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.
Across my teams and portfolio, I’m watching AI fundamentally reshape product-led growth—from static funnels and one-off playbooks to adaptive, compounding growth loops that learn in real time. The shift isn’t just technological; it’s an operating model change that rewards continuous discovery, rigorous instrumentation, and outcome-driven product strategy.
"Learn how AI is transforming PLG with a new generation of growth loops that can turn your product into a self-optimizing platform." That line captures what I’ve been building toward: systems that sense user intent, decide the next best action, act contextually, and learn to improve the loop with every interaction.
Here’s the core pattern I rely on. First, sense: unify product analytics and behavioral signals (think Amplitude analytics, Pendo events, Intercom conversations) into a single, queryable, privacy-safe layer. Second, decide: apply AI Strategy—LLMs for product managers, rules, and retrieval—to segment users by intent and probability of success. Third, act: deliver in-app guides, product tours, tooltips, or personalized nudges that accelerate user activation and time-to-value. Finally, learn: run A/B testing with a clear minimum detectable effect (MDE), then feed outcomes back into the model for continuous optimization.
Activation is where the gains start compounding. With gen ai, I can auto-generate tailored onboarding checklists, dynamic walkthroughs, and contextual help that adapts to the user’s role, data maturity, and current friction points. We’ve moved from generic product tours to precision guidance that updates based on real-time behavior—often lifting first-week activation and shortening time-to-first-value without adding support load.
Experimentation is the governor that keeps speed and quality in balance. I instrument every growth loop end to end and pair eval-driven development with A/B testing to confirm incremental impact. Amplitude analytics gives me cohort views and path analysis; Pendo or Intercom can deliver in-app variants; a unified analytics platform closes the loop on retention analysis so I’m not optimizing for click-through at the expense of long-term value.
Retention and expansion are where AI shines as a compounding engine. Retrieval-first pipeline patterns allow instant, contextual support that deflects tickets and boosts perceived product competence. Agentic AI can orchestrate next-best actions—prompting power users toward advanced features, surfacing value moments, or timing expansion prompts when success signals appear. The result is a virtuous cycle: better guidance drives deeper adoption, which improves model accuracy, which unlocks more relevant guidance.
None of this works without guardrails. I bake in AI risk management from the start: strict data governance, privacy-by-design, human-in-the-loop review for high-impact actions, transparent user consent, and continuous drift monitoring. The goal is reliable automation that users trust—augmented by clear fail-safes when confidence drops.
Operationally, I anchor the work in empowered product teams and product trios, focus on outcomes vs output OKRs, and practice continuous discovery to validate problems and solutions before scaling. The baseline metrics I watch: activation rate, time-to-value, week-four retention, PQL/PQA conversion, expansion revenue, and support deflection—each tied to a specific growth loop hypothesis.
If you’re starting fresh, begin with the highest-leverage loop: user activation. Instrument your onboarding journey, define the critical path to value, ship two to three personalized interventions, and measure impact with a precommitted MDE. Scale what wins, drop what doesn’t, and iterate weekly. Once activation is compounding, extend the same approach to adoption depth, collaboration features, and expansion triggers.
In practical terms, AI-powered PLG is less about flashy features and more about disciplined feedback loops. Build the sensing fabric, keep the decision layer auditable, ship small actions quickly, and treat learning as the product. Do that, and your product doesn’t just grow—it becomes a self-optimizing platform.
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.
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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.
I set out to create a lightweight, high-impact “Pendo Wrapped” experience for our users—and I did it in under 10 minutes with Pendo MCP. As a VP of Product Management, I’m constantly looking for fast, pragmatic ways to turn product insights into moments that drive engagement. This experiment was about transforming raw analytics into a concise, celebratory year‑in‑review that motivates customers to explore more value.
When I say “Pendo Wrapped,” I mean a simple, narrative-style summary of usage highlights: what got adopted, which moments mattered, and where value showed up most clearly. Framed well, that story reinforces product‑led growth by reminding users why they chose us, nudging them toward the next best action, and strengthening activation and retention without heavy development work.
