Customer feedback is the most reliable compass I have for product strategy and execution. Over the years leading product at HighLevel, I’ve built and refined a system that turns raw signals from users into clear, prioritized decisions our teams can confidently ship.
A practical guide to collecting and using product feedback in product management (from AI tools to early-stage tactics) for better product decisions.
My playbook starts with continuous discovery. I keep a steady flow of insights from sales calls, customer support threads, community forums, and in-product behavior so I can triangulate patterns rather than chase loud anecdotes. This mix of quantitative and qualitative data helps me separate urgent noise from strategically meaningful trends.
On the quantitative side, I rely on product analytics to ground the conversation. Amplitude analytics gives me activation, retention cohorts, and feature engagement, while controlled experiments and A/B testing validate whether an idea actually moves a target metric. Tying these signals to specific customer segments helps me see where product-led growth is working—and where it’s stalling.
For qualitative insight, I combine in-app guides and lightweight surveys (via tools like Pendo) with structured interviews and support escalations (often surfaced through platforms like Intercom). I map problems using the Kano Model to understand which requests are basic expectations, which are performance drivers, and which are potential delights. This keeps our roadmap focused on outcomes, not just outputs.
AI now accelerates the synthesis step. With LLMs for product managers in my AI product toolbox, I summarize interview transcripts, cluster themes across thousands of notes, and quantify sentiment without losing nuance. I still review raw artifacts to avoid hallucinations and preserve context, but AI reduces the time from signal to insight dramatically—freeing me to spend more energy on judgment and storytelling.
In early-stage contexts, I bias toward speed and proximity to users. I schedule founder- or PM-led discovery calls weekly, instrument product tours early, and launch scrappy in-product prompts to validate demand before over-investing. When data is sparse, I focus on high-signal channels (power users, churned customers with qualified use cases) and document crisp problem statements that connect directly to activation, retention analysis, and revenue outcomes.
Prioritization ties everything together. I translate insights into hypotheses aligned to outcomes vs output OKRs, then pressure-test them with feasibility and strategic fit. We run small, measurable experiments, track deltas in activation and retention, and adjust the product roadmapping and sprint planning cadence based on what the data and customers teach us.
This approach builds trust with stakeholders and creates empowered product teams. By grounding decisions in a transparent trail of feedback, analytics, and experiments, we reduce thrash, move faster, and—most importantly—ship product moments that customers value.
If you’re refining your own feedback engine, start by instrumenting the basics, set a weekly discovery rhythm, and let AI handle the heavy lifting on aggregation and synthesis. The compounding effect is real: better insights lead to better bets, which lead to better outcomes for your users and your business.
Every year, I circle Pendomonium on my calendar because it reliably delivers the perfect blend of strategy, execution, and community. It’s where product leaders, builders, and operators compare notes on what actually moves activation, adoption, and retention—and where I pressure-test my roadmap and go-to-market assumptions against real-world data and peer experience.
Pendomonium is a product festival by Pendo in downtown Raleigh. Get answers to all your questions about the best product festival of the year.
From a product management leadership lens, the value is clear: Pendomonium is a concentrated learning loop for product-led growth. I come to deepen my craft around in-app guides, onboarding flows, user activation, and product tours—then translate those insights into roadmap bets and experiments my product trios can execute immediately.
Why attend? First, signal over noise: the sessions focus on measurable customer behavior and practical playbooks, not vague inspiration. Second, community: the hallway track and roundtables are some of the best conference networking moments in our field. Third, clarity: I leave with sharper product strategy, a prioritized backlog, and a short list of experiments to validate with customers.
If you’re a first-timer, arrive with intent. Define two or three outcomes you want—such as improving onboarding completion, increasing feature adoption, or tightening product roadmapping and sprint planning—and build your agenda around those goals. Star sessions on product discovery, product strategy, and hands-on Pendo use cases like in-app guides and product tours so your notes translate into immediate action.
Make the most of the community. Treat the hallway track like a scheduled session: set a goal to meet ten peers, bring a crisp introduction, and ask concrete questions such as, “What measurable behavior change did your in-app guide drive?” or “Which activation metric mattered most for your last launch?” Swap templates and dashboards, and follow up within 24 hours while context is fresh.
Logistics matter more than most people admit. Downtown Raleigh is walkable, but high-demand sessions fill quickly—arrive early, wear comfortable shoes, and keep a portable charger handy. Schedule buffer time between talks to debrief, review notes, and have serendipitous conversations with the Pendo team and practitioners who can deepen your approach.
Capture, then operationalize. I use a simple note structure: Insight → Hypothesis → Experiment → Metric. Turn session takeaways into tests (for example, variations of onboarding checklists or empty-state prompts) and define success criteria in advance. Align those experiments with your OKRs and use QBRs to review outcomes, ensuring what you learned at the festival translates into measurable product impact.
Post-event, run an internal readout within a week. Demo two applicable ideas, propose a 30-60-90 day experiment plan, and tie each initiative to a customer behavior metric such as time-to-value, daily active usage, or feature adoption. This is how Pendomonium goes from inspiring to invaluable—by turning insights into shippable, testable work that advances your strategy.
