Tag: Pendo

Unlock Product Insights Fast: Connect MCP and Pendo to Claude, ChatGPT, and Cursor

I’ve spent the last year pushing our AI Strategy from slideware to shipped value, and one pattern keeps winning in real-world product teams: connecting agentic AI directly to trustworthy product analytics. That connection is where Model Context Protocol shines—safely bridging LLMs with the tools and data product managers rely on every day.
Model Context Protocol (MCP) gives AI agents access to your business data. Learn how MCP works, how product managers are using it, and how to connect Pendo’s MCP server to Claude, ChatGPT, or Cursor for instant product insights.
In practice, I treat MCP as a clean, auditable interface between LLMs and enterprise systems—decoupling the model choice from the data plane and enabling a retrieval-first pipeline with strong data governance. Because MCP standardizes the way agents discover resources and tools, it simplifies context window management, enforces least-privilege access, and makes it easier to evolve our stack without rewriting prompts or fragile glue code.
For product leaders, the immediate payoff is speed to insight. Instead of hopping across dashboards, I ask the agent questions in natural language—“Which onboarding step drives the biggest drop-off by segment?”—and get synthesized answers backed by traceable queries. That shift turns AI workflows into a daily habit, improving continuous discovery and accelerating product-led growth while maintaining privacy-by-design controls.
Under the hood, I think about MCP in four layers: resources (read-only data surfaces such as feature usage or retention cohorts), tools (safe operations like creating a note, exporting a segment, or proposing an in-app guide), prompts (task-scoped instructions tuned for LLMs for product managers), and observability (logs and evaluations). This structure keeps eval-driven development front and center and reduces operational risk.
Here’s how I connect Pendo analytics through MCP to my preferred assistants without compromising security or accuracy:
1) Prepare access: confirm your Pendo MCP server endpoint, authentication method, and scopes; apply least-privilege and redact any PII not required for analysis.
2) Register the server: in Claude, ChatGPT, or Cursor, add the MCP server with the provided URL and API key or token, then enable only the resources and tools your use case demands.
3) Validate the contract: prompt the agent to list available resources and describe tools; run harmless dry runs (e.g., “summarize top feature adoption trends last 30 days”) to confirm the interface behaves as expected.
4) Operationalize: standardize prompts for recurring analyses (QBRs vs OKRs, activation funnels, retention analysis), set guardrails, and log every interaction for audit. This is where prompt engineering meets governance.
5) Iterate with metrics: track answer quality, latency, and usage; expand scopes gradually and gate new tools behind human-in-the-loop until you reach reliable performance.
Once configured, I use the agent to surface weekly activation insights, identify outlier cohorts, and auto-draft product discovery notes with links back to Pendo reports. The result isn’t magic; it’s a disciplined AI product toolbox that brings the right context to the right question, fast.
If you’re starting from zero, pilot with one high-value question, one team, and one assistant. Keep the footprint small, measure outcomes, and then scale—with security, compliance, and stakeholder management baked in from day one. That’s how you turn MCP from an interesting protocol into a durable competitive advantage.

Inspired by this post on Pendo – Best Practices.

January 3, 2026
4 Proven Ways GTM Teams Accelerate Growth with Pendo’s HubSpot Integration

I’ve led GTM and product teams through countless tool integrations, and few have delivered compounding returns like connecting Pendo with HubSpot. See how customer behavioral data can help sales, marketing, customer success, and product teams create a better, more engaging customer experience. When we put product behavior where our revenue teams already live, the entire go-to-market engine becomes sharper, faster, and more customer-centric.

Here’s how I frame the value: the Pendo–HubSpot CRM integration unifies in-app product usage with contact and account context, so we can orchestrate lifecycle touchpoints across email, chat, and in-app guides while giving every function a single source of truth. The result is a product-led growth motion that aligns marketing, sales, customer success, and product around measurable activation, adoption, and expansion.

First, I help sales prioritize pipeline with usage-enriched lead and account scoring in HubSpot. Signals like feature adoption depth, weekly active users, trial milestones reached, and time-to-value tell AEs who is ready to buy and why. With real-time alerts and views, reps can tailor discovery, shorten sales cycles, and increase win rates—turning product interest into qualified demand.

