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  • Etsy Learns About Situational Markets and Customization

    Etsy Learns About Situational Markets and Customization
    2026/11/09

    The Experience Strategy Podcast

    Hosts: Aransas Savas | Joe Pine, author of The Transformation Economy | Dave Norton, PhD, author of Human Context: How Intelligent Companies Build Customers

    Episode Overview

    A Wall Street Journal article on Etsy's AI-powered personalization push gives the hosts a real-world case study in what happens when companies finally stop confusing purchase history with preference — and start thinking in situations. The results are hard to argue with: Etsy's sales are up 7.5% since they began making the shift.

    What We Cover

    What Etsy got wrong — and what they're fixing
    Etsy's recommendation engine was built on Bayesian algorithms that treated every purchase as a revealed preference. Buy a onesie, and the system assumes you must love baby gear. The obvious problem: most people buying baby gifts are not having babies. Etsy figured this out themselves and began reworking their algorithms to identify whether someone is shopping for themselves or for someone else — and to make recommendations based on that context rather than raw purchase history.

    Purchase history is not preference
    Joe draws a distinction worth holding onto: purchases can be one-offs, while preferences tend to persist. The two are not the same thing, and conflating them is what produces the kind of recommendations that make customers feel like the system simply isn't listening. Dave puts it plainly — most retailers have built their entire recommendation infrastructure on a faulty assumption.

    The situation Etsy is still missing
    Etsy is asking the right question — gift or self-purchase — but that is only the beginning. What the hosts push toward is the fuller situational picture: what mode is the shopper in? What is the occasion? What is the relationship between the buyer and the recipient? Those questions are not being asked yet, and the gap between where Etsy is and where they could be is significant.

    Spotify's approach as a signal
    Aransas flags a Spotify feature that inverts the usual dynamic: instead of inferring everything from behavior, it simply asks. Tell us what you like to listen to in the mornings. What are we missing? The survey feeds directly into the listening experience with no human intermediary. Joe reframes it: this is not a survey in the marketing research sense. It is a tool for understanding individual customers in real time.

    The Muse disruption on the horizon
    Dave brings the conversation back to the larger threat. Personal AI agents — Muse and those like it — are going to know each user's context in a way that Etsy, Amazon, and every other retailer cannot match from the outside. The agent will do the shopping. Amazon has already blocked Muse for exactly this reason. Retailers who are not building toward situational understanding now are not behind on a future problem — they are behind on a current one.

    Etsy's homepage tells the story
    Four of Etsy's seven navigation tabs are gift-oriented. Aransas notes that once you click into the gifts section, the site does ask who you are shopping for and for what occasion — which is real progress. But the entire frame assumes gifting as the default context, leaving significant surface area unaddressed for shoppers who are there for themselves.

    Key Idea

    If you can see that something is going to become a problem in the future, it is a problem now. The retailers who are treating contextual personalization as a roadmap item — rather than an urgent response to present disruption — are already behind.

    Resources Mentioned

    • Etsy Aims to Get More Personal and Boost Business with AI — Jennifer Williams, Wall Street Journal
    • Muse Is a Wake-Up Call to Any Customer-Facing Company — Dave Norton, The Experience Strategist on Substack
    • Human Context: How Intelligent Companies Build Customers — Dave Norton
    • The Transformation Economy — Joe Pine

    Continue the conversation at the Experience Strategy Substack. New episodes drop weekly.
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    16 分
  • Speed Puzzling, Midlife Pivots, and the Case for Dumb Solutions