If this is your first Pendomonium, expect high energy, candid conversations, and a wealth of practical tactics you can apply immediately. I’ll be there comparing notes, learning from peers, and sharing what’s worked—and what hasn’t—in scaling product organizations. If you spot me in a session on activation or onboarding, come say hello.
Every quarter, I watch product teams move from gut feel to data-informed decisions—until instrumentation bottlenecks slow them to a crawl. That’s why I’ve become an advocate for codeless analytics: it removes the dependency on engineering sprints for basic event tracking and lets teams answer product questions in hours, not weeks.
We explain what codeless analytics are, why (and how) Pendo supports them, plus responses to the top three myths about low-code/no-code solutions.
Here’s how I frame it with my teams: codeless analytics enables product managers, designers, and customer success to tag features visually, track interactions, and analyze adoption without shipping code. The goal isn’t to replace engineered events; it’s to accelerate discovery, speed up iteration, and reduce context-switching for developers. In practice, this means cleaner prioritization, faster validation of hypotheses, and tighter product-led growth loops.
Why Pendo? In my experience, Pendo’s codeless model shortens the distance from question to insight. Visual tagging makes event setup accessible, in-app guides and product tours let us experiment with onboarding and activation, and governance controls ensure data remains trustworthy across teams. The result is a unified analytics approach where we reserve custom instrumentation for complex logic while using codeless tracking for everyday product questions.
Let’s address the top three myths I hear most often. Myth 1: “No-code is only for simple use cases.” In reality, most decisions we make weekly—feature adoption, path analysis, funnel drop-offs, and retention analysis—do not require custom code. Codeless analytics handles these well, and when we need deeper context (like server-side events), we complement it with engineered tracking. It’s a both/and, not an either/or.
Myth 2: “Codeless data isn’t accurate.” Accuracy comes from governance, not the method. I set clear standards: naming conventions, tagging reviews, ownership, and periodic audits. With disciplined process, codeless tracking yields consistent, decision-grade data. The added benefit is visibility—non-technical stakeholders can validate the instrumentation themselves, reducing misalignment.
Myth 3: “Engineers must instrument everything to scale.” Engineering time is precious; we should spend it on differentiated capabilities, not on routine click tracking. Codeless analytics scales by empowering product teams to self-serve, while engineering focuses on back-end, performance, and edge cases. When paired with a unified analytics platform and clear data contracts, this model scales cleanly across product lines.
For teams adopting this approach, I recommend a simple operating model: define your core product questions up front, tag features aligned to those questions, connect insights to in-app guides for experiments, and measure user activation and retention continuously. Whether you run Pendo alongside Amplitude analytics or within a broader unified analytics platform, the key is to keep the insight-to-action loop tight.
The future of product analytics is codeless because it puts insights where they belong—directly in the hands of the people designing the experience. When we remove bottlenecks, we learn faster, ship smarter, and drive measurable PLG impact. That’s how we turn product analytics from a reporting function into a competitive advantage.
Trust is the currency of any high-stakes AI product, and nowhere is that more true than in healthcare. I recently dug into how Healio built an AI assistant for physicians—an audience that can’t afford to be wrong—and it’s a masterclass in balancing accuracy, transparency, and speed without compromising credibility.
Healio, a 125-year-old medical publishing company, set out to create Healio AI to help clinicians prepare for patient care. From the outset, their guiding principle was simple: physicians won’t trust you until you prove it. That lens shaped every decision—from discovery and prototyping to architecture, evaluation, and ongoing validation.
Discovery started with a survey of 300 healthcare professionals to understand real-world needs at the point of care. The headline insight: physicians primarily want AI for preparation, not bedside use. Even more surprising, the top ask wasn’t purely diagnostic support; it was help with patient communication and empathy—translating complex information into clear, accessible conversation.
Momentum mattered. After beginning with Figma mockups to validate workflows, the team built a working prototype in a single weekend using Cursor. That velocity wasn’t about cutting corners; it was about proving value quickly, reducing ambiguity, and iterating with concrete feedback from physicians.
Under the hood, the system employs RAG and hybrid search—combining lexical search, vector search, and semantic search across multiple trusted sources like PubMed. As any PM who has integrated biomedical literature knows, "just use PubMed" isn’t simple—there are five different ways to access the same data, each with trade-offs. The team made pragmatic choices to balance freshness, coverage, latency, and cost while preserving trust in source quality.
Designing for trust extended all the way to the citation UX. The team leaned into citations that physicians actually trust: subscripts, hover states, and progressive disclosure. This gave clinicians verifiable threads back to source material without overwhelming the core interaction, aligning with how experts want to audit evidence under time pressure.
Evaluation wasn’t left to chance. They stood up eight LLM judges for evals: safety, medical accuracy, faithfulness, relevancy, completeness, reasoning, clarity, and overall quality. Just as importantly, they treated those signals as directional, not definitive. In a high-stakes domain, physician feedback trumps LLM-as-judge feedback—so they complemented automated evals with direct reviews from practicing clinicians to calibrate quality and reduce hallucinations.