Second, I accelerate onboarding and user activation by building HubSpot segments from Pendo cohorts and triggering coordinated journeys. New users receive the right lifecycle emails while in-app guides, product tours, and tooltips nudge them through key actions. This reduces time-to-value, increases early retention, and creates a smoother first-run experience.

Third, I protect and expand revenue with proactive customer success. Behavioral health scores and retention analysis spotlight accounts drifting from core workflows, prompting playbooks for outreach, training, or in-app interventions. Conversely, expansion signals—like adoption of premium features or growing seat usage—route to the right owner for timely upsell conversations.

Fourth, I close the loop for product decision-making. By syncing feedback, NPS, and usage cohorts with campaign and pipeline data in HubSpot, the team can measure how launches and in-app experiments influence engagement and revenue. This unified analytics platform approach keeps roadmaps tied to outcomes, not opinions, and helps us double down on the features that move the business.

To make this work, I start with a clear data contract and privacy-by-design guardrails: shared definitions for active users and adoption milestones, owner responsibilities for fields, and explicit consent handling. We then phase the rollout—beginning with one or two high-impact plays—instrument the baseline, and iterate using go-to-market strategy reviews to verify causal impact.

If your GTM teams are leaning into product-led growth, the Pendo–HubSpot integration is a force multiplier. Aligning lifecycle messaging, sales prioritization, and customer success around real behavioral data creates compounding advantages—more relevant outreach, faster activation, higher retention, and cleaner expansion.

Inspired by this post on Pendo – Best Practices.

January 3, 2026
How We Scale Revenue with Pendo Predict: My Playbook to Cut Costs and Reduce Risk

When revenue expansion, cost efficiency, and product risk mitigation all matter at once, I turn to Pendo Predict. In my role leading product management, I’ve seen how predictive insights can supercharge product-led growth by aligning onboarding, user activation, and in-app experience design with the outcomes our customers value most.

“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 why I integrate Pendo Predict at the heart of our product strategy.

Here’s how I operationalize it: I start by mapping our value proposition to clear activation milestones, then use Predict to surface segments that are most likely to convert, expand, or churn. With those signals, we personalize in-app guides and product tours to address friction in real time, accelerating user activation and streamlining onboarding without adding headcount.

To scale revenue, I connect Predict’s likelihood scores to our product strategy rituals: prioritizing roadmap bets that increase adoption, sequencing releases where the impact will be highest, and instrumenting retention analysis to verify lift. This turns our product into a self-reinforcing growth engine—nudges, guides, and contextual help show up exactly when users need them, driving deeper engagement and upsell readiness.

Cost reduction follows naturally. By meeting users inside the product with targeted in-app guides, we deflect support tickets, shorten time-to-value, and reduce the volume of one-off interventions. We also improve platform scalability by focusing engineering effort on the experiences Predict flags as the biggest levers, not just the loudest requests.

Risk is where Predict becomes a strategic safety net. Instead of betting the quarter on intuition, we run controlled changes, use A/B testing for in-app messaging, and monitor predicted outcomes before rolling out broadly. This de-risks roadmap decisions while preserving velocity—critical for a product team operating at scale.

Practically, my playbook is simple: (1) define activation and retention events tied to our value proposition, (2) use Pendo Predict to identify high-impact segments, (3) deploy tailored product tours and in-app guides to close the gap, (4) validate impact with retention analysis and iterate. Repeat this loop and adoption compounds, creating reliable, product-led growth.

If your team is aiming to raise the ceiling on adoption and engagement while controlling spend, Pendo Predict gives you the visibility and control to do both. For us, it’s the connective tissue between strategy and execution—the data-driven way to deliver the right experience at the right time, and to do it consistently at scale.

Inspired by this post on Pendo – Best Practices.

January 3, 2026
Why I’m All-In on INDUSTRY 2025: 5 Powerful Reasons For Product Leaders at The Product Conference

INDUSTRY 2025: The Product Conference is circled on my calendar for good reason. In my role leading product management at HighLevel, I look for events that sharpen strategy, accelerate learning, and connect me with operators who ship. This one consistently delivers on all three, and 2025 promises to raise the bar for product management leadership.

Join Pendo at INDUSTRY in Cleveland, Ohio.

