Tag: product tours

Inside-Out vs Outside-In: How I Balance Both to Build Products Users Love—and CFOs Trust

Inside-out or outside-in thinking? I choose both. The strongest product strategies fuse a bold internal vision with relentless customer evidence, creating a flywheel that lifts adoption, engagement, and revenue while reducing risk.

When I lead with inside-out thinking, I articulate a clear product thesis, technical roadmap, and platform leverage. This is where we define points of parity and differentiation, sharpen our value proposition, and ensure our architecture scales. It’s disciplined, outcomes-first, and anchored in product positioning—not output checklists.

Outside-in thinking ensures that vision stays honest. I listen to customers, analyze friction in onboarding, instrument user activation, and study retention analysis to validate whether our promises translate into real user value. This is where product discovery, A/B testing, and in-app signals tell me what’s working, what needs refinement, and what we should stop doing.

In practice, I operationalize this balance through Software Experience Management. “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.” That promise captures the core of how I align strategy with reality inside the product, not just around it.

Concretely, I combine product analytics with in-app guides and product tours to accelerate onboarding and improve user activation. I run targeted experiments to de-risk decisions, and I iterate quickly based on what users actually do—not just what they say. The result is a product-led growth engine that compounds over time.

This approach also builds trust with finance and go-to-market partners. Inside-out clarity gives us confident, sequenced bets; outside-in data provides proof that those bets pay off. When engagement expands and adoption climbs, the business case writes itself.

If you’re deciding where to start, begin with three moves: define activation events aligned to your value proposition, instrument the experience end-to-end, and ship one high-impact in-app guide to remove a known onboarding blocker. Then measure, learn, and iterate—quickly.

The truth is, great products emerge when conviction meets evidence. Inside-out sets the vision. Outside-in earns the right to scale it.

Inspired by this post on Pendo – Perspectives.

November 12, 2025
Cut Time to Value, Boost Retention: My Proven Playbook for Activation, Growth, and Loyalty

Time to value is the most reliable early indicator of long-term user retention I know. When customers experience meaningful product impact fast, they stick around, expand, advocate, and cost less to support. Over the years leading product teams, I’ve learned that speed-to-impact isn’t a nice-to-have—it’s the engine behind sustainable product-led growth and efficient go-to-market.

Accelerate retention by reducing time to value. Learn how faster product impact drives growth, reduces costs, and keeps users engaged in the long term.

Practically, I define time to value as the duration from first touch (or first login) to the moment a user achieves their “aha” outcome—something tangibly useful aligned to their job-to-be-done. The shorter that journey, the higher the likelihood of user activation, trial conversion, and durable engagement. This is why I obsess over onboarding, in-app guides, product tours, and the clarity of our value proposition.

My first move is to map the Minimum Path to Value (MPV): the smallest set of actions needed to deliver a real result for a new user. I strip away everything non-essential in that path—fields, clicks, choices, and jargon. Opinionated defaults, smart templates, sample data, and single-player workflows let customers succeed in minutes, not days. The goal is to reduce cognitive load while making the next best action unmistakably clear.

Instrumentation turns TTV from a hunch into a system. I track activation events, cohort retention, and conversion using platforms like Amplitude analytics and Pendo, with timely nudges through Intercom when users stall. I look at the distribution of TTV (not just the average), correlate it with retention analysis, and set explicit targets such as “new users reach first value within 10 minutes.” Those targets become team-level outcomes—not outputs—and we review them weekly.

Experimentation is how we iterate toward the fastest path to value. I rely on A/B testing to compare onboarding flows, progressive profiling to delay non-critical inputs, and opinionated setup wizards to remove guesswork. Auto-generated example projects, pre-configured integrations, and guided checklists accelerate user activation without sacrificing flexibility for advanced users.

Content and guidance matter as much as UX. Tooltips, contextual in-app guides, and short product tours should be timely, skippable, and laser-focused on the outcome, not the feature. I pair these with a concise knowledge base and short explainer videos that reinforce the same value narrative a user sees inside the product.

Cross-functional alignment is essential. Product, marketing, sales, and customer success must rally around the same activation metric and TTV target. That alignment ensures our trial messaging, onboarding emails, and CS playbooks don’t compete—they compound. When everyone points to the same first-value moment, friction drops and adoption rises.