    Speed Puzzling, Midlife Pivots, and the Case for Dumb Solutions
    2026/10/28
    The Experience Strategy Podcast | theexperiencestrategist.substack.com We rarely bring guests onto the show. But when Dave heard Jarrett King's story — a layoff, a national speed puzzling competition, and a new puzzle company built from scratch in 90 days — he knew the audience needed to hear it. This episode is part entrepreneurship story, part experience strategy case study, and part argument for why the most human solutions might be the ones with no intelligence in them at all. What's in This Episode Who is Jarrett King? A marketer and designer with a long corporate career who, after a role elimination in December 2024, found herself with open time and an old passion: jigsaw puzzles. She'd been puzzling since age five — her grandmother's kitchen table, beach vacations, COVID lockdowns. What she didn't know existed was competitive speed puzzling. How speed puzzling works. The USA Jigsaw Puzzling Association governs competitive speed puzzling and puts on a national championship. Pairs have one hour and fifteen minutes to complete a 500-piece puzzle. Solo competitors get an hour and a half. There are heats, state championships, and a national event — held in Atlanta, both last year and again this coming March. Jarrett found out about the organization in October 2024, went through a layoff six weeks later, and spent her holiday downtime doing what felt natural: training. The 90-day sprint. Starting January 1st — literally on New Year's Day, spreadsheet open, puzzle boxes open — Jarrett and her co-founder went from zero speed puzzling experience to competing at the Georgia State Championship in February. They finished in one hour and thirteen minutes, just under the wire. Two minutes to spare, next to a Delta Airlines corporate team. They were near last. They were thrilled. A month later at nationals, they didn't finish in time — but neither did 67 of the other 200 teams. And that failure launched the company. The puzzle design question that started a business. When you DNF at nationals because the puzzle was particularly hard, and you're a marketer and designer by training, you start asking: what actually makes a good puzzle? Not good for casual solving — good for competitive speed puzzling, where you're spending so much time with the art that the distinctions between pieces become critical. Jarrett's framework: color palette range (too many colors or too few both create different challenges), line clarity (hand-drawn thick lines versus blurry or muddy edges), and pattern and texture variation within similar color zones. What emerged was a product company built around getting these things right. No AI art. Ever. Borderline Obsessive Puzzles has made a public pledge: no AI-generated art in their puzzles. Their mission is to feature the work of human artists and creatives — and to support the artisans behind the physical product too, including the craftsman who hand-bent every piece of metal in the die that will cut their puzzles. Jarrett watched him work in person. The guy has spent decades learning that craft. That's what's behind the physical object. Why this is a perfect dumb solution — in the best sense. Dave brings it back to the framework from Human Context: puzzles are a dumb solution. No intelligence. No data layer. No sense-and-respond loop. And right now, that's a feature, not a limitation. There's a growing consumer release happening — a desire to set the screen down, engage differently, and experience something that is purely, beautifully human. Jarrett sees it in the speed puzzling community: Gen Z participants who are vocal about AI fatigue, gravitating toward analog activities that open creative pathways. The same reason companies used to bring fidget toys into brainstorming sessions — puzzles open neural firing that doesn't happen in front of a screen. Accumulated knowledge, applied in new directions. Aransas connects Jarrett's story to the research she's been conducting for Courage Capital — 200+ interviews with women who've made significant midlife pivots. The consistent pattern: they're not starting from scratch, they're applying decades of accumulated expertise in new domains. Jarrett brings design sensibility, marketing instincts, and consumer research habits to a product category where most competitors are just enthusiasts. That combination is hard to replicate. The fundamentals don't change. As Jarrett puts it: whether it's a digital solution or an analog one, the fundamentals are the same. Put the human at the center, and you get a better outcome. Dave's book was written with AI-powered companies as the primary audience — and here's a puzzle company with no technology at all demonstrating the same principle. About Borderline Obsessive Puzzles Jarrett and her co-founder are building a puzzle company grounded in thoughtful design, human-made art, and competitive-grade quality. Kickstarter campaign (live through November 18): kickstarter.com/projects/...
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    22 分
  • Time Value — The Measurement Framework Every Intelligent Company Needs

