Tag: in-app guides

Director of Product, Growth & AI at Amplitude: My Playbook for Viral Growth and Engagement

I see the Director of Product, Growth & AI at Amplitude as a mandate to operationalize "viral and core growth strategies, user acquisition, and product engagement" with precision. From my vantage point, that means building a rigorous, metrics-first operating system grounded in Amplitude analytics and product-led growth principles, then layering in an AI Strategy that personalizes experiences without sacrificing control or safety.

I start by defining a clear North Star Metric and mapping a driver tree to expose causal levers across acquisition, activation, engagement, retention, and monetization. With behavioral analytics and cohort analysis, I quantify which user behaviors correlate with long-term value. I operationalize rapid experimentation through A/B testing with sensible minimum detectable effect (MDE) thresholds, guardrail metrics, and sequential testing to ensure we move fast while preserving measurement integrity.

For "viral and core growth strategies," I lean on durable growth loops more than one-off hacks. Viral loops might include collaboration invites, user-generated content, and shareable artifacts that make the product more valuable as it spreads. Core growth centers on frictionless activation: guided onboarding, in-app guides, product tours, progressive disclosure, and judicious tooltip design that connects users to the ‘aha’ moment quickly. Session replay and funnel instrumentation help isolate friction and systematically remove it.

On user acquisition, I connect performance channels and go-to-market strategy tightly to in-product activation. Rather than optimizing for clicks, I optimize for post-signup behaviors that predict retention. This includes improving landing page-message-product congruence, refining qualification (so top-of-funnel aligns with downstream value), and orchestrating lifecycle messaging that nudges users toward key activation milestones.

To deepen product engagement, I focus on leading indicators of retention and feature adoption. I segment by jobs-to-be-done and intent, then personalize in-app prompts to surface the right capability at the right moment. Retention analysis, pathing, and funnel breakouts inform which nudges to deploy and where—whether that’s smarter checklists, contextual education, or lightweight in-product interventions that turn sporadic usage into reliable habits.

AI raises the ceiling on what’s possible here. With a thoughtful AI Strategy, I use gen ai to personalize onboarding flows, recommend next-best actions based on behavioral signals, and summarize complex activity patterns into actionable insights for the team. I maintain strict measurement: every AI intervention ships behind feature flags, is evaluated through controlled experiments, and adheres to privacy-by-design principles. The outcome is a system that learns continuously while staying aligned to business and user outcomes.

Execution is where strategy becomes real. I rely on empowered product trios, continuous discovery with customers, and outcome-focused roadmaps that tie directly to the driver tree. This keeps the organization moving in sync: engineering prioritizes the highest-signal experiments, design accelerates comprehension and task success, and product ensures each release strengthens the core loop rather than adding ornamental features.

Ultimately, the blueprint is simple and disciplined: anchor on "viral and core growth strategies, user acquisition, and product engagement," quantify what matters with behavioral analytics, and iterate through well-instrumented experiments. Combine that with targeted AI augmentation, and you create a compounding growth engine that is both measurable and resilient.

Inspired by this post on Amplitude – Perspectives.

June 5, 2026
Stop Support Tickets Before They Start: How AI Unsticks Users and Lifts Conversions

Every moment of friction in a product carries a hidden cost: attention drifts, motivation wanes, and the next click becomes a support ticket—or worse, silent churn. Over the years, I’ve learned to treat “stuck” as an urgent product signal, not just an operational nuisance. When we unstick users in the flow, we protect revenue, brand trust, and the momentum that powers product-led growth.

Learn how Amplitude’s Global Support team uses AI Assistant to reduce support tickets, prevent user churn, and increase conversions.

I reference that line often because it captures a proven pattern: meet users where confusion peaks and resolve it instantly. In my practice, the formula is straightforward—pair behavioral analytics and session replay with a just-in-time AI Assistant, routed by clear driver trees. This transforms support from reactive firefighting into a proactive, in-product experience that accelerates onboarding and boosts user activation.

Here’s how I operationalize it. First, I use Amplitude analytics and behavioral analytics to surface high-friction steps—pages with elevated drop-off, loops, or rage clicks. Session replay clarifies the “why” behind the numbers, while cohort and retention analysis reveal who’s most at risk. Then I deploy targeted in-app guides and tooltip design to preempt known pitfalls, while an AI Assistant handles real-time questions with context from our knowledge base and product docs.