First, I expect deeply actionable product strategy insights—beyond platitudes. I’m prioritizing conversations on outcomes vs output OKRs, product roadmapping and sprint planning, and how great teams articulate a crisp value proposition while maintaining points of parity that matter. I’m going in with specific questions on product-market fit lessons and how to systematize strategic bets without stifling discovery.

Second, the surge of AI in product work is too important to observe from the sidelines. I’m comparing approaches across AI Strategy, LLMs for product managers, prompt engineering, and eval-driven development—especially in retrieval-first pipeline patterns. My focus: where AI genuinely improves product discovery, in-app guides, and customer support ai strategy, and where it risks adding complexity without outcomes.

Third, the community is unmatched for conference networking and pragmatic learning. I’m intentional about meeting product trios who run continuous discovery at scale, as well as leaders who’ve cracked stakeholder management under pressure. These are the moments where competitive differentiation is born—through candid stories of what didn’t work and why.

Fourth, I’m eager to stress-test data practices that power product-led growth. I’ll be exchanging notes on retention analysis, unified analytics platform decisions, user activation, and how teams integrate qualitative feedback with event data to inform roadmaps. I’m also interested in how practitioners leverage platforms like Pendo, Amplitude analytics, Intercom, and HubSpot to reduce time-to-insight and craft effective product tours and in-app guides.

Fifth, I treat INDUSTRY as a checkpoint for leadership growth. I’m looking for fresh takes on empowering product teams, first principles decision making, organizational development, and the IC to manager transition. The best sessions don’t just inspire; they give me two moves I can apply with my team on Monday.

To make the most of the week, I’m applying a continuous discovery mindset: arrive with clear learning goals, capture portable frameworks, and translate at least two insights into experiments before wheels-up. If you’re focused on product strategy, product discovery, and product-led growth, we’ll have plenty to compare and build on together.

I’ll be in Cleveland ready to learn, share, and connect with peers who care about craft and outcomes. If you’re attending, let’s compare notes on what’s working, what’s stalled, and how we can raise the bar for product management leadership in 2025 and beyond.

Inspired by this post on Pendo – Perspectives.

January 3, 2026
Implementing Agentforce the Right Way: A Practical Playbook with Pendo and Salesforce

I think about Agentforce implementation the same way I think about any high-stakes product launch: start with outcomes, instrument relentlessly, and iterate in tight loops. When agentic AI touches core workflows in Salesforce, the winners are the teams that combine rigorous product strategy with thoughtful CRM integration and product-led growth tactics.

Learn the ways in which Pendo helps companies design and iterate on their agentic strategy for Salesforce.

My working playbook begins with clarity. Before a single agent is deployed, I align with stakeholders on the highest-value “jobs” inside Salesforce—reducing case handle time in Service Cloud, accelerating lead qualification in Sales Cloud, or improving data hygiene for revenue operations. That alignment shapes our agentic AI approach and prevents us from shipping clever agents that don’t move the metric that matters.

From there, I treat telemetry as a first-class requirement. I instrument the end-to-end journey with Pendo so we can observe when an agent triggers, when it falls back, when it hands off to a human, and how those moments affect conversion, CSAT, and cycle time. I refer to this observability layer as Agent Analytics, and it’s the backbone of evidence-based iteration.

Guidance is equally critical. I use Pendo’s in-app guides to onboard admins and frontline users directly inside Salesforce, deliver contextual tooltips that explain what the agent will do next, and collect feedback within the flow of work. That combination shortens time-to-value and builds trust, which is essential for customer support ai strategy and change management.

Iteration is where the compounding returns show up. I run A/B testing on prompts, decision policies, and handoff rules; evaluate performance on real user cohorts; and promote what works. This is classic product-led growth applied to agentic AI—ship small, measure precisely, and scale winners. Prompt engineering is not a one-time task; it’s a continuous discovery loop.

I also weave in governance from day one. Privacy-by-design, data governance, and AI risk management aren’t add-ons—they are design constraints that shape what the agent is allowed to see and do. The guardrails live alongside the experience: clear disclosures, reversible actions, and easy ways for users to override or escalate.

Finally, I operationalize the learning loop. Weekly reviews with a product trio (PM, design, engineering) examine Pendo dashboards, qualitative feedback, and Salesforce outcomes. If an agent is underperforming, we adjust prompts, refine retrieval, or simplify the decision tree. If it’s exceeding targets, we expand the use case and systematize the pattern.