My approach was straightforward: define a clear objective (celebrate milestones and prompt the next step), choose a focused set of metrics (adoption, engagement, and activation), and target relevant segments. Then I layered the narrative on top of existing analytics using in‑app guides and product tours to deliver the experience where it matters most—inside the product.
The reason it took minutes, not hours, is that Pendo MCP let me work with what we already had—segments, saved reports, and proven guide templates—so I could spend time on the story, not the scaffolding. No code, minimal configuration, and a crisp call to action made it feel polished without being heavy.
Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.
If you want to replicate this quickly, start by selecting one user segment and three metrics that matter to them, write a two‑sentence narrative that connects those metrics to outcomes, and ship a short in‑app guide with a single, purposeful CTA. That’s enough to deliver a personalized year‑in‑review feel and spark immediate exploration—no new infrastructure required.
What surprised me most was how a small, story‑driven touch created outsized alignment across customers and internal teams. It turned analytics into advocacy, reminded our users of the value they’re already getting, and opened the door to deeper adoption. If you’re pursuing product‑led growth, a fast “Pendo Wrapped” is one of the highest‑leverage experiments you can run this week.
2026 is closer than it feels, and the signals are already clear. I’ve been synthesizing what I’m seeing across empowered product teams, boards, and cross-functional partners into a practical view of what matters next. A sharp look at product management trends for 2026. Not guesses, but signals from top product leaders shaping how PMs will actually work next.
In this analysis, I distill eleven shifts that are changing the craft—from outcomes vs output OKRs and continuous discovery to stronger product strategy and tighter product roadmapping and sprint planning. The throughline is simple: prioritize customer value, ship with focus, and measure what moves the business. These aren’t headline trends; they’re working patterns I’m seeing across high-performing organizations.
AI is no longer a side project—it’s part of the product manager’s core toolkit. Agentic AI, LLMs for product managers, and trustworthy AI workflows are accelerating discovery, sharpening problem framing, and enabling faster iteration. The best teams pair this with disciplined evaluation and experimentation, so insight compounds without sacrificing safety, privacy, or product quality.
Execution is getting crisper through product trios and stronger stakeholder management. When design, product, and engineering co-own discovery and delivery, teams reduce handoffs and increase clarity. That alignment translates into better prioritization, fewer context-switches, and a roadmap that reflects real trade-offs—not wish lists.
On growth, product-led growth remains a durable engine when it’s anchored in a compelling value proposition and instrumented end-to-end. Clear activation moments, in-app guides, and thoughtful product tours outperform brute-force acquisition. When we connect these motions back to product strategy and the roadmap, we create a repeatable loop that compounds adoption and retention.
Governance and trust are now table stakes. Privacy-by-design, data governance, and a pragmatic approach to regulatory compliance protect both users and velocity. Teams that build these practices into their operating model move faster because they avoid late-stage rework and maintain stakeholder confidence.
If you’re leading a product org—or aspiring to—this is your field guide to 2026. I’ll unpack where these shifts are strongest, how to apply them in your context, and the pitfalls to avoid. The aim is to give you clear language, concrete practices, and a sharper edge as you shape what your team builds next.
I’ve spent the last few years turning AI from an intriguing demo into an operational advantage, and the clearest wins come when we treat agents as productized workflows—not toys. In practice, that means aligning agentic AI to a sharp product strategy, instrumenting everything, and scaling what works across the organization.
Learn how companies like Replit are consolidating workflows, creating one-person departments, and building systems for scale with Amplitude
When I talk about agentic AI, I’m focused on outcomes: fewer handoffs, faster cycle times, and measurable uplift in activation, retention, and NPS. The most successful rollouts start with a specific job-to-be-done, translate it into clear AI workflows, and then iterate with a tight feedback loop between data, design, and engineering.
My implementation playbook is simple and disciplined. First, choose a high-friction workflow and define success upfront. Second, make the build vs buy call on the foundation model, orchestration layer, and connectors. Third, establish AI risk management and safeguards early—before scale amplifies errors. Finally, run small, eval-driven releases and promote what performs.