Pricing and packaging can also accelerate time to value. Free trials should be long enough for users to credibly reach first value; usage-based gates should never block the MPV. I prefer to unlock everything needed to hit the “aha” moment, then meter after the value is viscerally felt—this respects the user’s time and reinforces trust.

There’s a cost story, too. Faster time to value reduces tickets, shortens onboarding cycles, and lowers cost-to-serve. It also clarifies product discovery: when we see where users stall, we don’t guess at roadmap priorities—we let the data guide our next bet.

In my experience at HighLevel, I’ve repeatedly seen activation rates jump when we cut time to value from days to minutes. The specific tactics vary by product, but the pattern holds: when the first outcome is undeniable and fast, retention follows—and so does efficient growth.

If you’re looking for a starting point, try this: define one activation event that clearly signals value, instrument it end-to-end, design a Minimum Path to Value that gets new users there in under 10 minutes, and run weekly experiments until you consistently hit the target. Do that, and you won’t just improve onboarding—you’ll build a product that earns loyalty from the very first session.

Inspired by this post on Amplitude – Best Practices.

November 9, 2025
The Product Playbook: Measuring Agent Performance with Pendo and Agent Analytics to Drive ROI

I treat agent performance analytics as a strategic product lever, not a back-office metric. When I combine Pendo’s product signals with Agent Analytics from our support systems, I get a unified view of where users struggle, how agents intervene, and which in-app experiences accelerate resolution. That visibility lets my team drive product-led growth and improve customer experience while lowering support costs.

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.

In practice, I build a clear scorecard that blends both product and support KPIs: first response time, resolution rate, first contact resolution, CSAT, containment/deflection rate, average handle time, ticket volume per active account, onboarding completion, user activation, and time-to-value. This balanced view ensures we reward not just speed, but durable outcomes that reduce repeat contacts and improve retention.

To make the data actionable, we connect our CRM integration, ticketing events, and Pendo product analytics in a unified analytics platform. That gives me cohort-level clarity—who needed help, what they were doing before opening a ticket, how agents responded, and whether users stayed engaged afterward. With clean instrumentation and consistent taxonomies, Agent Analytics becomes a reliable operating system for both product and support leadership.

I then use in-app guides, tooltips, and product tours to proactively address the top friction points that drive ticket volume. Through A/B testing, we compare cohorts exposed to guided workflows versus control groups, measuring deflection, faster task completion, and downstream conversion. When a guide meaningfully reduces tickets for a given workflow, we promote it from experiment to standard onboarding, and we feed those learnings back into our roadmap.

The real unlock comes from tying outcomes to business impact. I track how improvements in resolution quality and self-serve adoption influence expansion revenue, support cost per account, and risk signals like churn propensity. Retention analysis helps us validate whether reduced friction and better agent coaching translate into sustained engagement and healthier accounts.

Operationally, Agent Analytics helps me coach teams with precision. I spotlight high-performing behaviors, identify knowledge gaps, and standardize winning playbooks directly in the product via in-app guidance. This approach empowers agents, shortens onboarding for new hires, and keeps our best practices current as the product evolves.

None of this works without trust. We apply privacy-by-design principles and strong data governance, ensuring that analytics, coaching, and automation respect user consent and data minimization standards. With that foundation, we can scale confidently—experiment faster, learn from every interaction, and continuously improve the software experience.

If you’re getting started, begin by baselining your agent and product KPIs, ship one high-impact guide to deflect a top ticket driver, and review results weekly. Within a quarter, you’ll have a repeatable loop: diagnose friction, test an in-app solution, measure deflection and satisfaction, and reinvest the gains into the next set of improvements.

Inspired by this post on Pendo – Best Practices.

November 4, 2025
Inside Our AI-Native Product Training: Accelerating Adoption, ROI, and Measurable Growth

AI is reshaping how we build products, learn new skills, and lead teams. I’ve seen great organizations stall when training lags behind technology. That’s why we rebuilt our approach to product training from first principles—so every team can operate confidently with AI at the core of their product management practice.

Our north star is simple: operationalize AI Strategy for every product manager and cross-functional partner. We designed a learning system that shortens time-to-adoption, amplifies ROI, and links capability-building to clear, measurable outcomes.

Product School transforms product teams into AI-native organizations with training that accelerates adoption, maximizes ROI, and drives measurable growth.

