actually move the needle - including the geo-tagging change that increased event attendance by 51% and the Lorem Ipsum homepage trick that drove 250% more engagement. If you're tired of tools that promise speed but deliver sameness, this episode is your blueprint for moving from workslop to real strategy.   Chapters: 00:00 The Volume Trap: More Content Doesn't Equal Better Content 01:27 The Optimism-Execution Gap: Why CMOs Aren't Investing 10:05 AI Slop: When You Recognize Your Own ChatGPT Output 13:04 The Homepage Login Discovery 15:50 Why Simple Geo-Tagging Works 18:34 The Lorem Ipsum Homepage Experiment 19:29 Ruggable's Cat People vs. Dog People Strategy 28:00 The NASCAR Slide That Almost Went Wrong 31:17 Hiring Full Stack Marketers: Soft Skills Over Hard Skills 43:00 Course Correction Year: Two Pieces of Homework ----Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.