On the safety front, the team implemented HIPAA compliance and input guardrails for masking personal health information. That choice reflects strong data governance and privacy-by-design thinking: protect PHI by default, constrain prompts to safe boundaries, and make compliance a first-class citizen in the product architecture.
They also addressed monetization without compromising experience. Serving contextual ads while the LLM processes queries is a practical approach that preserves physician workflow efficiency and creates a clear, non-intrusive revenue model.
Critically, the work didn’t stop at launch. The Healio Innovation Partners provide ongoing discovery and validation, ensuring the system evolves with physician needs and the medical evidence base. This is the operating cadence you want for any AI product that sits at the intersection of safety, accuracy, and fast-changing knowledge.
My takeaways for building AI in high-stakes domains: prioritize retrieval-first pipelines over model cleverness; couple RAG with hybrid search across vetted sources; design citations that earn trust at a glance; use eval-driven development, but let domain-expert feedback be the ultimate judge; and embed regulatory compliance into your product strategy from day one. If trust is your North Star, this is a playbook worth emulating.
I just tuned into the latest conversation on the upcoming Product at Heart 2026, and it hit on the exact challenges product leaders are navigating right now: curating meaningful content in a world where AI moves faster than our agendas, designing formats that create real connection, and ensuring every minute earns its place. Listening to Petra Wille and Teresa Torres map out the speaker lineup, workshops, and structural shifts, I found myself nodding along—this is the kind of thoughtful curation we need if we want product teams and product leaders to walk away with practical value, not just inspiration.
Listen to this episode on: Spotify | Apple Podcasts
What stood out immediately is the bold move to a single-track conference for 2026. In an era of gen ai hype and endless breakouts, this choice signals clear intent: tighter curation, a shared experience, and less FOMO. The team isn’t carving out a separate AI track—and I love that decision. Their stance is simple and sensible: No AI track—AI will show up everywhere, but not as a siloed topic. The team sees it as part of the everyday toolkit. That mirrors how high-performing, empowered product teams actually work today—AI Strategy and AI workflows are part of the operating system, not a side show.
The keynote lineup is already compelling. Christian Idiodi (SVPG) brings storytelling that turns product principles into habits you can actually use on Monday. Elaine Kasket, cyber-psychologist, exploring digital afterlife and AI replicas, will push us to think more deeply about the human side of our systems. And Teresa Torres will be sharing what she’s learning about AI—exactly the kind of continuous discovery mindset we need as we integrate LLMs into product discovery and delivery.
I’m also thrilled to see roundtables become what they’re calling an “alternative track.” That’s a smart way to deepen learning without fragmenting attention. The best conference ROI I’ve had often comes from targeted small-group conversations—where product trios compare approaches, swap metrics frameworks, or challenge each other’s product strategy assumptions. It’s a design choice that rewards curiosity and builds communities of practice.
We also get a behind-the-scenes look at Teresa’s Maker Studio workshop, where participants will build personal AI workflows. That’s exactly the hands-on, practitioner-first approach teams need right now—less demo theater, more systems that stick. If your roadmap includes integrating LLMs into continuous discovery or augmenting your team’s decision velocity, this kind of guided practice is gold.
The broader workshop slate looks deep and balanced. Expect returning favorites and practical frameworks: Rich Mironov on the realities of product leadership in complex orgs; Büşra’s metrics workshop translating outcomes into action; and an overview of additional workshops from Rich Mironov, Büşra Coşkuner, Marcus Castenfors, and Özlem Yüce. From success metrics to toolkits for product managers, the content spans IC to product management leadership—ideal if you’re stepping into new roles or scaling empowered product teams.
One of the most exciting evolutions is the Product Leadership Event, now a 1.5-day retreat. The format blends talk sessions, mini-workshops, dinners, and small-group excursions (boat rides, improv, etc.), giving leaders time and space to exchange playbooks, stress-test decisions, and build real relationships. It’s capped at 60 attendees (all in product leadership roles) to keep it intimate and useful. As someone who believes in outcomes vs output OKRs and first principles decision making, I appreciate how this structure encourages depth over breadth—and real accountability among peers.
Here are the core takeaways I’m carrying into my own planning: single-track means tighter curation, so every talk has to earn its place. Roundtables are growing into an “alternative track,” offering more ways to engage beyond stage talks. Workshops go deep and meet you where you are—IC, manager, or executive. And the leadership retreat expands to maximize learning from peers, not just from the stage. If you care about product discovery, product strategy, and conference networking that leads to actual business impact, this program looks thoughtfully engineered.
If you’re planning your 2026 calendar—or just curious how conferences evolve alongside the craft—this is a thoughtful walkthrough of what to expect. Come say hi to Teresa and Petra—on stage, at a roundtable, or somewhere in the hallway conversations that make these events memorable.
For more context and resources mentioned, explore: Product at Heart, Arne Kittler, Mind the Product, Christian Idiodi of Silicon Valley Product Group, Elaine Kasket, House of Beautiful Business, The 7 Habits of Highly Effective People by Stephen Covey, Rich Mironov, Marty Cagan, Claude Code, Codex by OpenAI, Marcus Castenfors, Büşra Coşkuner and her Success Metrics: A Playbook for Product Managers, Özlem Yüce’s Essential Toolkit for Product Managers, Petra’s Product Leadership Wheel (PLwheel), and Netlight.