That ambition informs how we design curriculum and delivery. We combine gen AI foundations, LLMs for product managers, applied product discovery, product roadmapping and sprint planning, and product management leadership. The learning experience blends case-based instruction with simulations and real product data so teams practice exactly how they’ll perform.

To ensure knowledge becomes behavior, we embed training directly into product workflows: in-app guides, product tours, onboarding sequences, and user activation loops tied to outcomes vs output OKRs. This closes the gap between knowing and doing, and it makes capability visible in the metrics that matter.

We focus on empowering product teams—clarifying decision rights, elevating accountability, and creating feedback loops that enable faster iteration. When teams own their roadmap and understand the AI building blocks, they move from experimentation to repeatable, scalable value creation.

Measurement is built in from day one. We instrument for adoption, time-to-first-value, feature activation, and ROI attribution, enabling continuous improvement and transparent stakeholder communication. The result is a system that compounds learning into performance.

This is how we’re building AI-native organizations: practical, data-informed, and outcomes-driven. It’s not just training—it’s an operating model that helps teams learn faster, ship smarter, and grow with confidence.

Inspired by this post on Product School.

November 3, 2025
Pendo’s Summer Release: How I Reimagine Onboarding, Support, and Expansion in the SaaS + AI Era

I’ve been reflecting on How Pendo’s Summer Release reimagines onboarding, support, and expansion in the SaaS + AI era, and it resonates deeply with the product-led playbooks my team and I use every day. The core promise is simple and powerful: “These three best practices aren’t new, but how you achieve them is.” That framing captures the shift I see across high-performing product organizations—same outcomes, radically upgraded execution through AI, in-app experiences, and unified analytics.

For onboarding, I prioritize accelerating user activation with clear product tours, in-app guides, and great UX writing that removes cognitive load. The difference now is how precisely we personalize these moments: segmentation driven by product usage, CRM integration, and experiments (A/B testing with a disciplined minimum detectable effect) help us craft paths that meet users where they are. When onboarding is instrumented this way, it becomes a scalable engine for product-led growth rather than a one-time setup task.

Support is undergoing an equally meaningful transformation. Contextual, in-app help combined with agentic AI can diagnose issues, surface relevant knowledge, and guide users without forcing channel switches. I’m bullish on this, but only when it’s anchored in privacy-by-design, AI risk management, and strong data governance—trust is the prerequisite for any customer support AI strategy. When done right, support shifts from reactive ticket resolution to proactive value delivery.

Expansion, to me, is the earned outcome of consistent product value. In the SaaS + AI era, we can use unified analytics to identify readiness signals—feature adoption, outcomes achieved, and time-to-value—and trigger timely, ethical nudges in-app. The best motions align offers with real customer milestones, whether that’s consumption SaaS pricing upgrades, role-based add-ons, or advanced capabilities unlocked through demonstrated need. This is product-led growth at its most customer-centric.

Underpinning all three motions is measurement discipline. I push for a unified analytics platform that ties together behavioral data, retention analysis, funnels, and cohorts with downstream CRM integration. That allows product trios to make fast, informed decisions and connect activation, support efficiency, and expansion to business outcomes. Whether your stack includes Pendo, Amplitude analytics, or custom pipelines, the principle is the same—one source of truth that informs action.

Execution matters as much as strategy. Empowered product teams working in tight product trios can ship small, valuable increments, run clean experiments, and learn faster than the market shifts. Strong stakeholder management and clear product roadmapping keep leadership aligned on outcomes vs output OKRs, so we’re funding what works and pruning what doesn’t. In my experience, this operational rigor is what turns promising ideas into durable competitive differentiation.

If you’re looking to operationalize these ideas, start by defining activation and expansion milestones that map to your value proposition. Instrument your in-app guides and product tours to support those milestones, and commit to an experimentation cadence with well-defined MDE. Layer in agentic AI carefully—pilot in the support surface where context is rich and stakes are clear—and enforce privacy and governance from day one. Finally, close the loop with unified analytics so every improvement compounds.

Pendo’s Summer Release highlights a broader reality: our industry isn’t inventing new destinations, we’re modernizing the routes. Onboarding, support, and expansion remain the pillars—but AI, in-app experiences, and integrated data make them smarter, faster, and more human. That’s the shift I’m leaning into—and the one customers feel immediately.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Design Four High-Impact Lifecycle Journeys with Pendo Orchestrate to Drive Retention

I’ve spent my career building product-led growth motions that deliver value fast and build durable retention. The most consistent pattern I’ve seen is simple: When we orchestrate timely, contextual guidance inside the product, customers discover value sooner, adopt core workflows more completely, and return more often. That’s exactly where Pendo Orchestrate shines for my team.