When teams ask me for the “right way” to implement Agentforce, my answer is simple: treat your agent like a product. Measure with Pendo, guide inside Salesforce, and iterate until the business outcome moves. That’s how we turn promising agents into durable advantages.

Inspired by this post on Pendo – Perspectives.

January 3, 2026
How I Used Pendo In-App Guides to Ignite Our Summer Release Adoption, Engagement, and ROI

Launching a major release is only half the battle; earning adoption inside the product is where the real wins happen. For our Summer Release, I made a deliberate choice to promote new capabilities where customers experience value—in the app—by leaning on Pendo’s in-app guides, product tours, and tooltip design. This product-led growth approach let us deliver timely, contextual education without disrupting a user’s flow, aligning our go-to-market strategy with how people actually work.

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.

I began by segmenting audiences around key jobs-to-be-done and lifecycle stages—onboarding users, power users, and specific roles—so every prompt supported a clear value proposition. We mapped the journey for each segment and placed concise guides at decision points where users naturally discover adjacent features. The goal was simple: accelerate user activation, reduce time-to-value, and make the Summer Release feel intuitive, not intrusive.

Execution hinged on progressive disclosure. Short, focused product tours introduced what changed and why it mattered, while tooltips offered deeper context when users hovered or asked for help. We paired this with behavioral targeting so guides appeared only after relevant triggers—usage patterns, page views, or completion of prerequisite steps—keeping the experience helpful and respectful.

We ran A/B testing on headlines, CTAs, and guide placement to refine messaging and reduce friction. Variants explored different tones (instructional vs. benefit-led), lengths (microguide vs. multistep tour), and formats (banner, modal, tooltip). The winning patterns emphasized outcome-first language, clear next steps, and optional deep dives for advanced users.

Measurement focused on adoption and engagement: guide view-to-click rates, feature usage uplift post-guide exposure, and downstream behaviors tied to retention analysis. While we avoided vanity metrics, we did look for sustained usage over time, not just one-time clicks. The early signals were encouraging—faster discovery of new capabilities, higher completion of key workflows, and more consistent engagement across targeted cohorts.

Cross-functionally, we aligned in-app messaging with our broader go-to-market strategy, ensuring consistency across help center content, enablement, and customer communications. This cohesion strengthened competitive differentiation and reinforced our product strategy: deliver value in context, then invite users to explore more when they are ready.

The biggest lesson? Thoughtful in-app guides and product tours are not about broadcasting release notes—they are about orchestrating moments of clarity that compound into adoption. By combining precise segmentation, disciplined experimentation, and clear success criteria, we turned a launch into sustained product-led growth. Next, we’re extending this playbook to onboarding and lifecycle milestones to keep momentum strong across releases.

Inspired by this post on Pendo – Perspectives.

January 3, 2026
Healthcare Product Benchmarks That Matter: Actionable Metrics and Playbooks From Our Report

I rely on product benchmarks to align teams, sharpen strategy, and accelerate outcomes—especially in healthcare, where stakes are high and complexity is real. Over the years, I’ve learned that the right metrics create clarity across product, engineering, compliance, and go-to-market, enabling faster, safer decisions that translate into measurable impact.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the healthcare technology industry’s performance across key product metrics.

When I evaluate a healthcare product’s health, I focus on a few essentials: activation rate and time-to-value for new users, weekly active usage and feature adoption for clinicians and admins, and cohort-based retention analysis to understand whether value compounds over time. I also look at funnel friction (onboarding drop-off, failed setup steps), support load per account, and reliability signals that influence trust—because in healthcare, trust fuels growth.

Benchmarks turn those metrics into context. They help me answer, “Are we good, or just lucky?” By comparing our numbers to industry peers, I can prioritize the few bets that matter, set outcomes vs output OKRs, and guide empowered product teams to focus on the highest-leverage improvements.

Operationally, I instrument products with a unified analytics platform and tools like Amplitude analytics and Pendo to track user activation, feature adoption, and in-product journeys. Pairing that with continuous discovery keeps insights fresh, while A/B testing and clear minimum detectable effect (MDE) thresholds ensure we ship with statistical confidence.

