Tag: unified analytics platform

Connecting Product Analytics, Attribution, and Growth Decisions
Connected product analytics is not simply a larger collection of events, dashboards, and campaign reports. Its practical value comes from preserving the context behind customer behavior, applying consistent definitions, and carrying trustworthy insights into the systems where teams make decisions.

The four source articles describe complementary parts of that operating model: journey-aware attribution, governed product data, AI-assisted analysis across tools, and continuous measurement. Combined, they offer a framework for turning scattered signals into more defensible growth decisions.

Key takeaways
- Attribution becomes more informative when relevant campaign, session, and product context remains connected to later outcomes.
- Persisted context can reveal associations across a journey, but it does not by itself prove that a touchpoint caused a conversion.
- Naming standards, ownership, metadata, and shared customer definitions determine whether connected analytics can be trusted.
- AI agents and connectors can reduce the effort required to investigate and communicate insights, provided permissions and analytical boundaries are explicit.
- Growth improves through a repeatable learning loop that connects observed behavior to a decision, an intervention, and subsequent measurement.
Attribution improves when journey context survives the final click

The source on persisted properties challenges the idea that the last recorded interaction adequately explains a conversion. It reports that customer decisions may be shaped by activity distributed across sessions, channels, campaigns, and product experiences. In its examples, an e-commerce purchase may follow product discovery, promotions, and cart activity; a financial-services outcome may depend on education, trust-building, eligibility checks, and compliance-sensitive steps; and a B2B lead may emerge after product tours, comparison pages, demos, onboarding interactions, stakeholder reviews, and CRM touchpoints.

Persisted properties address part of this measurement problem by retaining meaningful context as a user continues through a journey. This gives analysts more than the attributes attached to the final event and supports questions such as which acquisition context is associated with later activation, which discovery experience precedes stronger conversion, or which onboarding path appears among retained users.

That richer context should not be confused with automatic causal proof. Attribution assigns or interprets credit according to available data and a chosen analytical approach. A recurring touchpoint may be a useful signal, a proxy for user intent, or an actual contributor to an outcome. Connected journey data makes those possibilities easier to investigate, while controlled experiments and other appropriate evaluation methods remain necessary when a team needs to establish whether changing a touchpoint changes the result.

The practical shift is therefore from asking which interaction deserves all the credit to asking which sequence of interactions warrants attention. That framing is more useful for product roadmaps, campaign investment, onboarding design, and retention analysis because it treats conversion as the outcome of a journey rather than an isolated click.

Data governance supplies the shared meaning behind every signal

More connected data creates more analytical value only when teams agree on what the data represents. The Pendo administration source emphasizes naming conventions, ownership rules, and review cycles for pages, features, segments, guides, and reports. It also describes visitor, account, and product metadata as a strategic asset that should reflect concepts such as onboarding stage, plan type, activation, customer-success motion, and retention.

The marketing analytics source approaches the same requirement from an organizational angle. It argues that analytics works best as a shared language across product, marketing, sales, and customer success. Instead of allowing each function to interpret campaign and product signals independently, teams can align around customer journeys, funnel behavior, and the points at which users find value or leave.

Together, these sources show that the semantic layer is as important as the technical connection. A campaign label, user segment, account tier, activation event, and retention definition must remain intelligible when they move between an analytics platform, a CRM integration, a product report, or an AI-assisted workflow. Otherwise, a connected system can distribute ambiguity more efficiently without improving judgment.

Governance also affects interventions, not just reports. The Pendo source recommends contextual and concise in-app guides, product tours, and tooltips tied to measurable outcomes. This connects the measurement layer to the product experience: the same governed definitions used to identify friction should inform who receives guidance, what behavior the guidance is intended to change, and how the result will be evaluated.

AI connectors reduce workflow friction but do not repair weak analytics

The agent-connectors source extends connected analytics beyond dashboards. It describes an agent working across tools already used by product, analytics, and go-to-market teams, allowing context, analysis, and action to be brought into a more unified interaction. Its central benefit is operational: people can spend less effort moving information between tabs and systems while maintaining the flow of an investigation.

The marketing source similarly presents AI as most useful when paired with behavioral analytics, customer context, disciplined measurement, positioning, and a clear go-to-market strategy. In that account, AI workflows improve the scale and speed of judgment; they do not create durable growth independently of a sound measurement practice.