Instrumentation is where the leverage compounds. With Amplitude analytics as a unified analytics platform, I design purposeful events (agent intent, tool calls, resolution state, human handoff), map funnels from user input to agent outcome, and cohort users by context to pinpoint lift. This gives me an honest read on where agents help, where they hinder, and what to tune next.
The “one-person departments” concept isn’t about doing more with less at all costs; it’s about assembling a tight loop of product management leadership, data, and automation so one operator can own a business outcome end-to-end. An agent handles the repeatable work, while the human focuses on judgment, edge cases, and continuous improvement that compounds.
As we scale, I look for platform scalability patterns: shared tools and policies, reusable prompt libraries, standardized evaluation suites, and consistent governance. That structure keeps agent performance predictable while preserving speed, and it aligns beautifully with product-led growth when agents are embedded directly in the product experience.
If you’re starting now, begin with a single, valuable workflow. Instrument it thoroughly with Amplitude analytics, make decisions from the data you see—not the demos you remember—and expand only after you’ve proven uplift. Iteration beats ambition here: agentic AI rewards teams who measure relentlessly and scale only what truly works.
Inspired by this post on Amplitude – Perspectives.
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.
Buy-in isn’t a single meeting; it’s a designed journey. Over the years leading product strategy at HighLevel, I’ve learned that the fastest way to earn durable support is to reduce uncertainty, align on outcomes, and create visible momentum. Explore how to get buy-in from stakeholders with practical strategies, clear communication tips, and proven methods used by the best. Here’s the 7-step playbook my teams and I rely on to move from idea to aligned action.
Step 1 — Anchor on outcomes, not outputs. I start by writing a crisp problem statement, the target customer, and the measurable outcome tied to our North Star metric. I translate this into outcomes vs output OKRs so every stakeholder can see the difference between what we’ll ship and what we intend to change. This framing keeps discussions grounded in impact, not features.
Step 2 — Map stakeholders and incentives. Effective stakeholder management begins with a living map: economic buyers, executive sponsors, influencers, and operators. I capture each person’s goals, risks, and decision cadence. When I speak to Finance, I foreground cost and runway; with Sales, I emphasize pipeline and win rate; for Customer Success, I speak to retention and NPS. Meeting stakeholders where they are builds trust quickly.
Step 3 — Co-create early with the product trio. I pull the product trios (PM, Design, Engineering) into continuous discovery with GTM partners to validate assumptions and de-risk the solution. This is where empowered product teams shine—rapid discovery sprints, early prototypes, and clear learning objectives. Co-creating exposes blind spots early and transforms critics into champions.
Step 4 — Socialize a narrative, not a deck. Before any formal review, I circulate a short narrative memo that ties our product strategy to a clear value proposition, competitive differentiation, and go-to-market strategy. I include options and trade-offs so stakeholders feel invited to shape the path, not just stamp approval. Pre-wiring conversations ensure that the “meeting” is simply the last 10% of the decision.
Step 5 — Back the story with data and a viable plan. I combine retention analysis, funnel metrics, and customer evidence to demonstrate opportunity size and risk reduction. Then I outline a phased approach with product roadmapping and sprint planning, milestones, and success metrics. I highlight the smallest viable bet that proves value fast, along with contingency paths if we learn something unexpected.
Step 6 — Design the decision. I define the decision we need, by whom, and by when. The decision doc includes the problem, options, risks, mitigations, and the explicit ask. I schedule 1:1s to address concerns, then run a focused review with clear roles and time-boxed discussion. Clarity about the decision—and the criteria—prevents drift and protects timelines.
Step 7 — Sustain momentum post-approval. After the green light, I convert the plan into execution cadences: weekly demos, transparent dashboards, and QBRs vs OKRs check-ins to reinforce outcomes. We celebrate learning milestones, not just launches, and keep stakeholders informed with concise updates that tie progress to the original outcomes and value proposition. Momentum is the best antidote to second-guessing.
Clear communication and a repeatable process turn buy-in from a hurdle into a habit. When stakeholders see a compelling narrative, credible evidence, and a path to value, they don’t just approve—they advocate. Follow these seven steps and you’ll build alignment faster, ship smarter, and strengthen trust across the organization.