In practice, my playbook for healthcare product-led growth is straightforward: simplify onboarding with targeted product tours and in-app guides, tighten the first-win loop to reduce time-to-value, and eliminate blockers surfaced by behavioral analytics. Then, reinforce the loop with lifecycle messaging, role-specific education, and clear value propositions for clinicians, operations teams, and executives.

Of course, none of this works without strong governance. Data governance and regulatory compliance aren’t just guardrails; they’re growth enablers. Clear audit trails, privacy-by-design, and reliable incident management build the trust that keeps adoption high and churn low.

If you’re ready to benchmark your roadmap against the market, this report gives you the clarity to spot gaps, the language to align stakeholders, and the metrics to execute with precision. Use it to calibrate your product strategy, guide your next set of experiments, and confidently scale what works across the healthcare technology ecosystem.

Inspired by this post on Amplitude – Perspectives.

December 29, 2025
Game-Changing Product Benchmarks Every Media & Entertainment Leader Must Know

Benchmarks are my reality check. In the fast-moving media and entertainment space, I rely on concrete product metrics to align strategy, prioritize roadmaps, and drive product-led growth with confidence. When my team and I calibrate against industry benchmarks, we turn opinions into outcomes and ensure our bets are tied to measurable impact.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the media and entertainment industry’s performance across key product metrics.

Here’s how I think about what matters most in this report: user activation and time-to-value to understand onboarding effectiveness, retention analysis to quantify staying power, feature adoption to validate value delivery, and engagement depth to see whether we’re building habit loops—not just generating clicks. I also look at experimentation maturity (A/B testing volume and velocity), release cadence, and how we structure outcomes vs output OKRs to keep teams accountable to real customer impact.

Benchmarks aren’t scorecards—they’re decision accelerators. I use them to run a gap analysis, set clear targets, and focus the roadmap on the few bets most likely to move our leading indicators. For example, if activation lags, we invest in clearer in-app guides, product tours, and progressive onboarding; if retention stalls, we refine the value proposition and instrument cohorts to isolate which segments respond best.

Operationally, I instrument a unified analytics platform with Amplitude analytics for cohorting and funnel analysis, and Pendo for in-app guidance and feature adoption insight. Weekly product health reviews keep the team oriented around activation, retention, and engagement. When we A/B test, we set a minimum detectable effect (MDE) up front and tie experiments to specific OKRs, so decisions aren’t swayed by noise. This discipline helps empowered product teams ship faster without sacrificing rigor.

If you’re building in media and entertainment, use these benchmarks to define what “good” looks like for your model, then localize targets to your audience and content format. Start by instrumenting the essentials, align leaders on the few metrics that matter, and iterate with high-velocity experiments. The right benchmarks will sharpen your product strategy, improve stakeholder confidence, and turn your roadmap into a reliable engine for growth.

Inspired by this post on Amplitude – Perspectives.

December 23, 2025
Stop Asking, Start Listening: Turn VOC Into Measurable Behavior, Retention, and Revenue

I’ve learned that the fastest path from feedback to impact is not to ask more questions—it’s to listen more closely to what users already tell us with their clicks, scrolls, and pauses. Surveys and interviews give us color, but behavioral analytics reveal truth. When I connect voice of the customer (VOC) to real user behavior, I can prioritize with confidence and ship changes that improve activation, retention, and revenue.

Discover how to connect voice of the customer (VOC) feedback to user behavior and turn opinions into action.

Here’s the mindset shift that changed my team’s outcomes: opinions are hypotheses, behavior is evidence. I blend qualitative VOC with quantitative product analytics so our roadmap aligns to outcomes vs output OKRs. The result is a tighter feedback loop, fewer bets based on anecdotes, and more decisions grounded in measurable user value.

First, I instrument the product so it can “talk back.” That means a clean event taxonomy for key moments like time-to-first-value, onboarding completion, feature adoption, and conversion health. Tools such as Amplitude analytics, Pendo, and a unified analytics platform help me track funnels, cohorts, and retention analysis with consistent definitions across teams.

Next, I normalize the messy reality of VOC. Support tickets, sales notes, app reviews, in-app guide responses, product tour feedback—everything gets tagged into themes such as onboarding confusion, performance slowness, permissions friction, or pricing clarity. This shared language lets me map qualitative signals to behavioral segments without losing nuance.