    Time Value — The Measurement Framework Every Intelligent Company Needs
    2026/09/24
    The Experience Strategy Podcast | theexperiencestrategist.substack.com Part four of a four-part mini-series unpacking the frameworks in Dave Norton's new book, Human Context: How Intelligent Companies Build Customers. You can build the right market strategy, identify the right situations, understand your customer's mode — and still not know if what you built is actually working. Time value is the measurement framework that closes that gap. In this final episode of the series, Dave explains the four categories of time value, how they map to the Stupid/Dumb/Smart/Genius framework, and why AI creates a compounding effect that changes what customers expect from every minute they spend with a product. What's in This Episode Why time value is the right measurement. NPS, sales lift, likelihood to buy — these are all useful, but they measure the company's outcomes. Time value measures the customer's outcome. When someone uses a tool, an app, a service, or an AI, what they're actually asking is: was this worth my time? That question has always mattered. Now, with AI compressing what used to take months into hours, customers' expectations for the value of their time are rising sharply — and companies that measure with yesterday's metrics will miss what's actually happening. The four categories. The framework has been part of Dave and Joe's work for nearly twenty years, and it's now a central chapter in Human Context: Time wasted — the experience consumed time and delivered nothing. The baseline failure case.Time well saved — the experience completed something faster than the alternative. Adjusting a thermostat with Alexa. Quick, functional, done.Time well spent — the experience was worth being in, beyond just efficiency. Engagement mattered. The person was present and got something from it.Time well invested — the time spent now pays dividends later. A skill developed, a relationship deepened, an outcome that compounds. Any experience — dumb, smart, or genius — can land in any of these categories. A pen and paper can be time well invested. A sophisticated AI tool can be time wasted. The category isn't determined by the technology; it's determined by the customer's experience of it. The compounding effect of genius solutions. With AI, there's a fifth dynamic layered on top: compounding. When a well-trained AI starts doing in hours what used to take months, customers don't just experience time saved — they experience something closer to a superpower. And that changes the baseline expectation. The more capable the tool appears, the less tolerance there is for failure. Dave's point: we need measurement frameworks that account for this — because evaluating a genius solution by the same standards as a dumb one misses what's actually at stake. Stupid is in the incident, not just the category. The episode's sharpest clarification comes through a live case study from the TV show The Pit: a hospital AI tool takes dictation and charts for the doctor, immediately freeing up time. But it mishears a drug name and records the wrong medication. Was it time wasted? Joe's framing cuts through the debate cleanly: an incident can be stupid even when the overall tool is smart. The doubter on the show sees the error and says the whole thing is risky. The loyalist says you still have to proof your work. Both are right in their own frame. Dave's conclusion: if errors like that happen repeatedly, the tool loses value. If it happens occasionally and the system learns from it, the tool gets better. The key variable is whether the person using it can train it. The trainability gap. Companies haven't caught up to what consumers now expect from AI tools. People who use Claude, ChatGPT, or Gemini daily have learned to train their tools — to shape responses, correct mistakes, build better versions over time. Then they open Copilot and find a system that can't learn the way they need it to. Dave's diagnosis: the doctor in The Pit should be able to flag the wrong drug and have the system remember. That's what closes the gap between smart and genius. And it's what most enterprise AI deployments are still missing. What's actually working right now. A quick round of AI tools the hosts are finding genuinely useful: A transcription app (beginning with G) that can handle multi-person conversations at a table, distinguishing speakers in real time.OpenAI's voice interface — Dave's son switched from Claude to ChatGPT specifically for this, and Dave says Siri now feels painful by comparison.Google's paid Workspace / Gemini integration — Aransas is getting smart email suggestions, natural-language search across past conversations, and contact introductions that pull in context automatically. Useful enough to feel slightly unsettling. Three questions for measuring dumb experiences. For solutions with no intelligence layer, Dave offers a simple diagnostic: Did I accomplish what I set out to do? How engaged was I with the ...
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    18 分
  • Modes — The Missing Variable Between Situation and Personalization