The AI Assistant is more than a chatbot. With well-structured AI workflows, it detects intent, pulls precise snippets from docs-as-code, and handles routine issues instantly. When complexity spikes, it executes a graceful handoff to consultative support via Intercom or a Zendesk integration—preserving conversation history and sentiment cues—so humans spend time where judgment matters. This hybrid model keeps response times low without sacrificing quality.

To de-risk changes, I lean on A/B testing and feature flags. I measure time-to-value, activation rate, and funnel conversion as leading indicators, while tracking ticket deflection, CSAT, and NRR as trailing indicators. The goal isn’t just fewer tickets; it’s faster learning loops and a compounding improvement in user outcomes. When we see activation curves steepen and onboarding friction flatten, we know the system is working.

Practically, I start with the top three friction points in onboarding, implement narrow in-app guides, and deploy the AI Assistant with strict guardrails and clear escalation paths. Weekly reviews align product, customer success, and solutions engineering around shared telemetry—so we tune prompts, content, and UI patterns together. Over time, I’ve seen ticket volume decline meaningfully, while conversion and retention rise as users experience fewer dead ends.

If you’re evaluating where to begin, identify the moments where confusion compounds—pricing configuration, integrations, and data mapping are common culprits. Then introduce targeted, context-aware help right where users hesitate. You’ll not only prevent “every stuck user” from turning into a ticket—you’ll convert friction into confidence, and confidence into growth.

Inspired by this post on Amplitude – Best Practices.

June 1, 2026
Stop Chasing Churn: How Behavioral Analytics Powers Proactive Retention in SaaS

Churn is a lagging indicator—and by the time I see it in a dashboard, the moment to change a customer’s mind has usually passed. At HighLevel, I’ve learned that durable retention starts long before a cancellation ticket, with product-led growth habits, customer success partnerships, and a clear view of user behavior that flags risk early and often.

Stop chasing SaaS churn after it happens. Learn how proactive product and service experiences, powered by behavioral analytics, help reduce churn before users leave.

My operating model is simple: treat retention as a design problem, not a rescue mission. I anchor our strategy in behavioral analytics and retention analysis, translating leading indicators—activation milestones, time-to-first-value, depth of feature adoption, and expansion intent—into outcomes like Net Recurring Revenue (NRR) and cohort-based retention. When these inputs move in the right direction, churn becomes the exception, not the trend.

To get there, I start with rigorous journey mapping and continuous discovery. We define the exact “aha” moments that signal value realization, instrument events across the funnel, and segment cohorts by persona, plan, and use case. Tools in a unified analytics platform (e.g., Amplitude analytics or Pendo) help us pinpoint where engagement decays, which features predict stickiness, and which friction points block activation. This evidence replaces hunches and lets us prioritize the highest-leverage work.

From those signals, I build a transparent risk score that anyone can use. It blends usage momentum (DAU/WAU), core feature frequency, anomaly detection on key behaviors, billing and payment health, and support sentiment. When the score crosses a threshold, we trigger plays—inside the product and through customer success—so we’re helping users before they drift, not pleading after they’ve left.

On the product side, I favor lightweight, contextual interventions: in-app guides tailored to stalled tasks, checklists that shorten time-to-value, adaptive product tours, and tooltip design that clarifies the next best action. We A/B test these experiences with a clear minimum detectable effect (MDE), watching both local metrics (feature completion, error rate) and global metrics (activation, retention). The goal is precision—right nudge, right user, right moment—without adding cognitive load.

On the service side, we run consultative support and customer success plays keyed to the same behavioral triggers. A sudden drop in core usage may prompt a quick diagnostic call; repeated failed integrations can route to solutions engineering; stalled accounts get value reviews or QBRs focused on outcomes, not feature checklists. Because product and service draw from the same data, customers experience a single, coherent journey.

Proactive retention also depends on smart packaging and pricing. When value metrics mirror how customers win, plan boundaries reinforce the right behaviors and reduce “silent churn” caused by misaligned tiers. Outcome-based pricing and clear upgrade paths can turn potential risk into expansion rather than attrition.