Then I join feedback to behavior. For any theme, I create a cohort of users who expressed it and compare their funnel completion, activation rate, and retention curves to a control group. If customers say a flow is “too complex,” I look for excessive time-on-step, back-and-forth navigation, tooltip dependence, or drop-offs at a specific screen. Cohort and funnel analysis make the problem visible and quantifiable.

Prioritization becomes straightforward once the impact is measurable. I size the opportunity by the delta in activation, conversion, or retention and estimate the lift from fixing the root cause. This moves us from feature wish lists to product-led growth bets with clear business cases and confidence intervals.

When it’s time to ship, I close the loop with disciplined experimentation. I use A/B testing with a clear minimum detectable effect (MDE), guide users through changes with in-app guides and product tours, and monitor behavior shifts in near real time. Success means behavior moves in the direction the VOC suggested—fewer drop-offs, faster task completion, and improved activation and retention.

A recent example: we kept hearing about “slow” reporting. Instead of debating, we correlated the feedback with sessions showing long load times and repeat clicks on filters. By simplifying defaults, prefetching key queries, and clarifying loading states, we cut perceived wait time by 42% and improved day-7 retention for affected cohorts. VOC identified the friction; behavior showed us exactly where to fix it.

This practice thrives with a simple cadence: weekly listening reviews with product trios to spot themes, monthly synthesis across VOC and usage, and dashboards that pair sentiment with behavior. Over time, the organization shifts from reactive requests to continuous discovery, where each insight is traced to a measurable change in user behavior.

If you want a roadmap that sells itself, start by letting the product speak. Connect your VOC themes to behavioral analytics, quantify the gaps, and ship targeted improvements that users can feel—and you can measure.

Inspired by this post on Amplitude – Perspectives.

December 19, 2025
Must‑Know Product Benchmarks for Financial Services: Actionable Insights to Accelerate Growth

I’ve learned that in financial services, intuition isn’t enough—rigorous product benchmarks are what separate signal from noise. When my team and I evaluate portfolio performance, we anchor our decisions to the metrics that correlate with customer trust, compliant growth, and durable revenue.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the financial services industry’s performance across key product metrics.

Here’s how I use a benchmark report in practice: I calibrate our baseline against peers, identify the few levers that disproportionately drive outcomes, translate those findings into outcomes vs output OKRs, and align stakeholders across product, risk, operations, and go-to-market. Benchmarks turn debate into data and surface the opportunity cost of not fixing broken journeys.

The product metrics I zero in on typically include user activation rate, time-to-first-value, onboarding completion, funnel conversion (for example, from signup to funded account or application to approval), cohort-based retention analysis (D7/D30/D90), depth of feature adoption, weekly-to-monthly active ratios, support contact rate, and cost-to-serve. In financial services, these signals tell a clear story about trust, reliability, and product-market fit.

To operationalize these insights, I combine Amplitude analytics with Pendo in-app guides to instrument end-to-end journeys, segment by customer profile, and run disciplined A/B testing with clear guardrails. This lets us move from anecdotes to statistically defensible changes and iterate confidently on onboarding, product tours, and moments that drive activation and engagement.

Because the trust and regulatory bar is higher in financial services, I also watch for friction in verification flows, error states that erode confidence, and any gaps between intent and completion. When benchmarks show we’re lagging, I pair discovery with rapid experiments to improve the experience while maintaining privacy-by-design and strong governance.

Use this benchmark report to pinpoint where you outperform and where you lag, prioritize roadmap bets, and focus your product-led growth motion. When teams rally around a shared set of product benchmarks, execution speeds up, trade-offs become clearer, and the value proposition sharpens for both customers and the business.

Inspired by this post on Amplitude – Perspectives.

December 16, 2025
Unlock B2B Product Excellence: Essential Benchmarks to Outperform Your Tech Peers

I rely on disciplined product benchmarks to turn strategic intent into measurable progress. In B2B technology, benchmarks give me and my team the clarity to prioritize what truly matters, align executives around shared outcomes, and course-correct before small gaps become growth-stalling problems.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the B2B technology industry’s performance across key product metrics.

When I assess product health across a portfolio, I start with a core set of product benchmarks: activation rate, onboarding completion, time-to-first-value, weekly and monthly active accounts, feature adoption, cohort-based retention, expansion and contraction revenue, and support deflection. Together, these metrics show where the product creates value, where users get stuck, and which levers most efficiently drive product-led growth.