    Modes — The Missing Variable Between Situation and Personalization
    2026/09/18
    The Experience Strategy Podcast | theexperiencestrategist.substack.com Part three of a four-part mini-series unpacking the frameworks in Dave Norton's new book, Human Context: How Intelligent Companies Build Customers. Most companies build their entire models around habits and routines. But the number one behavior people exhibit today isn't a habit or a routine — it's mode acquisition. People move fluidly between modes all day, they're fluent in the language of them ("I'm in writing mode, don't talk to me"), and they expect the companies they interact with to understand where they are. Most companies don't. This episode explains what modes are, how they connect to situational markets, and why knowing someone's mode tells you more about how to serve them than any demographic ever could. What's in This Episode The definition that matters most — and it's one word. A mode is a mindset and a set of behaviors that a person gets into temporarily. That last word is what separates modes from habits (permanent patterns) and routines (permanent structures). Right now, the three hosts are in podcast mode. When they hit stop, they won't be. Joe takes a different tone between episodes. Aransas's voice changes. Dave gets Joe's "I'm in writing mode" texts. People are already living this way — the framework just names what's happening. The ballpark and the batter. To connect modes back to the last episode: if situational markets are the ballpark — the bounded context, the market you're going after — then modes are the batter. The same situation can contain many different modes. Two people can both be in the situation of choosing a college for their daughter. One is in full mommy mode — relational, emotionally loaded, cross-domain in its implications (financial, personal, professional, aspirational). Another is an adult returning to education for themselves, in a more self-directed, pragmatic mode. Same situational market. Completely different mode. Completely different support needed. Joe's extension makes it even richer: everyone on the field is in a different mode at every moment. The batter stepping up with a runner on third is in a different mode than when he's in the dugout. And if you know someone's in dugout mode — Dave's point is clear — sunflower seeds are your product. Modes cross demographics. That's the whole point. Aransas in mommy mode could be any age, any income, any geography. The mode — not the demographic — tells you what she needs. Joe names the full stack of jobs that mode carries with it: functional, emotional, social, and aspirational. Know the mode, and you know the context. Know the context, and you can personalize in a way that demographic data never allows. How modes, situations, and jobs connect. The three frameworks work together as a system. Jobs to be done describes what the customer needs broadly — the functional, emotional, social, and aspirational outcomes they're hiring for. Situational markets describe the bounded context: the when-statement, the market that's been identified and sized. Modes describe how — the specific mindset, energy, and intention the person brings to that moment. Dave's summary: situation tells you the market, mode tells you how they're experiencing their time within it. Know all three, and you have context that's genuinely actionable. Why modes replace personas — and why that matters. Personas imply a static, monolithic person. Modes are the opposite: dynamic, contextual, temporary. A person is not their persona. They are whoever they are in this moment, in this situation, in this mode. That concept changes what you build, what you say, and how you show up. The mode map in Human Context gives companies a practical tool: map your specific situations to the modes customers are likely to enter, and build for those combinations rather than for a type of person. Most companies are leaving this on the table. Dave has been developing the modes framework for over a decade. He remains genuinely surprised that more companies don't use it — especially now, when the language of modes is so embedded in how people describe themselves. The person who texts "I'm in writing mode" is handing you a direct signal about their context. Most companies have no system for receiving it. Get the Book Human Context: How Intelligent Companies Build Customers by Dave Norton is available now on Amazon (Kindle and print) and on Dave's Substack. The book includes a mode map framework and industry-specific workbooks showing how to apply modes, situational markets, and the other frameworks to your category. Find the hosts by name or as The Experience Strategist at theexperiencestrategist.substack.com.
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    16 分
  • Situational Markets — The Framework That Replaces Traditional Market Strategy