From first click to lifelong retention, you’ll deliver the right message at the exact right time, every step of the way. With Pendo Orchestrate, you can design those kinds of moments with intention. And in this blog, we’ll show you how.

At a high level, I map the customer lifecycle into four journeys—onboarding, activation, retention, and expansion—and align each to clear outcomes. Using targeted in-app guides and product tours, behavioral triggers, and segment-specific messaging, I can optimize each stage without overwhelming users. What follows is how I approach each journey to maximize time-to-value and retention.

Onboarding: I design progressive onboarding that adapts to a user’s role and first-run actions. Instead of a single, long product tour, I use short, contextual nudges that appear exactly when a user reaches a relevant screen or performs a key event. This reduces cognitive load, shortens time-to-value, and sets up a reliable path to initial success. When needed, I A/B test different sequences and measure impact on activation rate to ensure we’re improving the real user experience, not just adding more guidance.

Activation and habit-building: After first value, I focus on reinforcing the behaviors that correlate with long-term retention. Here, lightweight tooltips, celebratory moments when users reach the “aha” action, and just-in-time prompts for adjacent features help form habits. I track cohort-level activation metrics and use retention analysis to see whether these nudges translate into sustained product usage. If a segment stalls, I adjust copy, timing, or the sequence to better match user intent.

Retention and re-engagement: Not every customer stays on a steady path. For at-risk cohorts—users who haven’t completed a critical workflow or whose usage is declining—I trigger helpful, empathetic in-app guides that remove friction and offer a direct path back to value. I also solicit lightweight feedback to understand obstacles. The goal isn’t to interrupt; it’s to make it effortless to recover momentum.

Expansion and upsell: When users demonstrate readiness—mastery of core features, frequent usage, or role-based signals—I introduce advanced capabilities with targeted product tours and clear value propositions. Timing is everything; I prefer unobtrusive prompts that appear at the exact moment their workflow benefits from an upgrade. By matching message to milestone, expansion feels like a service, not a sell.

Operationalizing these journeys starts with crisp definitions of success (activation, adoption depth, and retention), thoughtful segmentation, and a cadence of experimentation. I keep the loop tight: instrument key events, launch small, measure outcomes, and iterate. Over time, the orchestration becomes a durable system—consistently delivering the right guidance to the right user at the right moment, and continuously compounding product impact.

If you’re looking to scale product-led growth, these four journeys provide a pragmatic blueprint. Start with the stage that’s hurting most (often onboarding), prove the lift, then expand. As outcomes improve, your users feel supported, your product experience feels intuitive, and your business earns the retention and expansion it deserves.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
4 Proven Ways GTM Teams Drive Explosive Growth with Pendo’s HubSpot Integration

In my role leading product management, I’ve learned that the most reliable path to product-led growth is aligning product signals with the systems our go-to-market teams use every day. That’s exactly where Pendo’s HubSpot integration shines—by merging behavioral insights with CRM context so sales, marketing, customer success, and product move in lockstep.

See how customer behavioral data can help sales, marketing, customer success, and product teams create a better, more engaging customer experience.

First, I use the integration to create a single source of truth that blends in-app behavior with account and contact data. When product usage, feature adoption, and intent signals flow into HubSpot, lead scoring becomes smarter, pipeline quality improves, and our go-to-market strategy gets more precise. Reps prioritize the right accounts, marketing tunes messaging to demonstrated needs, and we operate as a unified analytics platform instead of scattered tools.

Second, I activate lifecycle journeys directly from HubSpot using in-app guides and product tours. By targeting experiences based on CRM stage or persona, onboarding accelerates, trial conversion increases, and time-to-value drops. The ability to personalize onboarding without engineering work gives marketing and customer success a powerful lever to deliver exactly the right guidance at the right moment.

Third, I orchestrate customer success playbooks that reduce churn and expand revenue. Health scoring improves when retention analysis is informed by real product usage, not just survey sentiment. When usage dips below a threshold, HubSpot workflows trigger save-plays; when product engagement surges, we operationalize expansion motions across self-serve upgrades and account-based upsell. The result is a tighter feedback loop between product adoption and revenue outcomes.