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Full transcripts are only available for paid subscribers.
I’ve led product teams through countless discovery cycles, and nothing has accelerated our learning loops like AI. By weaving AI into our continuous discovery practice at HighLevel, I cut time-to-insight, reduce risk earlier, and keep our product strategy relentlessly focused on customer outcomes.
AI streamlines product discovery by accelerating research, prototyping, and validation, enabling teams to make faster, smarter, and user-driven decisions.
In the research phase, I use gen ai and LLMs for product managers to synthesize interviews, cluster themes, and surface unmet needs in minutes instead of days. Pairing those qualitative insights with behavioral signals in Amplitude analytics helps me spot high-intent cohorts and friction points at scale, so our problem framing is both human-centered and data-backed.
From there, I translate insights into crisp hypotheses and prioritize with the Kano Model and outcomes vs output OKRs. To keep experiments honest, I define a minimum detectable effect (MDE) up front and design A/B testing plans that reflect realistic traffic and seasonality, ensuring our decisions are statistically grounded rather than anecdotal.
Prototyping is where gen ai for product prototyping really shines. I spin up multiple UX flows, UI copy variants, and edge-case scenarios using prompt engineering, then iterate with rapid feedback from product trios. When needed, I mock in-app guides and product tours to validate onboarding concepts before we commit to code, preserving velocity without sacrificing quality.
For validation, I lean on a mix of lightweight experiments—fake-door tests, concierge pilots, and targeted A/B testing—augmented by in-product surveys via Pendo or Intercom. For AI-powered features, I apply eval-driven development to measure relevance, latency, and safety, so we can ship responsibly while maintaining the pace of learning.
This approach only works when the team is structured to move fast. Empowered product teams and product trios own discovery end-to-end, with clear guardrails around data governance, privacy-by-design, and AI risk management. That alignment lets us shift from opinions to evidence, and from output to outcomes, without friction.
If you’re getting started, pick one discovery loop to transform: automate research synthesis, prototype two to three variants with AI, and validate with a tightly scoped experiment. Instrument your analytics, track time-to-insight and time-to-prototype, and iterate your product roadmapping and sprint planning with what you learn. The payoff is immediate: faster cycles, stronger conviction, and a more user-driven path to product-led growth.
Happy New Year! I’m kicking off 2026 with a behind-the-scenes look at what’s changing in my product practice, the experiments I’m running with my teams at HighLevel, and the trends I’m most energized by—especially around continuous discovery, AI workflows, and building stronger coaching cultures.
If you want to listen to the conversation that sparked many of these reflections, you can find it here: Spotify | Apple Podcasts.
Why Teresa sunset the live deep-dive cohorts—and how on-demand and the new Discovery Habits Toolbox better support real behavior change. This pivot resonated with my own experience: some skills, especially discovery habits, only stick when they’re reinforced in the flow of real product work, not just in a time-boxed cohort. In my org, we’re leaning into on-demand learning paired with manager coaching to drive durable behavior change.
What leaders actually need to coach interviewing, assumption testing, and core discovery habits inside their orgs. I’ve found that empowered product teams thrive when leaders have lightweight coaching tools, practical prompts, and clear expectations for product trios. This is less about one-off training and more about building communities of practice where deliberate practice and feedback loops become routine.
Why training is shifting toward ongoing, leader-supported learning (and how AI will accelerate the shift). AI Strategy isn’t just about tools—it’s about learning systems. For LLMs for product managers to create leverage, we need eval-driven development, privacy-by-design, and clear guardrails. I’m building AI workflows that enable managers to review interviews, spot anti-patterns, and nudge teams toward better decisions—without replacing critical thinking.
Teresa’s move into paid subscriptions and why AI content doesn’t fit the classic “design once, run for years” course model. I see the same reality in my content roadmap: the half-life of AI guidance is short. That pushes us toward subscription models, tighter feedback loops, and a more adaptive go-to-market strategy for education products.
A sneak peek into the AI tools Teresa is building for discovery work—from interview coaching to near-ready interview snapshot generation. I’m particularly excited by tooling that scaffolds better interviews, sharpens assumption testing, and speeds up synthesis without skipping the human judgment step. These capabilities map directly to where I want my teams investing time: spending less energy on admin and more on learning from customers.
Petra’s plans for the year: community building with Product at Heart, a new product leadership email course, her Product Leadership Wheel, and workshops launching in Cairo. As someone who believes in conferences as high-quality “energy wells,” I’m inspired by how these programs create momentum for leaders who are upgrading their coaching muscles.
The role of conferences and retreats in staying grounded, inspired, and connected. I treat these gatherings as strategic resets—spaces to test ideas, confront blind spots, and deepen my network for future collaboration. The best outcomes often come from serendipitous hallway conversations and hands-on sessions where you can pressure test frameworks with peers.