Operationally, I keep a weekly retention review with product trios and customer success leaders. We walk driver trees from inputs (activation, engagement depth, support friction) to outputs (NRR, churn), review session replay where confusion spikes, and commit to small, measurable experiments. This cadence compounds learning and keeps us honest about what’s moving the needle.

If you’re starting fresh, begin with four moves: define an activation milestone tied to value; instrument the few events that prove users are on track; build a basic risk score from those events; and craft three plays—one in-product, one lifecycle message, one success outreach—triggered by that score. You’ll create a flywheel where insights power interventions, and interventions feed better insights.

Churn will always exist, but it doesn’t have to be a cliff. With behavioral analytics guiding both product and service experiences, we can make retention the natural outcome of how we build, communicate, and support—long before a customer ever thinks about leaving.

Inspired by this post on Amplitude – Perspectives.

May 18, 2026
Making (Great) Data Flow Effortless in Amplitude to Unlock Faster Activation and Product-Led Growth

On the Amplitude growth team, the mission is clear: make it easier than ever to get (great) data flowing in Amplitude. That focus resonates deeply with me because, in my experience leading product organizations, nothing accelerates value creation faster than clean, trustworthy behavioral data reaching the right people at the right moment.

When Amplitude analytics is fueled by high-quality event streams, product teams can move from guesswork to precision. With consistent, enriched signals, behavioral analytics becomes a daily superpower—shortening time-to-first-insight, sharpening user activation strategies, and aligning everyone on outcomes. This is the foundation of a unified analytics platform that actually drives product-led growth.

“Great” data isn’t accidental; it’s designed. It starts with a clear tracking plan, human-readable event names, and strict schema validation. It continues with robust data governance, CI/CD-friendly instrumentation, and docs-as-code so analytics definitions don’t drift. When teams instrument once and trust forever, they reduce thrash, avoid rework, and build a durable decision-making muscle across product, engineering, and customer success.

The payoff shows up where it matters: onboarding becomes clearer, user activation improves, and experiments become more conclusive. With in-app guides and thoughtful product tours reinforced by reliable event data, I can see where users hesitate, why they drop, and which nudges actually help them succeed. That makes it easier to prioritize the highest-leverage changes and to communicate impact credibly to stakeholders.

I’ve repeatedly seen teams cut weeks of analysis down to days once they standardize event taxonomies, automate QA for instrumentation, and establish lightweight governance. The result is a smoother path to retention analysis, faster iteration on activation milestones, and a culture that treats data as a first-class product—not an afterthought.

Ultimately, making it effortless to get (great) data flowing in Amplitude is about dignity for the end user and leverage for the business. It’s how we turn curiosity into clarity, align teams around measurable outcomes, and scale product-led growth with confidence.

Inspired by this post on Amplitude – Best Practices.

April 30, 2026
Why Your Product Needs a Smarter Support Agent: Data-Driven, Agentic AI That Truly Helps

Your product deserves a support experience that does more than point users to a help article. In my work leading product teams, I’ve seen how an intelligent, in-product assistant can reduce friction, accelerate user activation, and create the kind of product-led growth that traditional support channels struggle to deliver. The bar is higher now: customers expect immediate, context-aware help that feels proactive, measurable, and trustworthy.

When I evaluate support solutions, I look for three capabilities: an assistant that truly knows the user’s context, can act on their behalf to resolve issues end-to-end, and can prove the impact with rigorous measurement. Anything less is just another interface to your knowledge base. The shift to agentic AI makes this possible—if it’s grounded in behavioral analytics and integrated with your unified analytics platform.

Learn more about Amplitude AI Assistant. Our in-product support agent knows your users, acts on their behalf, and measures whether it actually helped.

That promise resonates with how I design AI Strategy: start with data fidelity, not dialog. When an assistant is wired into Amplitude analytics and behavioral analytics, it can understand where a user is in the journey, the features they have (or haven’t) adopted, and which nudges or in-app guides historically drive success. This is the foundation for precise, contextual help—surfacing the right product tours at the right moments and removing guesswork.

Knowing users isn’t enough; the assistant must act. With agentic AI, the assistant can execute safe, auditable steps on a user’s behalf—updating settings, triggering a workflow, or guiding a multi-step configuration—rather than handing off a to-do back to the customer. Done well, this reduces time-to-value and support tickets while aligning with a thoughtful customer support ai strategy that respects permissions, privacy-by-design, and clear guardrails.