Fourth, I close the loop between sales, product, and marketing to refine product positioning and roadmap priorities. Signals from Pendo in HubSpot highlight which features correlate with win rates and renewals, so we double down on the value proposition that actually converts. Those same insights inform targeted campaigns, sharper messaging, and a continuous learning cycle across GTM and product teams.

To make this work in practice, I start with clear event taxonomies, privacy-by-design data governance, and tightly scoped use cases that we can measure within a quarter. We iterate with small A/B tests, compare outcomes to baselines, and socialize wins across sales, marketing, and customer success to build momentum. The integration becomes more than a data pipe—it’s an operating system for coordinated growth.

When product signals meet CRM workflows, teams stop guessing and start executing with confidence. That’s the power of Pendo’s HubSpot integration: it operationalizes product-led growth across the entire customer journey, from first touch to expansion.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
The 5 Stages of Software Experience Maturity: What to Fix First to Unlock Growth

I’ve led product teams through chaotic launches, painful plateaus, and breakout growth, and one truth keeps showing up: software wins when the experience is intentionally designed, measured, and continuously improved. To make that work repeatable, I rely on a simple maturity framework that aligns our product strategy, analytics, and in-app experience work across the organization. Find out where you stand—and what to fix first—with this maturity framework. Why “software experience” and not just “features”? Because activation, adoption, and retention depend on how clearly users understand value in their first sessions, how seamlessly they complete key workflows, and how consistently they succeed over time. That’s where empowered product teams, product-led growth, and outcomes vs output OKRs come together to create durable results. Stage 1 — Ad Hoc: At this level, teams ship features without a clear sense of who benefits, how success is measured, or how UX writing and onboarding shape outcomes. If this is you, fix this first: define your activation events, instrument the core funnel, and write concise, in-product copy that reduces friction. Even a lightweight retention analysis will reveal where value drops off. Stage 2 — Instrumented Awareness: You’ve added basic analytics and can see signups, activations, and drop-offs, often via tools like Amplitude analytics or a unified analytics platform. What to fix first: translate raw metrics into hypotheses and prioritize a small set of A/B testing experiments. Use a minimum detectable effect (MDE) to size tests, and start tracking leading indicators tied to adoption—not vanity metrics. Stage 3 — Guided Journeys: Onboarding, in-app guides, product tours, and contextual tooltips now clarify value and reduce time-to-first-value. What to fix first: build a guided path to activation for your top two personas, then test microcopy and sequencing. Pair qualitative insights from user feedback with cohort-based retention analysis to ensure your guides create durable behavior change, not just clicks. Stage 4 — Outcome-Driven Execution: Teams set outcomes vs output OKRs, run disciplined experiments, and connect learnings to roadmap decisions. What to fix first: standardize an experimentation playbook with clear guardrails for MDE, sample sizing, and stop rules. Align quarterly bets with a value proposition narrative that ties product discovery to measurable, customer-centric outcomes. Stage 5 — Predictive and Proactive: You anticipate user needs with tailored experiences, automate nudges at the right moments, and systematize continuous discovery. What to fix first: unify data across product, support, and lifecycle channels to personalize experiences without eroding privacy-by-design. Invest in scalable governance so insights flow to product trios and forward deployed engineers quickly and safely. How to use this framework: honestly score your current stage across analytics, onboarding, guidance, experimentation, and decision-making. Then pick the single change that removes the biggest bottleneck to the next stage—often a measurement gap, not a feature gap. Make improvements visible through product roadmapping and sprint planning, and celebrate progress to reinforce empowered product teams. In practice, maturity is not a badge; it’s a habit. When we pair rigorous analytics with thoughtful in-app experiences and clear strategic outcomes, we compound learning and unlock growth. If you’re unsure where to begin, start small: instrument activation, improve one critical guide, and run one high-quality experiment. Momentum follows.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
How I Scale Revenue with Pendo Predict: Cut Costs, Reduce Risk, and Drive Product Adoption

When my team and I set out to accelerate growth without ballooning costs, we leaned into Pendo Predict as a keystone of our product-led growth strategy. Predict gives us a practical, data-driven way to focus on the right users at the right moments, align teams around measurable outcomes, and turn product usage signals into revenue impact.

“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.” That statement maps exactly to how we operate: we use the platform to understand user behavior, guide users through high-value actions, and instrument the experience so we can learn, iterate, and scale with confidence.