This distinction matters because an agent can make an answer easier to obtain without making its underlying evidence more reliable. If event definitions conflict, metadata is incomplete, or attribution assumptions are hidden, a connected agent may produce a fluent response to the wrong question. The connector source therefore places importance on permissions, appropriate context, governance, and boundaries alongside prompt design.

A well-designed workflow should preserve the path from a business question to the supporting behavioral evidence. It should also make clear which system supplied the context, which segment or journey definition was used, and whether the result is a descriptive association, an attributed outcome, or evidence from a stronger evaluation. That transparency helps an agent accelerate analysis without becoming an unexamined source of truth.

A connected growth loop joins evidence, intervention, and learning

The sources converge on a continuous operating loop even though each enters it at a different point. Persisted properties preserve the journey context needed to form a better question. Governance and metadata make the relevant users, accounts, features, and outcomes consistently identifiable. Behavioral analytics helps teams locate meaningful movement or friction. Product guidance, campaigns, positioning changes, and go-to-market decisions then become interventions whose effects can be measured.

The Pendo source makes this learning loop explicit by recommending that initiatives record the expected behavior, the observed result, the change in the customer journey, and the team’s next response. The marketing source adds that product, marketing, sales, and customer success should use those findings collectively. The agent-connectors source supplies a potential interface for carrying the analysis across their tools, while the attribution source supplies the longitudinal context needed to avoid judging the intervention solely by the final interaction.

This model also clarifies what a useful growth insight looks like. It is not merely a rising metric or a generated explanation. It connects a defined audience and journey to an observable outcome, states the limits of the attribution, identifies a decision the organization can make, and establishes what should be measured afterward. That standard directs attention toward learning and resource allocation rather than dashboard activity.

The next stage of connected analytics will depend less on adding isolated reports and more on maintaining reliable context as questions move across teams and tools. Organizations that preserve that context, govern its meaning, and test the decisions made from it will be better positioned to turn analytics and AI into a durable growth capability.

References
July 3, 2026
Migrate Analytics Platforms Without Chaos: 7 Proven Lessons to Plan, Move, and Land Cleanly

I’ve led and rescued more analytics migrations than I can count, and I know the pressure: every event, dashboard, and decision pipeline depends on getting it right. Migrating analytics platforms doesn't have to be painful. Get seven lessons from Human37 and Amplitude to help your team plan, migrate, and land cleanly.

Here’s how I approach this work so teams keep momentum, regain trust in their numbers, and accelerate product-led growth on a unified analytics platform—without the rework and stakeholder fatigue that typically follow.

Lesson 1 — Start with outcomes, not events. Before moving a single event, I align leaders on the questions we must answer and the decisions we must speed up: activation, retention, and expansion. I map those goals to a simple driver tree, then back into the behavioral analytics we need. This trims noise, tightens scope, and ensures Amplitude analytics (or any destination) is instrumented for decisions, not vanity metrics.

Lesson 2 — Audit and map your data with rigor. I inventory current events, properties, IDs, and sources, then define a target schema with clear naming conventions, ownership, and versioning. Data governance and privacy-by-design are non-negotiable: we separate PII, document consent paths, and remove legacy debris. This step prevents schema drift and makes platform scalability sustainable.

Lesson 3 — De-risk the cutover with a phased plan. Rather than a big-bang switch, I dual-run critical flows, compare telemetry, and use feature flags to roll forward (and back) safely. Observability and anomaly detection are my guardrails: I monitor volume, cardinality, and event timeliness to spot regressions early—long before executives notice broken charts.

Lesson 4 — Treat instrumentation like product code. I wire schema checks into CI/CD, enforce typed analytics wrappers, and validate payloads pre-merge. With docs-as-code, the tracking plan stays current and reviewable. This keeps quality high at scale and avoids the slow death of broken funnels caused by well-meaning quick fixes.

Lesson 5 — Enable the people, not just the platform. Tools don’t create insight—teams do. I run hands-on enablement with product tours and in-app guides tailored to each role, establish communities of practice, and publish short playbooks for common questions (activation analysis, cohort retention, and journey mapping). When customer success and growth marketers can self-serve, adoption sticks.

Lesson 6 — Land cleanly with fast, visible wins. Within the first two weeks post-cutover, I showcase analyses that matter: retention analysis by use-case, friction points via session replay and heatmaps, and conversion lift by segment. These quick proofs build confidence, reinforce the value proposition, and keep stakeholders engaged through the longer tail of hardening.