Equally important is measurement. I expect every AI touchpoint to demonstrate lift: faster time-to-resolution, higher feature adoption, improved retention, and lower churn. This is where robust A/B testing, Agent Analytics, and retention analysis come in—so we can quantify the assistant’s contribution against meaningful product outcomes, not vanity metrics. If we can’t measure it, we can’t manage it.

Operationally, I advise teams to pilot with narrowly scoped, high-impact journeys and iterate with tight feedback loops. Instrument the assistant’s actions and outcomes, set minimum detectable effect thresholds for experiments, and continually refine prompts and playbooks. Tie insights back to your unified analytics platform so learnings inform roadmap choices and reinforce a durable product-led growth motion.

In short, the next generation of in-product support will be built on data-rich context, agentic execution, and rigorous proof of value. That’s the standard I hold my teams to—and the experience users deserve when they ask for help.

Inspired by this post on Amplitude – Best Practices.

April 21, 2026
Build vs. Buy for Churn Prediction: My Proven Playbook for Faster Retention and ROI

Churn is the silent tax on growth, and I treat churn prediction as a core product capability—not a side project. Over the years, I’ve led teams through multiple implementations across different data maturities and go-to-market motions, and the same question keeps returning at kickoff: what’s the smartest path to impact now and defensibility later?

“Should you build or buy your churn prediction model?” The right answer depends on time-to-value, data readiness, available talent, and whether churn prediction is a true differentiator for your product strategy or simply a must-have capability to power customer success and product-led growth.

When speed and coverage matter most, I start by evaluating category platforms that pair behavioral analytics with activation. As one example, vendors emphasize immediate business outcomes such as integrations, in-app guides, and workflow triggers that help you act on risk signals fast—without waiting months for model training or data engineering.

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.

Buying makes sense when you need rapid time-to-value, opinionated best practices, and a unified analytics platform to operationalize insights through product tours, in-app guides, and CRM integration. In these cases, I’m optimizing for coverage, consistent signal quality, and ease of activation for customer success—so the team can focus on interventions, not infrastructure.

Building is compelling when churn prediction is a source of competitive differentiation or you have proprietary signals others can’t access. If your product generates unique behavioral data, requires custom anomaly detection or explainability constraints, or must blend usage telemetry with domain-specific risk scoring, a tailored model can raise precision and unlock novel retention levers.

My hybrid approach has become a reliable playbook: buy first to establish a strong baseline and close the activation loop, then selectively build where proprietary data and context yield outsized gains. I use retention analysis to identify high-signal behaviors, then iterate with A/B testing and a clear minimum detectable effect (MDE) to validate uplift before committing engineering capacity.

Total cost of ownership is non-negotiable. I account for more than license or training costs: ongoing data engineering, feature pipeline maintenance, model monitoring for drift, and AI risk management all add up. Strong data governance, privacy-by-design, and regulatory compliance must be baked in—whether I build, buy, or blend both.

Activation determines real ROI. Predictions that don’t flow into customer success workflows, lifecycle messaging, or in-product nudges rarely move Net Recurring Revenue (NRR). I prioritize tight integrations that enable targeted experiments—journey mapping, contextual tooltips, and timely outreach—to reduce friction and increase user engagement at the moments that matter.

My quick decision test: buy if time-to-value and adoption are the immediate goals; build if proprietary signals and explainability are core strategic assets; blend if you want fast wins now with room to differentiate later. Answering the build vs. buy question through this lens consistently improves retention, accelerates product-led growth, and keeps teams focused on the customer experience rather than plumbing.

Inspired by this post on Pendo – Perspectives.

April 16, 2026
Net Recurring Revenue Mastery: How Elite CS Teams Drive Expansion, Retention, and Growth

Net Recurring Revenue (NRR) is the clearest signal of whether our product, pricing, and customer success motions are compounding value or quietly leaking it. When I review our dashboard, NRR tells me—in one number—how well we retain, expand, and engage customers. It’s the difference between linear progress and durable, compounding growth.

At its core, NRR answers a simple question: did revenue from our existing customers grow or shrink this period? The standard way I frame it is: NRR = (Starting MRR + Expansion – Contraction – Churn) / Starting MRR. Expansion reflects upsells, cross-sells, and increased usage; contraction and churn capture downgrades and departures. Great teams don’t just watch this number—they engineer it.