To scale revenue, we identify high-intent segments based on product behaviors and run targeted in-app guides and product tours that shorten time-to-value and boost conversion. Predict helps us surface which features correlate with expansion and retention, so our onboarding flows nudge users into those paths. This approach compounds: better activation drives stronger engagement, which fuels a healthier pipeline for cross-sell and upsell.

On the cost side, we reduce support load with contextual guidance—tooltips, checklists, and just-in-time education—so customers self-serve through common friction points. We consolidate insights in a unified analytics platform, enabling product, success, and go-to-market teams to work from the same source of truth. The result is fewer reactive escalations, tighter prioritization, and more engineering time invested in features that move retention and revenue.

Risk reduction comes from visibility and control. With predictive signals and retention analysis, we spot churn risk early, intervene with timely in-app messaging, and de-risk launches by rolling out features to targeted cohorts while monitoring adoption and engagement. We pair this with disciplined experimentation and A/B testing to validate changes before scaling broadly.

If you’re considering a similar motion, a simple playbook works: define your adoption and engagement metrics, instrument key workflows, create predictive segments, ship focused in-app guides, and measure impact against outcomes—not just outputs. Over time, this turns your product into a durable growth engine that consistently improves user experience and business performance.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
How AI Is Supercharging Product Design: Faster Prototyping, Smarter Testing, and Better UX Outcomes

AI has fundamentally changed how I lead design and testing, not by replacing craft, but by compounding it. When my teams pair generative models with time‑tested product management practices, we move faster, learn sooner, and ship with more confidence—without compromising privacy-by-design or quality. The result is a tighter loop from product discovery to product-market fit lessons. Learn how Pendo’s product design team is using genAI and traditional tools to speed up design and development. That single line captures my own operating model: blend genAI with established toolchains to accelerate, not shortcut. In practice, I treat AI as a force multiplier for product trios—PM, design, and engineering—so empowered product teams can explore broader solution spaces while staying anchored to outcomes vs output OKRs. In discovery, genAI helps me synthesize qualitative inputs at scale—interviews, support threads, and in-app behaviors—into testable opportunity statements. I triangulate those insights with a unified analytics platform and Amplitude analytics to spot friction, then use in-app guides and product tours to target learning, recruit the right cohorts, and validate problems before we overbuild. For prototyping, gen ai for product prototyping is a game-changer. I generate multiple UX writing variants, microcopy, and flows in minutes, then narrow the set using heuristics and stakeholder feedback. Before any A/B testing, we precompute the minimum detectable effect (MDE) and sample size, making sure our experiments are powered to detect meaningful differences, not noise. In testing, I combine classic A/B testing with AI-assisted analysis to surface patterns faster. GenAI drafts experiment summaries, flags anomalous segments, and proposes follow-up tests, while my team makes the final calls. We deploy targeted in-app guides to onboard users into trials, monitor adoption via event telemetry, and iterate quickly until the value proposition is unmistakable. Execution depends on rigor and guardrails. We codify AI risk management and data governance policies, keep humans-in-the-loop for critical judgments, and log model prompts and outputs for auditability. This lets us move with speed and integrity, aligning stakeholder management, product roadmapping and sprint planning, and go-to-market strategy around measurable outcomes. The payoff is material: shorter cycle times, clearer value narratives, and stronger product-led growth curves. By fusing genAI with traditional practices, we preserve the craft of design while scaling our capacity to learn. That’s how we differentiate—through faster insight generation, smarter testing, and experiences that feel unmistakably intuitive.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
The Real Reason Pendo Built Agent Analytics—and How It Drives Adoption, Revenue, and Trust

I’ve learned the hard way that the toughest part of launching in-app agents and guided experiences isn’t the build—it’s proving, quickly and credibly, that they move the business. If I can’t quantify adoption, engagement, deflection, and time-to-value, stakeholder confidence erodes and iteration slows. That’s exactly why an Agent Analytics capability matters: it turns opaque interactions into measurable outcomes that product, customer success, and engineering can all act on.

When I evaluate a capability like Agent Analytics, I anchor on a few questions. Which segments adopt the agent, and where does engagement drop? What fraction of issues are successfully deflected versus escalated? Which prompts, product tours, and in-app guides drive conversion and retention—and which add friction? How does agent usage correlate with onboarding completion, core feature activation, and long-term retention analysis? If I can answer those with a unified analytics platform, I can prioritize confidently.