    Situational Markets — The Framework That Replaces Traditional Market Strategy
    2026/09/11
    The Experience Strategy Podcast | theexperiencestrategist.substack.com Part two of a four-part mini-series unpacking the frameworks in Dave Norton's new book, Human Context: How Intelligent Companies Build Customers. Every time you segment your target audience by who they are, you limit the number of people who could buy from you. That's the uncomfortable truth at the center of this episode — and the reason situational markets may be the most practically disruptive framework in Human Context. Dave and Joe trace the idea from Joe's original "markets within" concept through to how AI is already forcing companies to make this shift whether they're ready or not. What's in This Episode Where the idea came from: Joe Pine's "markets within" framework. Joe developed a framework he calls the bow tie — narrow in the middle, wide on both ends. On the left is the traditional marketing view: a company has a market, which it segments into smaller groups. On the right is the insight that flips everything: there are multiple markets within each individual customer. We want different things at different times, in different situations, in different contexts. The pivot point — where the bow tie narrows — is the recognition that every customer is their own market. Situational markets is Dave's extension of that idea into something companies can operationalize. The Amazon account problem as a live demonstration. Joe and his wife share an Amazon account. She buys Kindle fiction. He buys business and theology books. Amazon treats them as one customer and gets it wrong for both of them — recommendations that belong to one land with the other, constantly. Dave's point: even without the shared account problem, you and Joe are essentially the same demographic. Similar age, same household. Classic targeting tools wouldn't distinguish you at all. That's exactly what's broken. The situation — who's shopping, for what, in what mindset — is the only thing that actually tells you something useful. Every time you segment the "who," you shrink the market. Dave landed on this as one of the book's most clarifying insights. Six demographic segments means six defined targets — and an unknown number of people excluded because they show up in the situation sometimes but don't fit the profile always. Situational markets are inclusive by design: anyone who finds themselves in the situation is a potential customer. You're not narrowing to a type of person. You're identifying a moment that any type of person can enter. This is what Clayton Christensen was pointing at. Dave connects situational markets to Christensen's "Marketing Malpractice" — the argument that the job, not the customer, is the unit of analysis. Christensen developed this through Jobs to Be Done. Dave's situational markets framework arrives at the same threshold from a different direction: stop asking who your customer is, start asking what situation they're in. The analytical tools that worked for demographic segmentation — sizing, prioritization, value creation — all transfer. The difference is you're no longer excluding people who don't fit a profile. What a situational market actually is. Three elements: A when statement. "When I am making a hotel reservation." "When I want restaurant-quality pizza at home." "When I want water at a concert but don't drink alcohol and want to look cool." The when statement creates a boundary around the context — something specific enough to design for.The who stays generic. As generic as possible. The situation defines the entry point. Don't pre-filter by demographic.Size it. A situation isn't a market until you can size it. It also needs to be a situation the customer actually wants to hire you for — not just something that arises. Dave spends significant time in the book on how to size situational markets, because that's what separates a use case from a genuine market opportunity. Three types of situations. Obvious situations — making a hotel reservation, back-to-school shopping, holiday gifting — are already well-understood and competed over. Less obvious situations are where differentiation lives. And then there are externally-driven situations: COVID is the clearest example, a time-bound, universal situational market that few companies were built to serve and even fewer anticipated. The examples that make it concrete. Liquid Death — built entirely around the situation of wanting water at a concert without alcohol and without a plastic bottle, while still looking like you fit in. The situation defined the product, the design, and the brand.Ooni pizza ovens — when I want restaurant-quality pizza at home. Aransas's read: it's less about the pizza than about looking like you know how to make one. Either way, the situation is specific, sizeable, and worth designing for.Google Search — what if Google had decided academics were the primary users and targeted accordingly? Instead, they said: whenever someone ...
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    16 分
  • Stupid, Dumb, Smart, Genius — The Framework That Tells You When and How to Use AI