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.
Your team probably doesn’t lack discovery techniques. It loses discovery when delivery becomes urgent. Customer contact clusters around planning, the team commits to a solution, the calendar fills, and assumptions quietly harden into backlog items. Everyone stays busy, but no one can point to the customer evidence behind the next decision.
Durable continuous discovery is an operating rhythm, not a research phase. The goal isn’t to conduct more interviews. It is to shorten the distance between customer reality and the decisions shaping your product. A weekly rhythm owned by a product trio can reduce rework, sharpen strategy, and keep discovery alive while delivery continues.
See your real discovery system before changing it
Adding a recurring customer interview to the calendar won’t fix a decision process built around handoffs. If ideas arrive from executives, become requirements in product, move to design, and reach engineering as implementation work, the interview is an extra activity attached to the side of the system. It isn’t part of how the team decides.
Start by making the existing system visible. Map what actually happened, including the awkward shortcuts and informal approvals. Do not draw the process described in a playbook.
Spend 60 minutes drawing how your team decides what to build. Show where ideas enter, who shapes them, who approves them, where customers appear, and how the team decides whether the result worked.
Compare your drawing with the drawings made by product, design, and engineering. Differences are evidence that the team does not share the same decision model.
Audit every product decision from last week in a 30-minute session. Include small decisions, not just roadmap commitments.
For each decision, record who made it, what information informed it, and whether the team had direct customer input or received a secondhand interpretation.
Mark the places where discovery and delivery reconnect. A production problem, adoption signal, support request, or implementation constraint can create a new discovery question; it should not disappear into a separate queue.
This process map and decision audit gives you a baseline without turning discovery into a maturity score. Look for the mechanism behind the misses. Perhaps customer input arrives after commitment. Perhaps the product manager is the only person who interprets it. Perhaps the team can describe the solution but not the opportunity it addresses.
Track a compact baseline: how recently the team had direct customer contact, which current decisions include direct input, where cross-functional decisions become handoffs, and which active solutions lack a named customer opportunity. Do not set targets yet. First identify where evidence stops influencing action.
If the process looks reasonable but the habit still collapses, inspect the six prerequisite mindsets: outcome-oriented, customer-centric, collaborative, visual, experimental, and continuous. Turn them into diagnostic questions:
Outcome-oriented: Can the team name the customer or business change it is trying to create, or only the feature it plans to ship?
Customer-centric: Does the team hear directly from customers, or mainly through sales, support, analytics, and stakeholder summaries?
Collaborative: Do product, design, and engineering make decisions together, or meet mainly to exchange work?
Visual: Is there one shared representation of the outcome, opportunities, solutions, and assumptions?
Experimental: Can the team name what could make the current idea fail?
Continuous: Does each learning activity lead to the next question, or does discovery end with a presentation?
Choose the weakest link as your first intervention. A team with output-based goals does not need a better interview script first; it needs an outcome that gives the interview a purpose. A team dominated by handoffs needs shared sensemaking, not another repository.
Install a weekly loop small enough to protect
A habit survives because its trigger, action, and output are clear. Put a recurring discovery block at a stable point in the team’s operating rhythm. Tie it to a current outcome and a live decision, not to a general ambition to understand users better.
Trigger: A protected calendar block recurs every week, including during active delivery.
Focus: The trio brings one current outcome, the decision in front of it, and the uncertainty preventing a confident choice.
Customer contact: The team has a direct customer touchpoint every week. That might be a customer interview, observation of a workflow, or a usability session connected to the current question.
Sensemaking: The trio separates what it observed from what it inferred.
Update: New evidence changes the opportunity solution tree or confirms why no change is warranted.
Commitment: The team names the next uncertainty and starts arranging the next customer contact.
A customer touchpoint is not any meeting attended by a customer. A sales demo, account review, or advisory session dominated by presentation may be valuable, but it does not automatically answer a discovery question. The useful test is whether the customer can reveal a real behavior, need, constraint, or reaction and whether the team can ask follow-up questions.
