Every day, my team and I practice a simple but powerful idea: build with the same data-driven rigor we expect our customers to use. That’s why we run "Amplitude on Amplitude"—using the platform to continuously discover opportunities, validate bets, and ship experiences that matter.
Learn how Amplitude uses its own platform to build experiences customers love. We use Amplitude to understand our customers, test ideas, act on insights, and drive growth.
In practice, this means treating Amplitude analytics as our unified analytics platform for the entire product lifecycle. We instrument key events, build behavioral cohorts, and tie those insights back to product strategy so our product discovery work focuses on the highest-impact problems. This continuous discovery loop keeps us close to real user behavior instead of assumptions.
When we have a hypothesis, we pressure-test it with A/B testing. Before we launch, we size the minimum detectable effect (MDE), align on success metrics, and ensure we’re powered to make a decision. Experiments aren’t just about lift—they’re about learning with speed and confidence so we can iterate without second-guessing.
Insights only create value when they drive action. We translate findings into in-app guides and product tours to nudge the next best action and accelerate user activation. Then we follow through with retention analysis to understand which features create durable engagement and where friction persists. This closed-loop approach helps us turn insight into designed outcomes.
The result is a product-led growth engine that compounds. By grounding our roadmap in evidence, we reduce risk, move faster, and deliver experiences customers love. More importantly, we create a shared language across product, design, engineering, and go-to-market teams so decisions are transparent, measurable, and aligned to customer value.
If you’re aiming to raise the bar on product management rigor, the "Amplitude on Amplitude" approach is a repeatable system: unify your data, run disciplined experiments, operationalize insights in-product, and measure long-term impact on activation and retention. That’s how we build with clarity—and win with our customers.
Inspired by this post on Amplitude – Best Practices.
In my role leading product management at HighLevel, I’ve learned that enterprise go-to-market lives or dies by the strength of the partnership between product and product marketing. When we operate as one team, we turn complex capabilities into clear outcomes that resonate with buyers and drive adoption at scale.
I’m especially energized by the archetype of a product marketing manager at a leading analytics platform—someone “focusing on go-to-market solutions for enterprise customers.” That mandate requires rigor across product positioning, value proposition design, competitive differentiation, and sales enablement, all while aligning deeply with engineering and customer success. In practice, it means translating signal from a unified analytics platform into narratives and plays that close deals and expand accounts.
Day-to-day, I partner with product marketing to validate messaging through continuous discovery and data. We use Amplitude analytics to instrument activation, engagement, and retention analysis—then feed those insights into product-led growth motions like in-app guides and product tours. A/B testing grounded in a clear minimum detectable effect (MDE) helps us separate noise from impact, while points of parity and true differentiation shape the story sellers can confidently carry into enterprise conversations.
This is also where outcomes vs output OKRs keep us honest. Rather than celebrating launches, we anchor on measurable behavior change: faster time-to-value, higher user activation, deeper feature adoption, and multi-threaded stakeholder engagement. Product trios provide the operating rhythm, and stakeholder management ensures sales, marketing, and success move in lockstep with the roadmap and GTM calendar.
If you’re building an enterprise GTM motion, start by tightening your value proposition to the top three pains your best-fit accounts actually feel, validate with real usage data, and then enable your field teams with crisp, data-backed talk tracks. With the right PM–PMM alignment and analytics foundation, your go-to-market strategy becomes a compounding advantage—not just a launch plan.
Inspired by this post on Amplitude – Perspectives.
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.
I’ve been systematically exploring how the product model shows up inside iconic companies. After studying “The Product Model at Spotify” and “The Product Model at Amazon,” I’m turning my lens to Google—specifically, how the product operating model, product culture, and product strategy manifest in practice and what we can pragmatically take back to our own organizations.
When I talk about the product model, I’m looking at the machinery that connects strategy to outcomes: empowered product teams, clear decision rights, tight product trios, continuous discovery, data-informed bets, and an operating cadence that enables learning at speed. My goal here is to unpack how those elements come together at Google and translate them into repeatable patterns you can adopt.
At a high level, I focus on how teams are empowered to solve problems rather than ship outputs, how outcomes vs output OKRs clarify what matters, and how experimentation (from rapid prototyping to A/B testing) de-risks decisions before they scale. I also examine how engineering and product partner to balance platform scalability with customer value, and how stakeholder management reinforces alignment without slowing teams down.
Why does this matter? Because the product model is a lever for resilience and speed. When product strategy is explicit and the operating model is built for learning, organizations multiply the impact of talented people. That’s how small, focused teams repeatedly deliver outsized results—even in complex, regulated, or high-scale environments like Google.