Lesson 7 — Govern and evolve continuously. After go-live, I schedule schema reviews, backlog grooming, and QBRs to prune events and refine definitions. Ownership is explicit, and changes flow through the same review process as code. This keeps the unified analytics platform trustworthy as the product (and org) changes.

I’ve seen this playbook turn skepticism into momentum. In one migration I inherited mid-flight, we refocused on decisions, tightened governance, and phased the rollout; the team moved from fire drills to confident launches—and stakeholders finally believed the numbers again.

If your team is staring down a migration, anchor on outcomes, automate quality, and invest in enablement. With disciplined execution readiness and the lessons I’ve applied alongside partners like Human37 and platforms like Amplitude, you can move fast, reduce risk, and land cleanly—without the chaos.

Inspired by this post on Amplitude – Perspectives.

June 22, 2026
How I Use Novus, the First Product Agent, to Turn Rapid Releases into Measurable Wins

In a world of relentless CI/CD and accelerating release trains, product leaders like me can’t afford lagging signals or fuzzy readouts on what’s truly moving the needle. I need immediate, trustworthy feedback that connects code shipped to outcomes achieved and customer value created.

Coding agents compress weeks of development into hours, but the faster your codebase changes, the harder it is to know what’s actually helping end-users.

That tension is exactly why I brought Novus into my product toolbox. To keep up with the pace of development, over 600 product teams are already using Novus, the first-of-its-kind product agent, to automatically set itself up, monitor product data, and tell you what to do next.

From my chair, that promise matters only if it translates into clear decisions. With Novus, I’ve been able to tighten the loop between experimentation and learning: it pairs eval-driven development with behavioral analytics and observability so I can see how a release influences activation, engagement, and retention—without spelunking through fragmented dashboards. The agentic AI backbone reduces the manual stitching I used to do across events, cohorts, and funnels, letting me focus on prioritization and product strategy instead of report wrangling.

Day to day, Novus fits naturally into our AI workflows. It surfaces anomalies early, clarifies trade-offs, and frames next-best actions in the language of outcomes. Because it plugs into a unified analytics platform approach, I can maintain continuous discovery at scale while preserving the rigor of Agent Analytics: hypotheses are explicit, telemetry is consistent, and results are traceable. That’s the operating cadence I expect from modern product management leadership.

If your roadmap moves faster than your learning loops, a product agent can be the missing link between speed and certainty. Novus helps me convert rapid releases into measurable wins, keeping the team aligned and confident about what to build next—and just as importantly, what to stop doing.

Inspired by this post on Pendo – Best Practices.

June 17, 2026
Supercharge Insights with Amplitude Agent Connectors: Connect Notion, Slack, Linear & More

I’ve led enough multi-tool product organizations to know how quickly momentum erodes when insights and actions live in different places. When my teams bounce between Notion, Atlassian, Slack, Linear, and analytics dashboards, we pay a real tax in context switching. That’s why I’m excited about what Amplitude is enabling with Agent Connectors—bringing our daily work and our data-driven decisions into one fluid, agentic AI workflow.

Connect Notion, Atlassian, Slack, Linear, and more to Amplitude's Global Agent. Get richer analysis and take action across tools without leaving Amplitude.

Practically, this means I can treat Amplitude analytics as a unified analytics platform where analysis and execution finally meet. Instead of exporting charts or copying insights into docs, I can drive Agent Analytics directly from the same surface where I manage behavioral analytics, reducing friction and accelerating decisions. For my product strategy, that’s a meaningful shift—from “insight later” to “insight-to-action now.”

Here’s how I’d use it on a typical day: I ask the agent to synthesize signals from recent feature usage, spotlight anomalies, and then draft a concise summary for our Slack channel. In the same flow, I can prompt it to reference our Notion specs for context and queue next steps in Linear, keeping Atlassian stakeholders looped in without any extra swiveling between tabs. The value isn’t just faster execution; it’s tighter alignment across teams because the analysis and the plan live together.

From an operating model perspective, this is how I scale AI workflows responsibly. I can define clear prompts, approval paths, and ownership so the agent augments—not replaces—expert judgment. Data governance and permissions remain front and center: the agent sees what your teams are allowed to see, and we maintain auditability on critical workflow steps. The outcome is a trustworthy, repeatable system that compounds learning over time.