    Stupid, Dumb, Smart, Genius — The Framework That Tells You When and How to Use AI
    2026/08/27
    The Experience Strategy Podcast | theexperiencestrategist.substack.com Part one of a four-part mini-series unpacking the frameworks in Dave Norton's new book, Human Context: How Intelligent Companies Build Customers. The Stupid/Dumb/Smart/Genius framework has been in use with clients for nearly a decade. It was born in a room in Burbank, in real time, when Joe Pine found himself in the back of a client workshop and Dave told the client that Joe would have a new framework for them. This episode tells that origin story, walks through how the framework works — with examples from glasses to healthcare — and explains why it's never been more relevant than right now. What's in This Episode How the framework was born. Joe and Dave were working with a media company in Burbank. Dave had mapped out consumer insights across a whiteboard — rows, columns, the full picture. Joe, watching from the back of the room, took it as a prompt to build a two-by-two on the spot. The dumb-versus-smart contrast came from the conversation itself: broadcasting is dumb — it sends out a signal with no feedback loop — and the client knew they needed to move toward smart but weren't sure how. From there, the natural extension followed: if dumb has a worse version, it's stupid (intelligence used against the consumer). If smart has a better version, it's genius (intelligence that truly knows and serves the individual). The framework has been a core part of Dave and Joe's client work ever since, and it's now a central chapter in Human Context. What each level actually means: Dumb — no context is being collected or used. Not a pejorative. A pair of glasses that helps you see is dumb. A family doctor who knows your history and gives good care is practicing dumb medicine. The product or service does exactly what it's supposed to do, without any data layer. There will always be a market for dumb — and increasingly, it may feel like a relief. Smart — an iterative sense-and-respond loop. The system collects some data and adjusts. Your streaming service tracking what you watch and making recommendations is smart. Glasses with an audio component connected to your phone are smart. Genius — anticipatory, fully contextual, deeply personalized support. The system knows your history, your situation, your mode, and acts on your behalf before you have to ask. Dave uses the example of a health AI that knows your supplements, your medical history, and the fact that you're going golfing today — and surfaces exactly what you need to know, without a doctor's visit. Meta smart glasses are a current candidate, though the jury's still out. Stupid — intelligence used adversarially against the consumer. Getting in the way of what someone is trying to do. Collecting and deploying data in ways that feel invasive or serve the platform instead of the person. Google Glass — where users were famously called "Glassholes" — is the canonical example. Joe's live case study: Amazon Prime serving him ads for Lioness every single episode, for a show he's already watched in full. That's not smart personalization. That's stupid. The same technology, all four levels at once. The glasses example makes the framework concrete: regular glasses are dumb (and excellent at it). Hearing-aid glasses or phone-connected frames are smart. Meta smart glasses are approaching genius. Google Glass was stupid. One product category, four distinct orientations — and a viable business model for at least three of them. Healthcare is the most important application. The dumb version: fill out the same form you filled out last time, see the doctor, get the prescription. Not bad care — just no data layer. The critical gap Joe identifies: no one has the full picture of what you're taking. Your GP, your specialist, your rheumatologist — each knows their piece. Nobody has the whole context, including your supplements, which means drug interactions go undetected. The genius version: a health AI with your complete medical history, supplement stack, and daily context — flagging what you need to pay attention to before you even call the office, with the doctor as a periodic checkpoint rather than the first point of contact. Consumer expectations will catch up to this. When they do, dumb healthcare won't feel bad — it'll feel retro. The consumer's rising expectations change the baseline. As genius becomes standard, what felt cutting-edge becomes expected, and what was once acceptable starts to feel quaint. Dave's point: companies need to be building toward genius now, because the window in which it's a differentiator is shorter than it looks. This Is Part One of Four The next three mini-episodes unpack the remaining frameworks from Human Context: Part 2: Situational MarketsPart 3: ModesPart 4: Time Value Get the Book Human Context: How Intelligent Companies Build Customers by Dave Norton is available now on Amazon (Kindle and print) and on Dave's Substack. The ten ...
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    14 分
  • Dave Norton's New Book Is Out — Human Context and the Paradigm Shift Every Company Needs to Make