Prepare each touchpoint by completing this sentence: After this contact, the trio might decide whether… If you cannot finish it, the question is probably too broad. Starting with a decision also reduces the temptation to collect interesting comments that never affect the product.
During the interaction, capture concrete observations before interpretations. Afterward, answer five questions while the context is fresh:
What did the customer do, describe, or struggle to explain?
What interpretation is the team placing on that observation?
Which opportunity or assumption does it affect?
What decision changes, if any?
What remains uncertain enough to examine next?
The distinction between observation and interpretation matters. A customer abandoning a task is an observation. Assuming that price caused the abandonment is an interpretation. If the team records only the interpretation, an early guess can become institutional memory.
Recruiting is part of the habit, not administrative work that begins after an interview is requested. Give coordination to a named owner, maintain a rolling pool of relevant customers, and create simple paths for customer success and support to nominate people who recently experienced the problem. Start the next invitation before the current discovery cycle feels complete. Otherwise, every customer cancellation becomes a reason to skip the week.
When delivery pressure rises, protect the trigger and narrow the activity. Ask a smaller question, review a focused prototype, or examine one step in a workflow. Do not silently replace direct contact with an internal meeting and call the habit complete. If a customer cancels, use the protected time to recruit, refine the decision question, and reschedule. Preserve the rhythm without pretending the missing evidence exists.
Give the product trio ownership of decisions, not ceremonies
A product trio is not three people attending the same interview. It is product, design, and engineering sharing responsibility for understanding the opportunity and choosing how to address it. Attendance can rotate. Interpretation and decision-making cannot be delegated to one function and handed back as a deck.
Make the trio’s decision rights explicit at the start of an outcome. Record the outcome it owns, the decisions it can make autonomously, the constraints it must respect, what requires escalation, and where its evidence will remain visible. Without that contract, discovery may reveal a better direction while the roadmap continues unchanged because nobody knows who can act.
The responsibilities below are a practical starting point, not rigid job boundaries:
Product keeps the outcome, strategic context, customer segment, and pending decision visible.
Design helps the trio expose customer behavior, frame opportunities, and choose an appropriate way to learn.
Engineering surfaces feasibility, system behavior, data, and implementation assumptions before the solution becomes expensive to change.
The trio decides what the evidence means, which option remains viable, and what uncertainty deserves attention next.
Use a short shared debrief after customer contact. The format can remain simple:
Observation: What happened without interpretation?
Meaning: What plausible explanations fit the observation?
Decision: What will the trio change or preserve?
Unknown: What still blocks commitment?
This prevents the loudest interpretation from becoming the team’s conclusion. It also gives engineering a role before implementation and gives design a role beyond producing artifacts.
Leadership should ask for evidence of changed decisions, not proof that ceremonies occurred. Instead of asking how many interviews the team completed, ask which opportunity became clearer, which assumption weakened, what decision changed, and how the change connects to the outcome. Interview volume is easy to report and easy to game. Decision quality is harder to display, but it is the reason the habit exists.
Connect discovery evidence to strategy and delivery
A weekly customer conversation can still become theater if its evidence floats separately from strategy, roadmaps, and sprint planning. The opportunity solution tree provides a shared spine: the desired outcome sits at the top, customer opportunities sit beneath it, and candidate solutions connect to the opportunities they could address. That outcome-opportunity-solution structure keeps the team connected to why it is considering a particular feature.
Use the tree as a decision interface, not a workshop artifact:
Product strategy: Put the intended outcome at the top so the team can test whether its discovery work supports the strategic direction.
Roadmapping: Attach candidate solutions to named opportunities. Keep alternatives visible until evidence or a real constraint justifies commitment.
Sprint planning: Require each significant item to trace back to an opportunity and outcome. If it cannot, surface the mismatch before implementation.
Customer contact: Update the affected opportunity, solution, or assumption during the debrief. Do not wait for a separate documentation session.
Stakeholder communication: Show what changed in the tree, why it changed, and which decision follows. This is more useful than presenting a collection of customer quotations.
Keep a record of rejected options and the evidence or constraint behind each rejection. Otherwise, an old idea can return with a new label and consume another round of debate. The record should remain revisable: new customer behavior, technical capability, or strategic constraints can justify reopening a branch.