If you’re exploring agentic AI for product teams, start small and instrument your ROI. Pick one or two connectors (Slack and Notion are great first choices), define a measurable workflow—like pushing weekly retention insights and creating prioritized follow-ups in Linear—and iterate using continuous discovery. In my experience, the first wins appear as reduced time-to-insight, fewer meetings to align, and faster cycle time from observation to shipped change.

The big picture is simple: bring your work to your analytics, and your analytics to your work. With Agent Connectors, Amplitude’s Global Agent helps close the loop from understanding behavior to taking action—without leaving the place where your insights are born.

Inspired by this post on Amplitude – Best Practices.

June 3, 2026
From Vision to Execution: Building Agentic, Data‑Driven Products with Real‑World Rigor

When I consider where product development is headed, one statement captures the mandate perfectly: "Eric Carlson is a Principal AI Engineer helping to shape and build Amplitude's next generation vision of of agentic and data driven product development." That vision resonates deeply with how I lead teams—anchoring strategy in behavioral analytics while enabling agentic AI to act on insights with speed, safety, and measurable impact.

Translating that vision into execution starts with clarity of outcomes. I frame driver trees that connect customer value to leading indicators—activation, engagement depth, and retention—then instrument product telemetry with Amplitude analytics and behavioral analytics to surface the moments that matter. From there, we operationalize learning with A/B testing and feature flags, ensuring each hypothesis gets a fair, observable run and that we can safely ramp what works.

Agentic AI changes the operating model. Instead of static dashboards, we design autonomous workflows that observe signals, reason over context, and take action—grounded in a retrieval-first pipeline and governed by eval-driven development. For product managers, this demands fluency with LLMs for product managers and practical prompt engineering, plus rigorous AI Strategy around data governance, privacy-by-design, and risk scoring so agents remain trustworthy under real-world conditions.

Cross-functional cadence is everything. I partner closely with Principal AI Engineers and product trios to blend continuous discovery with execution: rapid user interviews to reveal intent, opportunity solution trees to prioritize, and outcomes vs output OKRs to align incentives. The result is a system where insights are unified, decisions are explainable, and agents improve through tight feedback loops across analytics, experimentation, and production telemetry.

If you’re building toward an agentic, data-driven future, invest in a unified analytics platform, shorten the path from signal to action, and measure learning velocity as carefully as feature delivery. With the right foundations, agentic AI becomes more than a feature—it becomes a force multiplier for product strategy, customer value, and sustainable growth.

Inspired by this post on Amplitude – Perspectives.

May 13, 2026
4 Costly Agent Analytics Myths—And the Data-Backed Metrics I Rely on Instead