In the sections that follow, I’ll synthesize what I see as the core patterns behind Google’s approach and distill them into actionable guidance: how to structure product trios, how to run continuous discovery alongside delivery, how to set and calibrate OKRs for outcomes, and how to evolve your product culture so empowered product teams can do their best work. My aim is not to idolize a model, but to extract what’s portable and help you adapt it to your context.
From a product leadership vantage point, I’ve learned that the fastest path to trustworthy insights and product-led growth runs through the SDKs we put in developers’ hands. When the instrumentation layer is frictionless, data quality improves, teams move faster, and customer value compounds—especially when you’re building on Amplitude analytics.
I collaborate closely with a Senior Software Engineer on the Developer Experience team, specializing in development of Amplitude's Browser SDK. That partnership has reinforced a simple truth: an exceptional developer experience is a growth lever. Streamlined APIs, clear conventions, and resilient client-side telemetry reduce setup time, eliminate common integration errors, and unlock cleaner event streams for retention analysis and user activation.
On the technical front, our shared priorities center on performance, reliability, and privacy-by-design. We optimize for minimal bundle size and zero-regret API ergonomics, while ensuring robust offline queuing, retry logic, and graceful degradation to protect Web Vitals in real-world conditions. CI/CD guardrails, automated schema checks, and backward-compatible versioning keep event contracts stable and predictable as products evolve.
Data governance is a first-class requirement. Consent-aware collection, PII redaction at the edge, and clear controls for regional data routing align implementation with organizational risk tolerances. When teams trust the pipeline, they are more willing to broaden coverage, accelerate experimentation, and make faster, higher-confidence decisions.
The business impact is immediate. Cleaner event taxonomies drive sharper funnel views, enabling tighter A/B testing loops and faster identification of activation drop-offs. With dependable data, product trios can iterate toward the right experience, boosting activation rates, compressing time-to-value, and supporting durable retention analysis without chasing analytics debt.
Great SDKs also multiply the reach of developer evangelism. Strong documentation, copy-pasteable patterns, and pragmatic examples reduce onboarding friction and promote consistent instrumentation across squads. That consistency scales platform scalability, cuts incident noise, and supports reliable DORA metrics—so teams ship frequently without sacrificing quality.
My takeaway is simple: treat Amplitude's Browser SDK as a product surface, not just a technical dependency. Invest in the Developer Experience team, and you’ll find that every improvement pays dividends across experimentation velocity, data trust, and ultimately, product-led growth. When the foundation is solid, everything built on top gets better—faster.
Inspired by this post on Amplitude – Best Practices.
I obsess over the moments that make or break user trust: how fast a page paints, how responsive it feels, and how stable it stays as content loads. Web Vitals are the clearest lens I have to connect those micro-moments to macro outcomes—activation, conversion, retention, and, yes, SEO ranking. Bringing those signals into Amplitude lets me translate web performance into product decisions that move the business.
Now in Amplitude, improve your website user experience and SEO ranking by measuring and taking action on your Web Vitals.
In practice, I focus on the Core Web Vitals—Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS)—and instrument them as event properties so I can segment by page type, device, geography, traffic source, and user cohort. That gives me a single source of truth that aligns engineering performance work with product metrics like activation and revenue, all inside a unified analytics platform.
My workflow is straightforward: I instrument Web Vitals in the client (sampling if needed), stream them into Amplitude, and build dashboards that pair performance distributions with key funnels. I look for thresholds—where a user’s LCP or INP crosses a boundary and their likelihood to convert or retain drops. When I see those cliffs, I know exactly which pages or audiences to target and which improvements unlock the most value.
From there, I run experiments. A/B testing on navigation layout, image optimization, or lazy-loading strategies helps me validate that a performance lift also drives a statistically significant improvement in conversion or retention. Because the analysis lives in Amplitude, I can quickly cohort users by performance experience (for example, “green” vs “yellow” LCP) and quantify how much better experiences translate into business outcomes—reducing the risk of shipping changes that only move a synthetic score without helping users.
SEO benefits are a welcome compounding effect. When I push more sessions into the “good” Web Vitals range, I typically see lower bounce rates, stronger session depth, and better engagement—signals that support search performance. I treat rankings as an outcome of great user experience rather than the goal itself; by improving real-user metrics, I earn durable gains that don’t evaporate with the next algorithm change.
Operationalizing this is crucial. I define product-level service objectives for LCP, INP, and CLS by key page groups, review them in QBRs alongside activation and retention, and set guardrails so performance never regresses during feature velocity. This turns performance into a habit for empowered product teams rather than a one-off initiative.
If you’re starting fresh, begin with a narrow slice: instrument Web Vitals on your top three entry pages, visualize their distributions in Amplitude, and overlay conversion and retention. Within a week, you’ll see where experience degrades for specific cohorts and have a prioritized, testable roadmap for improvement. The fastest path to better UX and growth is making performance visible where you already make product decisions—and that’s exactly what this workflow delivers.