How Teresa is staying on top of academic research (and why “synthetic users” aren’t ready for prime time). I agree: while synthetic data can be useful for scaffolding, it’s not a substitute for direct customer contact. Combine academic rigor with real-world interviewing and strong data governance—especially when operating under General Data Protection Regulation (GDPR).
The shared challenge of evaluating vendors and conference speakers making questionable AI claims. My heuristic: ask for clear problem statements, reproducible evaluations, grounded benchmarks, and a path to safe deployment. If a pitch can’t show measurable uplift or ignores compliance, it’s not ready for empowered product teams.
Key takeaways I’m carrying into 2026: delivery models matter; leaders need coaching tools, not just training; AI is reshaping how we teach and learn; experimentation is the theme of 2026; and community still energizes. That’s the blueprint I’m using to strengthen continuous discovery, refine our AI workflows, and sustain high standards in product management leadership.
What about you? How are you integrating AI workflows into your discovery practice, and what coaching tools are helping your managers reinforce the right habits? Share your approach—I’d love to learn what’s working in your context.
Resources & Links:
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Teresa’s website: Product Talk
General Data Protection Regulation (GDPR)
Product Talk Academy
Deliberate Practice – ATP episode where Teresa talked about the ending live cohorts for Deep Dive classes
Support tickets are the rawest signal of product truth. Leading product teams at HighLevel, I’ve learned that the fastest way to build what customers value is to transform frontline conversations into a repeatable, data-driven system for discovery, prioritization, and execution.
What if your support and product teams could unlock CX insights to turn every ticket into strategic product intelligence? Explore how.
Here’s the operating system I rely on. First, I connect our support stack (think Intercom and our CRM integration) into a unified analytics platform so every conversation, tag, and resolution is queryable. I don’t just count tickets—I segment them by product area, customer segment, lifecycle stage, and revenue impact to reveal patterns that roadmaps can act on.
Next, we standardize a shared taxonomy. Agents apply concise, high-signal labels (problem type, severity, intent), and we augment that with AI-driven auto-tagging to reduce noise and improve recall. The result is trustworthy “voice of the customer” data that product managers and support leaders can both stand behind.
Prioritization then becomes rigorous and fair. I weight themes by severity, frequency, ARR exposure, and time-to-value, and tie them directly to outcomes vs output OKRs. Amplitude analytics helps me quantify impact—what’s breaking activation, what’s dragging conversion, what drives retention analysis—so the backlog reflects business outcomes, not opinions.
Discovery is continuous by design. Product trios (PM, design, engineering) run weekly reviews of the highest-signal themes, recruit users straight from recent tickets, and prototype solutions quickly. We validate ideas with A/B testing when appropriate and ship targeted in-app guides to reduce confusion before it becomes a ticket.
Crucially, we close the loop. When we release a fix or improvement, we notify affected customers and the agents who flagged the issue. We track downstream effects—ticket deflection, CSAT, feature adoption, and time-to-resolution—so everyone sees how customer support ai strategy accelerates product-led growth.
This approach also builds culture. Empowered product teams treat support as a strategic partner, not a cost center. Agents become co-creators of the roadmap, and PMs gain a steady stream of product discovery opportunities grounded in real user outcomes.
If you’re getting started, a simple 30-60-90 can help: in 30 days, unify the data and agree on taxonomy; in 60, instrument dashboards and adopt a weekly insights ritual; in 90, align priorities to OKRs, launch targeted fixes, and measure business impact. That’s how tickets turn into product truth—and how CX insights drive compounding wins.
Inspired by this post on Amplitude – Perspectives.
I lead product teams building travel and hospitality experiences, and one lesson keeps repeating: companies that measure what matters move faster. Benchmarks turn gut feel into grounded product strategy, making it clear where activation, conversion, and retention are underperforming—and where we can unlock outsized growth.
Discover exclusive data and strategies from our Product Benchmark Report. Compare the travel and hospitality industry’s performance across key product metrics.
When I evaluate a product line, I start with a simple model: attract, convert, delight, and retain. For travel and hospitality specifically, I focus on search-to-book conversion, onboarding completion, first-booking activation rate, time-to-book, average booking value, cancellation rate, support contact rate, DAU/MAU stickiness, repeat booking rate, and long-term retention. These key product metrics reveal friction in discovery and checkout flows, surface pricing and inventory gaps, and quantify loyalty.
From there, I assemble a test-and-learn plan. Using Amplitude analytics to instrument the funnel and Pendo for in-app guides and product tours, my teams design A/B testing with a clear minimum detectable effect (MDE), prioritize hypotheses, and execute rapid, weekly iterations. This is classic product-led growth: reduce cognitive load in onboarding, streamline search and filter UX, clarify policies before payment, and personalize reactivation nudges to improve user activation and retention analysis.
Benchmarks are only as trustworthy as the underlying data. I insist on strong data governance, privacy-by-design practices, and clear event taxonomies so that insights remain reliable across quarters and across markets. That foundation keeps our decisions defensible with stakeholders and regulators while accelerating delivery.