    Dave Norton's New Book Is Out — Human Context and the Paradigm Shift Every Company Needs to Make
    2026/08/12
    The Experience Strategy Podcast | theexperiencestrategist.substack.com It's a book launch episode. Human Context: How Intelligent Companies Build Customers — Dave Norton's new book — released yesterday on Amazon. Joe and Aransas have read the galley. This episode covers the why now, the four frameworks at the book's core, and how companies are meant to actually use it. What's in This Episode Why Dave felt compelled to write this now. It goes back to 2015 and Digital Context 2.0, his first book, in which he argued that context would be the organizing logic that brought channels, tools, and technologies together in service of the human. It didn't quite unfold that way — companies didn't want to share data, API ecosystems were slow to develop, and the consumer-enabling vision got stuck. Then generative AI arrived and changed everything. Context is now the literal engine that makes LLMs work. But there's a problem: every AI company in the world is thinking about context as their asset — something to extract from consumers and use against them to sell more. Dave wrote Human Context to plant a stake in the ground against that direction. The book argues that context belongs to the individual, and intelligent companies will build around enabling people rather than extracting from them. The two definitions of context — and why they matter. Joe sharpens the distinction that runs through the whole book: AI companies think of context as the model's context — the information fed into the system to make it work. What the book argues is that companies need to think about the customer's context — the situations, mindsets, and life circumstances of the individual they're serving. These are not the same thing, and confusing them is how companies end up building technology that serves the platform instead of the person. The four frameworks. The book is organized around four interconnected frameworks: Stupid/Dumb/Smart/Genius — co-developed with Joe Pine in 2016 and referenced in Dave's 2023 HBR article, this framework helps companies understand when and why to use intelligence. Dumb means no context is being collected — not a pejorative, just a description of the state. Smart means iterative sense-and-respond. Genius is what generative AI has now made genuinely possible — anticipatory, deeply contextual support. Stupid is when intelligence is turned adversarially against the consumer: wasting their time, getting in the way, or pursuing nefarious motives.Situational Markets — the move away from demographic targeting toward situations. What people have in common is not age or income bracket — it's that they find themselves in common situations. Companies that learn to identify, size, and design for situations are already operating at a different level of contextual sophistication. Demographics, as Joe puts it memorably, tell you nothing — a point he illustrates with the famous example of two people born the same day in the same town, one of whom became King Charles and the other Ozzy Osbourne.Modes — the mindset and behavioral state a person is in at a given moment. Combined with situation, modes give a company a genuinely rich picture of what the individual needs right now. Dave has been developing this framework for twelve years.Time Value — the measurement framework. How do you evaluate a dumb experience using time value? A smart one? A genius one? The book distinguishes between time saved, time well spent, and time well invested — giving companies a way to think about what their interactions are actually delivering. Demographics and personas: a gentle funeral. Joe makes the case cleanly. Demographics are shifts in bell curves — everyone is somewhere on every curve, and they tell you nothing about what any individual values or needs. Personas were a useful intervention when product designers needed reminding that real humans would use their products. But reducing a person to a persona is an injustice to both the individual and to the company's own capability. The frameworks in Human Context are designed to replace both. How the book is meant to be used. Dave is unapologetic: the book introduces the concepts, but the real work happens in the workbooks. Ten industry-specific workbooks accompany the book — covering how to size situational markets in your category, how to map modes, how to apply the frameworks to your actual business model. There's also a case study. The core ask of the reader: spend real time asking what it would actually take to move away from demographic thinking and start sizing situations instead. How Dave wrote it — and why provenance matters. Every chapter started as Dave's own writing, in some cases material he'd been developing for over a decade. AI was used to synthesize, consolidate, and accelerate — combining multiple drafts, simplifying, and then editing back to his voice. He's transparent about this in the book itself, with a dedicated provenance ...
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    26 分
  • Healthcare Is Coming Back — And It's Getting More Human