Inspired by this post on Amplitude – Best Practices.
Trust is the true currency of diagnostic analytics. If customers can’t verify why a system reached a conclusion—or how confident it is—adoption stalls. That’s why this line resonated so strongly with my own playbook: Amplitude used confidence levels, citations, and evals to build a diagnostic AI tool accurate enough to earn customer trust.
Confidence levels are my first non-negotiable. When a model flags a root cause or prescribes a next step, I want the UI to state its certainty upfront and in plain language—ideally with calibrated ranges and a brief rationale. This simple pattern sets the right expectations, reduces over-trust, and supports AI risk management by making uncertainty visible. In practice, we pair this with clear UX writing so users understand what “High,” “Medium,” or “Low” confidence really means in their workflow.
Citations are the second pillar. Every diagnostic needs a breadcrumb trail back to source data: which metrics were analyzed, what time window was used, and how the insight was derived. Linking directly to the underlying chart, query, or dashboard reinforces data governance and shortens the path from “interesting” to “actionable.” When customers can click through to verify the evidence, they gain the confidence to make decisions—fast.
Evals complete the trio. Before and after launch, I hold the team to eval-driven development: offline benchmarks, targeted scenario tests, and live performance monitoring that mirrors real customer use. We define success criteria for precision/recall, false-positive thresholds, and latency, then wire those checks into CI/CD so regressions are caught early. Continuous evals aren’t just QA; they’re the heartbeat of an AI workflow that keeps insights reliable at scale.
Operationally, these practices compound. Confidence levels help prioritize follow-up analysis, citations accelerate collaboration across product and data teams, and evals keep quality high even as models, data, and usage evolve. Together, they form a pragmatic AI strategy that aligns product discovery with measurable outcomes and safeguards customer trust where it matters most—inside daily decisions.
If you’re building a diagnostic AI tool, start with these three building blocks and resist the urge to hide uncertainty. Make it legible. Make it verifiable. And measure it continuously. That’s how we turn powerful models into trustworthy products customers depend on.
Inspired by this post on Amplitude – Perspectives.
Every week, retail and ecommerce leaders ask me the same thing: which product metrics truly separate the winners from the rest? As a VP of Product Management at HighLevel, Inc., I rely on benchmarks to translate strategy into measurable, repeatable outcomes—so I built a simple way to use them to guide roadmaps, experiments, and executive alignment.
Discover exclusive data and strategies from our Product Benchmark Report. Compare the ecommerce industry’s performance across key product metrics.
Benchmarks aren’t just numbers on a chart; they’re context. They help me calibrate goals, set outcomes vs output OKRs, and focus our product-led growth efforts on the handful of inputs that actually move revenue, loyalty, and lifetime value in retail and ecommerce.
The metrics I prioritize map to the customer journey: acquisition efficiency (visit-to-signup), activation and time-to-first-value, product-to-checkout conversion, order completion rate, repeat purchase and subscription retention, average order value, and LTV/CAC. I also track friction signals like cart abandonment, returns, and refund rates to surface hidden points of failure.
Here’s how I use the report in practice. First, baseline performance against peer benchmarks so we know whether we have a strategy or an execution gap. Second, segment by cohort (new vs. returning, mobile vs. desktop, subscription vs. one-time) to reveal where the experience is underperforming. Third, instrument clean funnels and events in our unified analytics platform—Amplitude analytics or Pendo—so every metric is observable and trustworthy.
From there, I translate gaps into a focused experimentation plan. We run A/B testing with proper guardrails, size tests using minimum detectable effect (MDE), and predefine success metrics to avoid p-hacking. Each experiment ties directly to an outcome metric, not an output, so we can attribute impact and iterate with confidence.
Strong execution requires strong alignment. I bring product, marketing, and CX together as a product trio to turn benchmark deltas into a crisp value proposition, targeted onboarding, and lifecycle messaging. That cross-functional focus turns insights into conversion, retention, and customer lifetime value—fast.
Data integrity underpins all of this. We establish clear event taxonomies, privacy-by-design practices, and governance to keep analytics reliable at scale. When the data is clean, decisions get faster, and experimentation becomes a compounding advantage.
If you’re ready to pressure-test your roadmap and accelerate growth, start with the benchmarks. Use them to prioritize opportunities, prove impact with disciplined experiments, and communicate strategy in language the business understands. That’s how retail and ecommerce teams move beyond vanity metrics and win their market.
Inspired by this post on Amplitude – Perspectives.
Every week I review dozens of applications for PM roles, and in under 30 seconds I decide whether to keep reading. In 2026, the bar is higher than ever: clarity, outcomes, and customer insight beat buzzwords every time.
Learn how to write a standout product manager cover letter with steps, examples, templates, and smart AI workflows to make your application stand out.