Measure whether evidence enters decisions
The safest discovery metric is not an isolated activity count. Measure the health of the loop:
Cadence: Did direct customer contact happen during the weekly rhythm?
Decision integration: Which current decision did that contact inform?
Shared ownership: Did the trio participate in sensemaking, even if every member did not attend the session?
Strategic traceability: Can a delivery item be traced to an opportunity and outcome?
Learning movement: Which belief, option, or assumption changed?
A team can conduct many interviews and learn very little if every conversation validates a solution already selected. Conversely, one focused interaction can be valuable when it exposes a faulty workflow assumption and changes a pending decision. Track cadence to protect the habit, but judge value by movement in the decision model.
Separate customer, model, and operational uncertainty in AI products
AI product teams face a specific discovery trap: an impressive model demonstration can make technical possibility look like customer demand. Keep different uncertainties separate so one kind of evidence does not answer a different question.
Customer uncertainty: What job is the person trying to complete? Where does the current workflow break? Under what conditions will the person trust, verify, correct, or reject an AI-assisted result?
Model uncertainty: Does the system produce acceptable behavior for the intended context? Which failures matter to the user, and how will the team evaluate them?
Operational uncertainty: Can the product obtain the required data and permissions? Where is human review needed? How will failures be detected, explained, and supported?
Customer contact can reveal workflow, language, trust conditions, and failure consequences. It cannot prove that the model behaves reliably. Model evaluations can reveal performance and failure patterns. They cannot prove that the workflow is valuable. Operational checks can establish feasibility and controls. They cannot prove adoption. Keep all of these linked to the same outcome while using the right evidence for each uncertainty.
On the opportunity solution tree, write opportunities in customer terms. “Use generative AI” is a solution direction, not an opportunity. “Reduce the effort required to turn a customer conversation into an accurate follow-up” describes a customer problem that could have AI and non-AI solutions. That distinction helps the trio discover value without becoming attached to a technology.
Fix the mechanism when the habit breaks
What you notice
Likely mechanism
What to change
The team talks to customers, but the roadmap never changes.
Sessions are disconnected from a live decision.
Write the decision before recruiting and record what changed immediately after the interaction.
Engineering joins only after discovery is complete.
The trio label is masking a handoff.
Include engineering in opportunity framing, assumption identification, and shared sensemaking. Session attendance can rotate.
Customer sessions repeatedly fall through.
Recruitment starts only after a question becomes urgent.
Maintain a rolling pool of relevant customers and assign coordination to a named owner.
The opportunity solution tree is stale.
The tree is treated as presentation material.
Update it during the debrief and remove or annotate branches that no longer have support.
Discovery pauses whenever delivery accelerates.
Discovery is scoped as a project rather than a continuous rhythm.
Protect the weekly trigger and narrow the question or method when capacity is tight.
Leadership keeps asking the team for certainty.
The team reports activities without showing their decision impact.
Show the outcome, changed opportunity or assumption, resulting decision, and remaining uncertainty.
Do not respond to a broken habit by adding more process everywhere. Match the intervention to the failure. A recruiting problem needs a pipeline. A decision-rights problem needs leadership alignment. A stale artifact needs an update trigger. A handoff problem needs shared sensemaking.
Key takeaways
Map the current decision system and audit last week’s decisions before adding a new discovery ceremony.
Anchor a direct customer touchpoint every week to a current outcome, decision, and uncertainty.
Let attendance vary when necessary, but keep interpretation and decisions jointly owned by the product trio.
Use the opportunity solution tree as the live connection between strategy, customer evidence, roadmap choices, and sprint work.
When delivery pressure rises, protect the trigger and shrink the activity instead of suspending the cadence.
For AI products, do not use customer enthusiasm as proof of model reliability or an evaluation result as proof of customer value.
Put the recurring customer touchpoint on the calendar, choose the outcome and decision it must inform, and name the product trio responsible for acting on what it learns. At the end of the next weekly cycle, do not ask whether the team “did discovery.” Ask what changed in the decision and what the team needs to learn next.
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.