    Healthcare Is Coming Back — And It's Getting More Human
    2026/07/22
    The Experience Strategy Podcast | theexperiencestrategist.substack.com A nurse whose only job was to hold a patient's hand during a procedure. A debrief with the doctor scheduled before the procedure was even booked. A title accidentally revealed mid-conversation. This episode covers a lot of ground — starting with where healthcare experience strategy stands right now, and ending somewhere that a certain author probably wasn't expecting. What's in This Episode Healthcare is recovering — and the investment is back. After a brutal five-year stretch that left providers burned out and hospital systems in survival mode, Dave sees real momentum returning. Capital is flowing back into healthcare, and what's different this time is a more mature understanding of where technology fits and where it doesn't. AI handling clinical note-taking is the clearest near-term win — freeing physicians from the documentation burden that was eating their limited time with patients. Longer term, the new generation of LLMs built for scientific discovery is accelerating treatment development in ways that weren't possible even three years ago. The problem with scaling human experience. Mayo Clinic and Cleveland Clinic were early adopters of design thinking — writing case studies on patient experience in the 2000s that the whole industry studied. But the business model kept pulling in the other direction: enormous capital expenses, opaque insurance structures, and the relentless pressure to grow. And as Dave puts it, when you scale up a healthcare system, individualized experience gets harder, not easier. That's always true in any category — but the stakes are higher in healthcare. Then a pandemic arrived and survival became the only goal. The better the patient experience, the better the outcomes. Joe has been saying this for years, and the research backs it. The insight is simple but organizationally difficult: healthcare is not a service business. It uses experiences, but it's fundamentally in the transformation business. Every patient walking through the door has an aspiration — some version of going from sick to well. That aspiration, and the experience designed around it, drives outcomes. Geisinger Health System has operationalized this through outcome-based pricing: knee replacement doesn't work, you don't pay. More systems are moving in that direction. Human needs versus patient needs — there's a difference. Aransas's experience at Memorial Sloan Kettering is the episode's anchor story. A procedure booked with a debrief appointment scheduled at the same time — eliminating the anxiety window between test and result. And a nurse whose sole role during the procedure was comfort: one hand on Aransas's hand, one hand on her shoulder. Joe's reframe lands hard: "They didn't just meet your patient needs — they met your human needs." The distinction matters. Patients are still too often seen as collections of symptoms. The shift toward the whole person is coming, but it's uneven. AI's real job in healthcare: offload the routine so humans can be human. The most useful frame for AI in any service category — and healthcare in particular — isn't automation for its own sake. It's freeing the human in the room to be fully present. Checklists, documentation, protocol verification: these are exactly the kinds of cognitive load that drain providers and crowd out the relationship. Aransas makes the point that the "which leg are we operating on?" verification ritual exists because it was a real risk. The goal is to use operations and AI to cover the routine, so providers can put their energy into the part that only humans can do. Trained empathy has a shelf life. Dave traces the arc from "Welcome to Wachovia!" — a scripted greeting that felt like cutting-edge hospitality in its day — to the present moment, where scripted warmth reads as inauthentic almost immediately. Rote empathy, whether from a human or an AI trained to flatter, produces the same result: it rings hollow. Consumers have been through enough now that they can tell the difference. Joe's COVID-era conclusion still stands: "Be human." That's not a soft directive. In an environment where AI handles more and more of the transaction, genuine human presence becomes the differentiator. The tools that made experiences more human are showing their age. Persona building. Journey mapping. Design thinking. These were genuinely useful frameworks, and the industry built real capability around them. But Dave argues they're no longer sufficient. The question isn't how to design a better map — it's how to build what he's calling intelligent experiences: a new framework for the human interface that fits the current environment. He's writing about it now. Referenced Memorial Sloan Kettering — patient experience design as a model for the categoryGeisinger Health System — outcome-based pricing for knee replacement proceduresWachovia Bank — early scripted greeting...
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