Finally, we translate insights into action with crisp product roadmapping and sprint planning. Cross-functional product trios align OKRs to the biggest benchmark gaps, and we review progress in weekly performance rituals so every experiment ladders up to strategy. This cadence helps teams stay empowered and keeps leadership focused on outcomes, not output.
If you’re building in travel and hospitality, use these benchmarks as your starting line and your ongoing scorecard. Calibrate targets against peers, double down on what moves the needle, and let the data guide bold, customer-centered bets. When teams rally around meaningful metrics, momentum compounds.
Inspired by this post on Amplitude – Perspectives.
Continuous Discovery Habits turns five this year, and I’m celebrating by inviting you to read it with me. Over 135,000 people have bought the book. I’ve seen these habits transform outcomes, reduce rework, and sharpen product strategy in my teams and across the product community, but I also know it’s not easy to sustain the practice—especially when you feel like the lone champion in your organization.
To make it easier and more social, I’m launching the 2026 Continuous Discovery Habits Book Club. We’ll read the book together—one section per month—with discussion questions, practical exercises, and resources that help you actually do the work, not just read about it. Whether you’re picking up the book for the first time or revisiting it, the goal is to build real muscle memory in discovery.
By December, you won’t just understand continuous discovery—you’ll be practicing it.
Each month, I’ll share a reading guide with reflection prompts, exercises you can run solo or with your product trios, and short videos to help you spread the ideas across your team. I’ll monitor comments throughout the year so you can ask for help, share what’s working, and connect with peers—even if you join late.
I’ll also host quarterly live discussion sessions so we can compare notes, push through sticking points, and swap tactics with other empowered product teams. If you want to participate, grab a copy of the book (or dig up your old copy), share the "Spread the Love" videos to get friends and colleagues on board, reserve time to try the team exercises, and register for the community sessions. Let’s do this.
🎖️ This reading guide is brought to you by New Year, New Habit: The 5-Day Customer Interview Challenge. Become a more confident interviewer in less than a week. You’ll conduct one practice interview a day, get personalized and detailed feedback so you know exactly what to improve, and we’ll be giving out daily prizes to the most improved. Join the challenge today.
This Month’s Reading: Introduction; Chapter 1: The What and Why of Continuous Discovery; Chapter 2: A Common Framework for Continuous Discovery. Estimated reading time: ~40 minutes.
These chapters will introduce you to why discovery and delivery are not phases—they happen continuously. You’ll see a clear benchmark for what "continuous discovery" looks like, learn what product trios are and why they’re the foundation for good discovery, and explore six prerequisite mindsets (outcome-oriented, customer-centric, collaborative, visual, experimental, continuous) you’ll need before these habits can stick. You’ll also get the opportunity solution tree—a visual framework for connecting what you’re building to why you’re building it. Need a copy? Grab the book: https://amzn.to/3hGkNYT?ref=producttalk.org
We learn best in community. Use these short videos to share key concepts with teammates and invite them to read along: What is product discovery? https://videos.producttalk.org/videos/799fdbb41e16ebc4f0/what-is-product-discovery?ref=producttalk.org — a quick intro to the key idea behind discovery work. Defining continuous discovery https://videos.producttalk.org/videos/a79fdbba151ee3c72e/defining-continuous-discovery?ref=producttalk.org — a clear benchmark to aspire to. The rhythm of continuous discovery https://videos.producttalk.org/videos/4d9fd5b4111ee0c2c4/the-rhythm-of-continuous-discovery?ref=producttalk.org — the two small research activities you should do every week. The underlying structure of product discovery https://videos.producttalk.org/videos/449fdbb5191fedc4cd/the-underlying-structure-of-product-discovery?ref=producttalk.org — how outcomes, opportunities, and solutions connect. What’s a product trio? https://videos.producttalk.org/videos/a79fdbb31e1be2c12e/whats-a-product-trio?ref=producttalk.org — why cross-functional collaboration matters.
🎖️ This reading guide is brought to you by Just Now Possible, a podcast about how AI products come to life—straight from the builders. If you are being asked to add AI features to your roadmap, you don’t have to start from scratch. Get a head start by hearing how other teams are navigating similar challenges. Find it on YouTube, Apple Podcasts, and Spotify.
When we reflect and discuss what we read, we absorb more and apply it better. This month is about building awareness of where you are today—no judgment. The first step in any change is getting a baseline. Next month, we’ll take small steps to strengthen the habits.
Here are three prompts for individual reflection. 1) Think about a recent product decision your team made. Did you rely more on opinions, data, or customer input? Get specific. 2) Which of the six prerequisite mindsets (outcome-oriented, customer-centric, collaborative, visual, experimental, continuous) is strongest for you personally? Which would require the biggest shift? 3) What’s your reaction to weekly customer touch points? Does this excite you? Scare you? Something else?
And here are three prompts for team discussion. 1) Who on your team is responsible for discovery and delivery? How interconnected are these activities? 2) How does your team currently collaborate cross-functionally? When product, design, and engineering come together, is it to make decisions—or to hand off work? 3) Think of a recent feature your team built. What opportunity did it address? What else could you have built to address that opportunity?