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.

In practice, I map an outcomes-first measurement plan: define a north-star (e.g., activated accounts), articulate contributing metrics (guide completion rate, agent task success, session depth), then run targeted A/B testing on copy, timing, and placements. With the right analytics, I can compare cohorts exposed to in-app guides and product tours against a control, validate impact, and double down on the patterns that consistently improve adoption and stickiness.

Cost and risk are just as important as growth. An effective Agent Analytics view helps me model support deflection, time-to-resolution, and escalation rates so I can quantify cost savings without sacrificing quality. On the risk side, I look for early-warning signals—low-confidence responses, repeated handoffs, or anomalous usage—so I can intervene before they turn into churn or brand concerns. The point isn’t vanity metrics; it’s operational clarity that enables responsible, scalable product-led growth.

This also changes team dynamics. Product trios get a shared source of truth for decisions, engineering gains sharper specs informed by real behavior, and customer-facing teams can see which experiences reliably unlock value for each segment. Instead of debating opinions, we iterate on evidence—tightening the loop between product roadmapping and sprint planning, UX writing, and go-to-market strategy.

My 90-day playbook looks like this: establish a baseline for adoption and engagement; instrument agent interactions end to end; ship two or three small, high-leverage experiments in onboarding and help experiences; and review results in weekly rituals. By day 90, I expect to see a clear line from agent engagement to activation and retention, along with a repeatable testing cadence that compounds learning.

I’ve seen the same pattern across products and markets: once teams illuminate the black box of in-app assistance with rigorous, actionable analytics, customer confidence rises, onboarding accelerates, and roadmaps get sharper. If you’re evaluating Pendo or already running it, put Agent Analytics at the center of your measurement strategy—and let your data, not assumptions, guide the next iteration.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
5 powerful reasons I can’t wait for INDUSTRY 2025: The Product Conference to supercharge strategy

I’m gearing up for INDUSTRY 2025: The Product Conference in Cleveland, Ohio, and I can already feel the energy that comes when the brightest product minds gather. As someone who lives at the intersection of product management leadership, execution discipline, and customer-centric innovation, this event is where I refine my craft and pressure-test my roadmap against the best.

Join Pendo at INDUSTRY in Cleveland, Ohio.

Reason 1: Elevate strategy from outputs to outcomes. I’m looking forward to sharpening how we align outcomes vs output OKRs with product roadmapping and sprint planning. INDUSTRY consistently surfaces practical frameworks to translate vision into measurable value—exactly what empowered product teams need to prioritize with confidence and communicate trade-offs to stakeholders.

Reason 2: Deepen discovery with data that actually drives decisions. I plan to compare notes on product discovery techniques that blend qual and quant—pairing interviews with a unified analytics platform, retention analysis, and a clear minimum detectable effect (MDE) to validate signal. The bar keeps rising on evidence-based decisions, and I’m eager to bring back new ways to reduce bias while accelerating learning.

Reason 3: Double down on product-led growth. From onboarding to activation, I’m focused on refining in-app guides and product tours that meet users at the moment of need. INDUSTRY is a great place to trade patterns for scalable, context-aware experiences that convert, retain, and expand without adding friction—fueling a durable product-led growth motion.

Reason 4: Build a responsible, practical AI Strategy. The conversations around gen ai for product prototyping, agentic AI, data governance, and privacy-by-design are evolving fast. I’m excited to learn how teams are balancing speed with AI risk management—turning experimentation into real features while protecting customers and preserving trust.

Reason 5: Level up leadership and influence. Product management leadership is as much about people as it is about prioritization. I’m excited to trade tactics on stakeholder management, strengthening product trios, and growing ICs through the IC to manager transition. These are the muscles that turn strategy into momentum.

Between keynotes, hallway conversations, and hands-on sessions, I plan to leave Cleveland with fresh approaches to discovery, clearer OKR alignment, and new ideas to operationalize PLG at scale. If you’re passionate about building products that customers love—and businesses rely on—let’s connect and compare notes on what’s working now.

I’ll share my takeaways after the conference, including actionable frameworks, templates, and experiments to run with your teams the very next sprint. If you see me in a session on analytics, onboarding, or AI, say hello—I’m always up for a quick debrief and a few what-would-it-take questions.

Inspired by this post on Pendo – Perspectives.

October 24, 2025