The teams that consistently outperform treat NRR as an outcome of intentional design across the entire customer journey. They align product-led growth with customer success, weaving onboarding, user activation, in-app guides, and lifecycle messaging into one coherent system. They make adoption the star of the show, not an afterthought tucked beneath quarterly targets.

To scale that system efficiently, I lean on platforms that streamline in-app guidance and rich behavioral analytics. The promise is crisp and concrete: “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.” When the experience is instrumented end to end, expansion opportunities show up as patterns, not surprises.

Retention analysis is where the signal gets sharp. I segment cohorts by plan, size, and use case; map their journey; and run driver trees that connect leading indicators (activation depth, feature breadth, time-to-value) to the lagging outcome (NRR). This turns hunches into hypotheses and gives customer success managers a prioritized playbook, not a long wish list.

Onboarding is the first and most powerful NRR lever. The faster a customer experiences their first win, the more likely they are to adopt core features, invite teammates, and expand. I use in-app guides, product tours, and contextual tooltips to pave the path to value—always grounded in clear jobs-to-be-done, not generic walkthroughs. The goal is simple: remove friction, celebrate progress, and make the next best action obvious.

Operating cadence matters as much as tooling. I separate the rhythms: QBRs for strategic alignment and expansion planning; OKRs for cross-functional execution and accountability. QBRs anchor the conversation in outcomes and value realized; OKRs ensure product, marketing, and CS move in lockstep to close the gaps those QBRs reveal.

Pricing and packaging complete the loop. When the value proposition is clear and plans are aligned to outcomes customers care about, expansion feels natural—more capability for more value. Usage insights guide which features to gate, which to bundle, and where to price to maximize retention while unlocking healthy upsell paths.

None of this works without tight product–CS collaboration. My teams practice continuous discovery—customer interviews, win/loss insights, and in-product feedback—so we improve the experience where it truly matters. Journey mapping turns those insights into experiments, and experiments turn into polished features once the data speaks.

I build an NRR driver tree into our weekly reviews. Each branch (activation, adoption, multi-seat expansion, downgrade prevention, reactivation) has a clear owner, a measurable hypothesis, and a time-bound experiment. A/B testing guides what we ship broadly, and we define success upfront to avoid moving goalposts after the fact.

I’ve seen NRR climb meaningfully in a single quarter when we pair rigorous retention analysis with targeted onboarding improvements and value-based packaging. The lift rarely comes from one big bet; it’s the compounding effect of many small, well-instrumented decisions.

Here’s the 90-day play I return to: first, baseline NRR by segment and identify the top three drivers of expansion and the top three causes of contraction. Next, streamline onboarding with in-app guides and product tours that accelerate time-to-value and drive user activation. Then, craft expansion plays aligned to real outcomes (additional seats, advanced workflows, new use cases), and operationalize them via QBRs. Finally, preempt downgrades with early-warning alerts, targeted education, and a clear path from “stuck” to “successful.”

NRR is a team sport. When product, customer success, and go-to-market align around adoption and outcomes, growth compounds, risk declines, and every customer interaction becomes a chance to create more value—today and in every renewal to come.

Inspired by this post on Pendo – Perspectives.

April 13, 2026
Stop Forcing AI to Prove ROI: A Product Leader’s Playbook to Measure Real Business Value

Every planning cycle, I feel the drumbeat: “Show me the AI ROI—this quarter.” The pressure is real, especially when boards and CFOs expect immediate payback. Yet when I review stalled initiatives across teams and peers, the pattern is consistent: most companies treat AI like a feature to ship, not a system to manage. That mindset almost guarantees we measure the wrong things, declare victory (or failure) too early, and miss the durable value AI can create.

Here’s the core problem I see: we leap to solution and skip the counterfactual. Without a baseline, a clear control, or a defined “what would have happened otherwise,” we’re guessing. We also fixate on lagging, financial KPIs that move slowly (revenue, cost, risk), then use outputs—not outcomes—as OKRs. If we don’t align on outcomes vs output OKRs upfront, the best team in the world can still optimize for activity over impact.