For this introductory month, focus on seeing your current system clearly. In my experience, visibility alone reveals friction and makes the path to change obvious—and measurable.
Exercise: Draw Your Current Discovery Process. Time: 60 minutes. Do this solo first, then compare with your team. Take a blank sheet and draw how your team actually decides what to build. Show where ideas come from, who makes decisions and how, where (if anywhere) customers enter the picture, and how you know if you built the right thing. Then compare drawings with teammates. Where do perceptions differ? What does that say about your shared understanding?
Exercise: Audit Last Week’s Decisions. Time: 30 minutes. Do this solo or with your team. List every product decision your team made last week—big or small. For each decision, note who made it, what information it was based on, and whether customer input was part of the process (and how). Then look for patterns: how many included direct customer input versus assumptions, opinions, or secondhand information?
If you prefer an audio summary of this month’s reading—including the book chapters and the resources below—listen here: Stop Building The Wrong Things Faster (audio summary by NotebookLM): https://www.producttalk.org/content/media/2025/12/January—Stop_Building_The_Wrong_Things_Faster.m4a
Related in-depth guides to go deeper: Product Discovery Basics: Everything You Need to Know: https://www.producttalk.org/product-discovery/ Product Trios: What They Are, Why They Matter, and How to Get Started: https://www.producttalk.org/product-trios/ Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Drive Outcomes: https://www.producttalk.org/opportunity-solution-trees/
Other voices worth reading: Product Discovery: Pitfalls and Anti-Patterns by Chris Jones: https://svpg.com/product-discovery-anti-patterns/?ref=producttalk.org Addressing the Challenges of Product Discovery by Saeed Khan: https://medium.com/swlh/the-challenges-of-product-discovery-6ac6109d13a8?ref=producttalk.org Making Product Discovery Work in Small Teams by Sofia Quintero: https://www.chargebee.com/blog/product-discovery/?ref=producttalk.org Product Waste and the ROI of Discovery by Richard Mironov: https://www.mironov.com/waste?ref=producttalk.org
Related course if you want structured practice: Product Discovery Fundamentals – this course walks you through the complete continuous discovery framework with hands-on exercises: https://learn.producttalk.org/cdh-master-class?ref=producttalk.org
Our live discussion schedule for 2026 (sessions are not recorded): Wednesday, March 18, 2026: 9am–10am PDT and 4pm–5pm PDT. Tuesday, June 16, 2026: 9am–10am PDT and 4pm–5pm PDT. Thursday, September 17, 2026: 9am–10am PDT and 4pm–5pm PDT. Wednesday, December 16, 2026: 9am–10am PST and 4pm–5pm PST. Invitations will go out to Supporting Members and CDH Members two weeks beforehand—reserve the time now.
As you work through this month’s material, connect it to your product strategy, outcomes vs output OKRs, and product roadmapping and sprint planning. In my teams, discovery sticks when product trios own the rhythm, weekly customer touch points are normalized, and the opportunity solution tree keeps everyone aligned on outcomes.
I’m thrilled to learn alongside you this year. Grab the book, invite your trio, and let’s build habits that last.
Every planning cycle, I’m asked the same high-stakes question: should we build or buy? In 2026, with generative AI reshaping the software landscape and budgets under scrutiny, the classic calculus needs an upgrade. The right call can accelerate time to value, protect precious engineering capacity, and sharpen competitive differentiation—while the wrong one can quietly inflate total cost of ownership for years.
“Navigate the build vs buy software dilemma, learn how AI is changing the game, and what you should leverage (and when).” That’s been my north star for product strategy this year, and it’s how I guide teams when the pressure is on.
My first principle is simple: build where we differentiate, buy where we need parity. If the capability is central to our value proposition or our defensibility, I’m inclined to build—often with a phased approach that de-risks scope. If it’s a non-differentiating layer (think billing, analytics plumbing, basic CRM integration), I’ll buy to accelerate, then revisit once scale and specialization justify a deeper internal investment.
AI changes the equation on both sides. On the “buy” side, modern platforms now ship agentic AI, fine-tuning options, and robust APIs that let us compose advanced capabilities fast. On the “build” side, AI workflows and toolchains (from code copilots to eval-driven development) compress cycle time, making bespoke solutions more attainable. The trade-off has shifted from pure functionality to questions of AI risk management, model governance, data privacy, and the portability of prompts, embeddings, and training data.
I evaluate decisions across two economic horizons: time to value versus total cost of ownership. Buying often wins the first round—faster deployment, proven reliability, and lower initial lift. But TCO can creep: integration work, per-seat or consumption SaaS pricing, training, vendor-driven roadmap gaps, and the “shadow ops” of maintaining connectors in our CI/CD. Building flips that profile: slower early velocity, higher upfront complexity, but potentially lower long-run costs and tighter fit with our platform scalability goals.
Operational risk matters just as much as features. I look at incident management posture, SRE maturity, SLAs, and DORA metrics to gauge resilience. If a vendor can’t meet our uptime and recovery expectations—or if their roadmap pace mismatches our deployment frequency—we’re effectively renting risk we can’t control. Conversely, if our team can’t realistically support the operational burden, buying is the safer choice.