I start with a crisp opening that communicates my value proposition in one sentence: the product problem I love solving, the customer I serve, and the measurable outcomes I drive. Then I connect my experience to the role’s core responsibilities—product discovery, product positioning, go-to-market strategy, and stakeholder management—without rehashing my resume.
A strong PM cover letter follows a simple structure: a hook with context, one paragraph proving product management leadership through outcomes vs output OKRs, a paragraph on how I partner with empowered product teams and engineering to ship, and a closing line that shows I understand the company’s roadmap and where I can help now.
To make this concrete, I include brief examples that show decisions, not duties: how I translated ambiguous customer signals into a roadmap, how I balanced platform scalability with speed, and how I measured success with activation, retention, and adoption—not vanity metrics.
Templates help me move fast, but I always tailor. I mirror the job’s language, highlight the few experiences that map 1:1, and cut everything else. I quantify impact where possible, link outcomes to business value, and keep it to 200–300 words so hiring managers can scan.
I also use smart AI workflows to accelerate the craft without sacrificing authenticity. My LLMs for product managers playbook: extract the role’s competencies, generate a draft outline, compare multiple versions with light A/B testing, and refine tone and clarity. Tools should augment judgment; the final voice is mine.
If you’re applying now, assemble your core template, slot in two role-specific examples, and close with a confident ask for next steps. With the right structure, clear outcomes, and a little AI leverage, your product manager cover letter will stand out in any stack.
I’m curating a living list of 2026 product conferences to help product managers, product leaders, and empowered product teams plan ahead with confidence. I use this calendar to align my team’s discovery work, roadmapping, and go-to-market strategy—and to prioritize conference networking and learning that moves the needle on product-led growth.
This list is not exhaustive. If there’s a product conference missing that should be here, please send it to conferences@producttalk.org. I’ll keep updating this as new events are announced so you have a reliable guide throughout the year.
I’ll be teaching a workshop and speaking at the Product at Heart conference in June in Hamburg, Germany. If you plan to attend, be sure to say hi.
Are you looking for the 2025 Product Conferences list? Find it here.
How I use this guide: I map events to our quarterly OKRs (outcomes vs output OKRs), focus on sessions that sharpen product discovery, stakeholder management, and product roadmapping and sprint planning, and bring a clear plan for takeaways I can apply the day I’m back. If you’re exploring AI Strategy and LLMs for product managers, you’ll find several strong options below.
January
Jan 28 — Product-Led Summit — Washington, DC, USA
Jan 30–31 — Prdkt+ — Cairo, Egypt
February
Feb 1–4 — WebSummit — Doha, Qatar
Feb 2–20 — DeveloperWeek Hackathon — San Jose, CA, USA & Virtual
Feb 4 — DDX Innovation & UX Conference — Tokyo, Japan
If you’re attending any of these, let me know—conference networking is always better with a plan and a friendly face. And if you’ve got a must-attend event on your radar, send it to conferences@producttalk.org so I can keep this guide comprehensive for the community.
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.
When I think about the roles that truly move the needle on AI Strategy and product outcomes, the Staff AI Engineer stands out. This is the person who can translate research into repeatable AI workflows, partner with product to solve real user problems, and operationalize models in a way that scales. It’s where innovation meets accountability—and where product management leadership meets hands-on engineering craft.
Ram Soma is a Staff AI Engineer at Amplitude, leading various AI initiatives across the company. He has a background in data science and machine learning engineering.
What does that look like in practice from my seat? It starts with precise problem framing and measurable success criteria. I align with a Staff AI Engineer on eval-driven development and instrumentation so we can track impact from prototype to production. With Amplitude analytics operating as a unified analytics platform, we can quantify user activation, retention analysis, and feature adoption, then iterate through continuous discovery with tight feedback loops.
Execution quality hinges on robust experimentation. Together, we design A/B testing plans with minimum detectable effect (MDE) targets, isolate confounding variables, and build evaluation harnesses that reflect real-world UX constraints. We also agree on rollout strategies—staged deployments, guardrails, and observability—so we can learn safely while preserving customer trust and performance SLAs.
On the technical approach, I look for pragmatic architectures that balance speed and reliability: a retrieval-first pipeline for grounding, judicious use of LLMs for product managers to instrument prompts and policies, and agentic AI patterns only when task decomposition truly reduces complexity. Just as important are privacy-by-design and data governance practices from day one, because responsible innovation beats retrofitting controls after the fact.
Finally, the magic happens in empowered product teams and product trios. When product, design, and Staff AI Engineering operate with shared context and clear constraints, we compress decision cycles and ship value faster. That’s how AI initiatives evolve from demos to durable capabilities—and how we enable product-led growth with measurable results that customers feel, not just features they see.
Inspired by this post on Amplitude – Perspectives.