My AI Strategy starts from a simple truth: value shows up along three vectors—revenue, cost, and risk—on different timelines. In the near term, we must validate leading indicators (adoption, engagement, activation) that ladder to those vectors through a transparent driver tree. Over time, those drivers compound into the lagging KPIs finance cares about. When we make the driver tree explicit, everyone can see how model precision, response time, and workflow integration roll up to conversion lift, case deflection, time-to-resolution, or reduced exposure.

To make this rigorous, I run a five-step playbook. First, define the decision and business outcome in plain terms. Second, instrument the baseline with behavioral analytics on a unified analytics platform—tools like Amplitude analytics or Pendo help expose friction points we’ll later target. Third, create a counterfactual using A/B testing and specify a minimum detectable effect (MDE) so we know how long to run and how much traffic we need. Fourth, quantify costs (training, inference, integration, change management) and include AI risk management, privacy-by-design, and data governance up front. Fifth, lock a measurement plan that connects leading indicators to lagging ROI through the driver tree.

Most AI initiatives don’t fail on model quality—they fail on adoption. If the workflow isn’t smoother, trust isn’t earned, or value isn’t obvious, users revert. That’s why I invest early in onboarding, in-app guides, product tours, and thoughtful tooltip design to reduce the time-to-first-value. Then I watch user activation, retention analysis, and task completion to ensure the assistive experience is not just novel—it’s habit-forming.

For generative use cases, eval-driven development is non-negotiable. I maintain offline evaluations for accuracy and safety, and online evaluations for business impact. Retrieval-first pipeline health, context window management, and prompt engineering affect reliability; so do latency and grounding quality. We ship behind feature flags, measure guardrail effectiveness, and tighten feedback loops from human-in-the-loop reviews into model updates—continuously.

On the business side, I avoid “AI theater” by structuring benefits like a CFO. Revenue: increased conversion or expansion driven by better recommendations, faster sales cycles, or higher trial activation. Cost: case deflection, agent time saved, fewer escalations, and lower rework. Risk: reduced exposure via automated checks, anomaly detection, and consistent policy application. If any claim can’t be tied to measured deltas—via A/B testing or strong quasi-experiments—it doesn’t go in the deck.

Build vs buy deserves the same discipline. I map platform scalability, governance requirements, and total cost of ownership against time-to-impact. Teams often underestimate integration and maintenance drag; a pragmatic mix of bought components with thin custom layers can accelerate outcomes while keeping options open. The goal isn’t to own every layer—it’s to own the learning loop and the differentiated experience.

I also remind teams that tooling should serve the strategy, not replace it. I’ve seen concise, effective messaging that captures the point: “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.” The words are compelling because they reflect the three-vector value model and the adoption imperative. The same standard should apply to any AI initiative we propose.

If you’re under pressure to prove ROI, shift the conversation: lead with the driver tree, specify your counterfactual, and anchor on leading indicators you can move in weeks—not quarters. Then connect those to the lagging KPIs finance expects over time. When we manage AI like a product—grounded in evidence, experimentation, and user-centered adoption—we don’t have to force ROI. We compound it.

Inspired by this post on Pendo – Perspectives.

April 8, 2026
5 powerful ways I use Pendo MCP to bring product analytics into ChatGPT, Claude, and Cursor

I’ve wanted my product analytics to follow me into every conversation, doc, and code review. Now they do—and it changes how quickly I can move from question to insight to decision.

Pendo is now available as an MCP (Model Context Protocol) server, easily accessible in Claude, ChatGPT, and Cursor.

Practically, this means my core product analytics, segments, and qualitative feedback can be surfaced right where I plan sprints, refine opportunity solution trees, and write specs. Fewer context switches, tighter feedback loops, and faster product decisions.

Here are five ways I put Pendo MCP to work across my day-to-day workflows—grounded in product management leadership habits and built for speed and clarity.

1) Daily triage and decision support: In ChatGPT or Claude, I quickly query product analytics to spot anomalies, usage spikes, or drop-offs by segment. Prompts like “Highlight top features by week-over-week growth and flag statistically notable anomalies” help me focus standups on what matters, tightening the loop between observability and action.

2) Continuous discovery prep: Before customer interviews, I pull recent NPS verbatims, feature adoption by persona, and journey mapping signals. In seconds, I have a concise brief that blends behavioral analytics with customer interviews, so I can ask sharper questions and validate assumptions faster—without leaving my AI workspace.