Security, regulatory compliance, and data governance are non-negotiables. I assess privacy-by-design, data residency, audit logs, role-based access, SOC2/ISO coverage, and threat detection and response. For AI-heavy systems, I add model lineage, red-teaming practices, PII handling, and retention policies. If we can’t verifiably meet our obligations in a build scenario within the launch window, we buy and require clear data exit and portability clauses.
To keep decisions objective, I use a lightweight scorecard across five dimensions: differentiation, urgency/time to value, regulatory/security risk, integration complexity, and AI leverage/portability. We weight criteria with product trios (PM, design, engineering), run discovery spikes, and validate assumptions with stakeholder management up front. A disciplined scorecard curbs recency bias and helps us communicate trade-offs to leadership.
In practice, I favor staged commitments. When uncertainty is high, we buy to learn—ship value quickly, instrument usage, and collect evidence. If adoption proves sticky and integration pain remains moderate, we double down with deeper vendor integration. If we uncover unique needs or cost inflection points, we pivot to a build plan that reuses learnings, data models, and UX patterns from the bought solution to reduce risk.
AI-specific choices deserve their own pass. For example, if we need retrieval-augmented generation, I’ll often buy for the orchestration and observability layer while building our domain-specific retrieval-first pipeline and prompt engineering guardrails. That split gives us speed plus control: we retain our IP and data gravity while tapping best-in-class tooling that evolves with the ecosystem.
Vendor strategy matters as much as technology. I negotiate clear data export, transparent API quotas, sandbox environments for continuous discovery, and price protections for growth. I pressure-test roadmaps, ask for integration references, and align on outcome-based milestones rather than feature checklists. Strong partners welcome this rigor; weak ones stall—another useful signal.
On the build side, I right-size ambition. We target minimum lovable scope, isolate risk in early sprints, and leverage open source where it’s mature and secure. We design for modularity so we can swap components without rewriting the world, and we budget time for in-app guides and product tours to smooth adoption, because user activation is the real finish line.
Here’s the playbook I return to: buy to validate and compress time to value; build to differentiate and reduce long-run TCO; continuously re-evaluate as the AI toolchain and our scale evolve. With a transparent scorecard, a bias for learning, and a clear view of risk, the build vs buy decision becomes less of a leap of faith and more of a repeatable product management capability.
2026 will reward teams that move fast without mortgaging the future. Make the call deliberately, instrument the outcomes, and stay humble—because the best strategy is the one you can adapt as new evidence arrives.
I’ve never seen great products emerge from a one-sided mindset. Inside-out thinking (strategy-first) and outside-in thinking (customer-first) aren’t rivals—they’re a flywheel. When I weave product vision and defensible differentiation together with real customer signals and behavioral data, adoption climbs, engagement deepens, and the roadmap becomes a catalyst for growth rather than a list of features.
For clarity: inside-out anchors on product strategy, value proposition, and the unique capabilities only we can deliver. Outside-in centers on continuous discovery, user research, and telemetry that reveals what customers actually do—not just what they say. At HighLevel, we pair these perspectives in every planning cycle so we’re bold in direction and grounded in evidence.
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That promise captures why the blend matters. Product-led growth lives or dies on moments like activation, time-to-first-value, and day-30 retention. Inside-out thinking ensures we’re building toward a compelling vision; outside-in thinking ensures users can discover, adopt, and realize value through clear onboarding, in-app guides, and contextual product tours.
Here’s how I apply it in practice. We start by articulating the smallest, sharpest version of our strategy—who we serve, the jobs we must win, and the non-negotiable outcomes. Then we pressure-test that thesis with continuous discovery: call snippets, funnel analysis, pathing, and retention analysis by cohort. When friction shows up in onboarding or early feature adoption, we deploy targeted in-app guides and tours to accelerate user activation without bloating the product or training costs.
A simple operating rhythm keeps the balance: begin each quarter with outcomes vs output OKRs tied to adoption and retention; instrument flows to expose drop-offs; ship iterative improvements; and reinforce them with just-in-time guidance. We use outside-in signals to sequence what we tackle next, and inside-out conviction to avoid chasing noise. The result is faster learning cycles and fewer expensive reworks.
Measurement closes the loop. I track activation rate, time-to-first-value, engagement with the few behaviors that predict renewal, and the impact of each guide or tour on completion rates. When we see lift, we codify the pattern; when we don’t, we prune and refocus. That evidence-based cadence keeps teams empowered and stakeholders aligned.
Culture makes this sustainable. Empowered product teams own outcomes, not tickets. Stakeholder management becomes easier when decisions are grounded in a clear strategy and transparent evidence from real users. And customers feel the difference when the product teaches itself—meeting them with the right help, in the right moment, without getting in their way.
If you’ve been choosing between inside-out and outside-in, stop. Fuse them. Lead with a crisp product strategy, listen with humility, and operationalize adoption through purposeful onboarding, in-app guides, and product tours. That’s how we compound learning, reduce risk, cut support costs, and accelerate product-led growth.