Benchmarks are only powerful if they inspire action. I instrument reliable analytics (Amplitude analytics) to capture consistent event data, pair that with in-app guides and product tours (Pendo, Intercom) to test friction hypotheses, and run A/B testing to validate changes with statistical rigor. From there, I translate insights into outcomes-based OKRs, so roadmapping and sprint planning focus on the few bets most likely to move our key product metrics.

I’ve seen this approach pay off repeatedly. When peer benchmarks revealed our user activation lagged, we simplified onboarding, clarified value propositions with sharper UX writing, and launched targeted in-app nudges. We didn’t just ship features—we improved the experience against a clear yardstick, and the subsequent lift in activation and early retention validated the strategy.

Cross-functional alignment is critical. I partner with customer success to interpret retention analysis by segment, with marketing to ensure messaging supports time-to-value, and with engineering to keep quality and reliability high. While product metrics lead, I also keep an eye on complementary signals like incident management and DORA metrics to ensure we’re not trading speed for stability.

If you’re leading a product organization, use benchmarks to calibrate ambition and create focus. Start by identifying the one or two metrics most predictive of long-term retention, set peer-informed targets, and iterate with continuous discovery. The result is a product strategy that is evidence-based, resilient to opinion cycles, and capable of compounding gains over time.

Ultimately, benchmarks aren’t about vanity; they are about velocity. With a shared view of where you stand against the B2B technology industry, you can make sharper bets, accelerate product-market fit, and turn your roadmap into a reliable engine for growth and customer value.

Inspired by this post on Amplitude – Perspectives.

December 11, 2025
Product Analytics for Everyone: Master Funnels, Retention, and Conversion to Drive Growth

Product analytics isn’t a specialist’s sport—it’s a team capability. In my role leading product teams, I’ve seen designers, engineers, marketers, and customer success partners uncover insights that shape strategy, accelerate product-led growth, and improve outcomes for customers. When we demystify the basics and bring analytics into everyday decisions, we build truly empowered product teams.

Here’s the core promise of this approach: "Learn the product analytics fundamentals of funnels, retention, and conversion drivers so that anyone can confidently answer key product questions." That line has guided how I teach product managers to think—start with the essentials, tie them to real customer behaviors, and make the work repeatable across the organization.

I start with funnels because they tell a story—the journey from discovery to value. A simple example: track the path from sign-up to user activation to the first value event. This reveals where onboarding succeeds or stalls, what friction blocks adoption, and which moments are ripe for optimization. With tools like Amplitude analytics or Pendo, we can break down conversions by segment, channel, or feature usage to isolate where improvements matter most.

Next comes retention analysis, the clearest signal that we’re building something customers choose to return to. Cohort analysis shows who comes back and when; retention curves show where value compels a second, third, and tenth use. Tie retention to activation milestones and the outcomes customers achieve—not just logins—and you’ll quickly spot whether your product discovery assumptions hold up in the wild. A unified analytics platform makes these insights discoverable and repeatable across teams.

Conversion drivers round out the picture. Once the funnel is clear and retention is stable, I look for the behaviors and experiences that predict success: feature combinations, time-to-value, message timing, or supportive content. Whether in Amplitude analytics or Pendo, correlating these drivers with outcomes lets us prioritize roadmaps with confidence. Pair this with continuous discovery—qualitative interviews, in-product feedback, and rapid experiments—and you’ll move from interesting data to decisive actions.

This is how we build empowered product teams: by making analytics a daily habit rather than a quarterly report. We bring insights into roadmap reviews, design critiques, and sprint planning; we celebrate learning from experiments as much as shipping features; and we hold ourselves accountable to customer outcomes, not just output. When everyone can interpret funnels, discuss retention, and isolate conversion drivers, we make smarter bets faster.

If you’re getting started, keep it simple. Define a clear activation metric, instrument the top of your funnel, and track a small number of cohorts. Share a weekly readout with highlights, surprises, and questions to investigate. Over time, stitch insights into narratives that drive product-led growth—and, most importantly, help customers achieve what they came for.

Product analytics isn’t just for analysts. It’s a shared language for product discovery, onboarding excellence, user activation, and long-term retention. When we practice it together, we build better products and stronger teams.

Inspired by this post on Amplitude – Best Practices.

December 5, 2025