3) Evidence-based prioritization: When shaping the roadmap, I bring in retention analysis, user activation metrics, and cohort views to weigh impact vs. effort. Using Pendo MCP inside Claude or ChatGPT, I translate insights into driver trees and a clear product strategy narrative that aligns stakeholders around outcomes, not output.

4) Product-led growth and onboarding: I review onboarding funnels, identify friction in first-run experiences, and draft in-app guides and tooltip copy that meets users at the exact drop-off points. With Pendo MCP, the context for product tours and in-app guides is right where I’m writing, so iteration cycles stay tight and data-informed.

5) Customer success and QBR prep: For account health and QBRs vs OKRs alignment, I generate succinct summaries of feature adoption, sentiment, and value realization—ready to paste into email, decks, or a CRM integration. This keeps sales-led and product-led growth motions unified, with a single source of truth visible in ChatGPT, Claude, or when I’m coding in Cursor.

The net effect: higher-quality decisions, faster. By bringing product analytics into my AI workflows, I reduce context switching, improve context window management, and keep my team anchored to real user behavior. Wherever I’m working—ideating in Claude, drafting in ChatGPT, or reviewing code in Cursor—my Pendo context is right there with me.

If you’re leading empowered product teams, this is a pragmatic way to operationalize continuous discovery, speed up alignment, and turn insights into outcomes. It’s a simple shift with outsized leverage.

Inspired by this post on Pendo – Best Practices.

April 1, 2026
Docs-as-Code Leadership at Scale: How Jeff Scattini Elevates End-to-End Product Documentation

Great products aren’t just shipped; they’re understood. In my product management practice, the difference between a good release and a great one often comes down to disciplined documentation that moves at the speed of delivery. That’s why the docs-as-code approach has become a cornerstone of how I build, lead, and measure product experiences across teams.

As I reflect on leaders who set a high bar in this craft, one description stands out: "With years of experience as Senior Documentation Manager, Jeff leads teams and oversees the end-to-end creation of documentation using docs-as-code methodology." That concise statement captures a model I deeply respect—one that treats documentation as a first-class citizen in the product lifecycle.

In practice, docs-as-code integrates documentation into CI/CD pipelines, version control, and peer review workflows—exactly how we ship software. This elevates quality, enforces consistency, and accelerates responsiveness to change, all while enabling rigorous content audit and UX writing standards. When documentation evolves with code, it becomes discoverable, testable, and measurable—key traits for scalable product management leadership.

The downstream impact is tangible. Users ramp faster through onboarding, in-app guides, and product tours because the narrative aligns with the product’s true state at any given commit. Support tickets drop, developers work with greater clarity, and PMs gain the feedback loops needed for continuous discovery. In a product-led growth motion, this clarity compounds—reducing time-to-value and enabling teams to ship confidently.

Equally important is the leadership pattern behind the methodology: aligning product, engineering, and customer-facing teams around shared truths. I’ve seen empowered product teams operate at their best when documentation is embedded in planning, sprint reviews, and release gates. This creates a single source of truth that scales knowledge, preserves intent, and shortens the path from decision to delivery.

For me, the standard expressed above isn’t just a role description—it’s a blueprint for operational excellence. When we manage documentation with the same rigor as code, we build trust at every touchpoint and create the conditions for sustained product velocity. That’s the level of clarity and execution I strive to foster across every product line.

Inspired by this post on Amplitude – Perspectives.

March 20, 2026
Unlock High-Impact Mobile Engagement: Amplitude Guides & Surveys for iOS, Android, React Native

Mobile engagement is most effective when it’s timely, contextual, and grounded in real user behavior. In my experience leading product teams, the fastest path to activation and retention comes from meeting users in the moment with relevant in-app guides and lightweight surveys that reduce friction and illuminate intent.

Deploy behavioral-driven mobile engagement with Amplitude Guides and Surveys for iOS, Android, and React Native platforms.

What excites me about this approach is how naturally it supports product-led growth. In-app guides and product tours streamline onboarding, while targeted micro-surveys surface the “why” behind user actions. The result: clearer journey mapping, fewer blind spots in the funnel, and a smoother path to user activation—all without adding engineering heavy-lift for each iteration.

To optimize continuously, I pair behavioral analytics with A/B testing and retention analysis. This lets my team validate hypotheses quickly, localize friction by segment or stage, and tune messaging for different cohorts. With Amplitude analytics at the core, we can connect engagement nudges to downstream outcomes, not just clicks—so we’re improving time-to-value, not just surface metrics.

My recommended starting point is simple: define a single activation moment, instrument the critical behaviors around it, and launch a focused guide plus one survey to test the narrative. Use journey mapping to identify the key decision points, then iterate weekly based on observed behavior, not opinions. This cadence keeps learning velocity high and ensures every change moves us closer to clear outcomes.

From a leadership perspective, I coach product trios to own an activation or retention KPI, run small controlled experiments, and document learning with crisp before/after evidence. Cross-platform support across iOS, Android, and React Native means we can scale wins quickly, standardize patterns, and create a repeatable playbook for new features and markets—all while keeping the user experience coherent and respectful.

Inspired by this post on Amplitude – Best Practices.

March 11, 2026
Mastering NRR: How Great Customer Success Teams Drive Expansion, Crush Churn, and Scale PLG

Net Recurring Revenue (NRR) is the cleanest truth-teller in my operating system. When I review NRR, I’m not just looking at whether we renewed accounts—I’m assessing whether our product and customer success motions are compounding revenue from our existing customers. Put simply: good CS teams protect revenue; great CS teams grow it through adoption, expansion, and durable retention.

Here’s how I frame NRR with my teams: it reflects revenue from our current customers after expansion, downgrades, and churn. If it’s at or above 100%, the installed base is self-sustaining; if it’s materially above 100%, the base is funding growth without net-new sales. That’s the holy grail for product-led growth and the benchmark I use to separate good from great.

At HighLevel, I’ve learned that you can’t “wish” your way to high NRR. You operationalize it. We align incentives, dashboards, and rituals so everyone—from PMs to CSMs to Solutions Engineering—owns the same outcome. Our “QBRs vs OKRs” discussions anchor on NRR drivers: activation rates, time-to-value, feature adoption depth, and expansion readiness. Those leading indicators tell me where we’ll land on lagging revenue results.

The best Customer Success teams operate like product teams. They use behavioral analytics and retention analysis to segment customers by use case and maturity, then design journey mapping to move each segment from first value to habitual value. They proactively reduce risk while creating clear expansion paths—new seats, premium features, or higher-tier plans—based on real product usage, not guesswork.

Onboarding is where great NRR trajectories begin. I focus on compressing time-to-first-value and time-to-second-value because those moments create the habit loops that underpin renewal and expansion. In practice, that means targeted in-app guides, contextual product tours, and nudges that drive user activation across the “sticky” features that correlate most with long-term retention.

To make this scalable, we blend human and product-led touchpoints. CSMs run outcome-based playbooks, while the product experience handles education and reinforcement at scale. When usage signals an expansion opportunity—say, a team consistently bumps into plan limits—we generate a product-qualified expansion lead and equip the CSM with the exact value storyline and proof points to close it.

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I’ve seen this playbook move the needle. After instrumenting our key workflows and deploying targeted in-app guidance, we watched adoption of our highest-retaining features climb, risk flags surface earlier, and expansion conversations become far more data-driven. We didn’t chase shiny objects; we built a reliable pipeline of retained and expanded revenue directly from product usage.

If you’re aiming to level up NRR, start with a crisp blueprint: define the critical events that predict renewal and expansion; set activation milestones per segment; deploy in-app guides and product tours to remove friction; give CSMs a single-pane view of risk and readiness; and review NRR weekly with the same seriousness you apply to new ARR. Consistency beats intensity here.

Finally, keep the narrative simple. Your leadership story isn’t “we shipped features,” it’s “we created customer outcomes.” Tie every CS and product initiative back to NRR drivers—and make the wins visible. When teams see the direct line from great onboarding and adoption to measurable expansion, they naturally operate like a unified, product-led growth engine.

NRR rewards rigor. Treat it as the top-line health metric for your installed base, make the software do more of the teaching, and empower CS to coach to outcomes. Do that well, and you won’t just separate the good from the great—you’ll build a compounding machine.

Inspired by this post on Pendo – Best Practices.

March 11, 2026