In my work with product, operations, and support leaders, I’m often asked to help make sense of Agent Analytics—what to track, how to attribute outcomes, and where to invest. After reviewing countless dashboards and running experiments across human agents and AI agents, I’ve learned that some of the most common measurement beliefs are precisely the ones that lead teams astray.
What comes up in conversation with leaders about Agent Analytics, and why not everything is what it seems.
Below, I unpack four pervasive myths I encounter and share the data-centered practices I use to replace them. My goal is simple: help you upgrade the way you measure performance so you can improve customer outcomes, accelerate learning, and scale impact with confidence.
Myth 1: “Lower average handle time (AHT) means higher performance.” AHT is useful but incomplete. When teams optimize solely for speed, they often push complexity into repeat contacts, reopens, or escalations. In the data, that shows up as a weak or negative relationship between lower AHT and durable outcomes like first contact resolution (FCR), customer effort, or revenue per conversation.
Reality and what I measure instead: I right-size speed by pairing AHT with intent-level resolution and recontact rate. For simple intents (password reset, billing address update), shorter is usually better. For complex intents (tiered troubleshooting, multi-step verification), “right-speeding” wins—slightly longer interactions that prevent rework. Practically, that means segmenting by intent complexity using behavioral analytics, tracking weighted “intent resolution rate,” and monitoring repeat-contact windows (24–168 hours) to catch downstream pain.
Myth 2: “AI agent containment tells the whole story.” A high containment rate can mask failure modes such as unresolved intent, silent abandonment, or low-quality handoffs that frustrate customers and spike human workload later.
Reality and what I measure instead: I break containment into three parts for voice and chat flows: (1) intent resolution without escalation, (2) graceful handoff quality when escalation is necessary, and (3) post-handoff efficiency and satisfaction. For voice AI agent experiences, I also track escalation clarity (did the transcript summarize history and intent?), time-to-human, and customer satisfaction on the combined interaction. This provides a fuller view of customer support ai strategy effectiveness and avoids over-crediting automation for partial wins.
Myth 3: “Quality is subjective, so it can’t be measured at scale.” Teams often default to sporadic QA because they assume it can’t be standardized across channels or agent types. The result is noisy feedback loops and stalled coaching.
Reality and what I measure instead: Quality becomes measurable when it’s grounded in observable behaviors linked to outcomes. I use a rubric anchored in behavioral analytics (e.g., verified customer need, correct resolution path, policy compliance, empathy markers) and validate it via correlation with FCR, recontact, and retention analysis. To scale, I combine calibrated human reviews with AI-assisted scoring, check inter-rater reliability weekly, and use driver trees to connect quality levers to business results. This creates a consistent, coachable signal for both human agents and AI flows.
Myth 4: “If the dashboard is green after launch, we’ve won.” Early wins can reflect novelty effects, cherry-picked routing, or short-term incentives that don’t persist. Declaring victory too soon locks in fragile gains and hides regressions across cohorts.
Reality and what I measure instead: I treat go-live as the start of learning. I use A/B testing with a clear minimum detectable effect (MDE), stagger ramps, and hold out stable control cohorts for at least one full demand cycle. I track outcomes vs output OKRs—focusing on intent resolution, customer effort, and revenue/customer health over vanity metrics. I also monitor seasonality and channel mix shifts inside a unified analytics platform to ensure improvements generalize beyond the first week.
How I operationalize this day to day: (1) define intents and complexity upfront, (2) unify journey data across channels, (3) instrument resolution and recontact rigorously, (4) apply driver trees to isolate what actually moves outcomes, and (5) iterate via disciplined experiments rather than sweeping changes. This approach aligns product and operations, speeds up coaching, and ensures AI investments compound rather than decay.
If you’re rethinking your Agent Analytics stack, start by replacing each myth with a sharper metric: pair AHT with intent-level resolution, pair containment with handoff quality and satisfaction, pair QA with outcome-linked rubrics, and pair green dashboards with robust experiments. The payoff is a measurement system that earns trust, guides better decisions, and consistently improves customer and business results.

Inspired by this post on Pendo – Best Practices.

May 7, 2026
Mastering Product Marketing with Amplitude Analytics: Proven Playbooks for Sustainable Growth

I’m continually refining how we use analytics to elevate product marketing, and this collection brings together my most effective playbooks for driving measurable growth with Amplitude Analytics. If you’re focused on product-led growth, you’ll find pragmatic guidance on translating behavioral analytics into sharper positioning, stronger activation, and durable retention.

In my day-to-day work, I connect product strategy with go-to-market strategy by grounding every narrative in real user behavior. That means using event data to validate our value proposition, mapping journeys to uncover friction, and aligning product positioning with the moments that actually matter in-app. The outcome is a marketing engine that mirrors how customers discover, adopt, and expand within the product.

Activation and retention are where outcomes are won or lost. I detail how to set leading indicators for user activation, instrument key behaviors, and run retention analysis that distinguishes healthy engagement from noisy usage. You’ll see how I turn cohort insights into precise messaging, targeted onboarding, and experiments that compound over time.

Cross-functional execution is essential, so I share ways to operationalize a unified analytics platform across product, marketing, and customer success. With shared metrics, product trios can move faster from product discovery to launch, and marketing can scale campaigns that reflect what’s truly driving adoption. This tight loop reduces guesswork and increases our hit rate on both features and narratives.

If you’re building a modern product marketing function, these essays and guides will help you move from intuition-led storytelling to evidence-backed strategy. Dive in to learn how I connect behavioral analytics to positioning, packaging, and roadmap choices—so every campaign and release ladders up to meaningful customer outcomes and sustainable growth.

Inspired by this post on Amplitude – Perspectives.

May 4, 2026
Supercharge Claude and Cursor with Amplitude Plug and Play: Your AI Analytics Expert in One Install

I’m excited to share that we’ve brought Amplitude Plug and Play to the Claude and Cursor marketplaces—a lightweight way to infuse your everyday prompts with serious product analytics context and speed.

"Learn more about our new AI plugin, the easiest way to turn your favorite AI client into an analytics expert with a single-install."

For years, I’ve watched teams lose momentum hopping between dashboards, docs, and spreadsheets just to answer simple questions like “What changed in activation last week?” or “Which cohort is driving retention?” With Amplitude analytics and behavioral analytics at the core, Amplitude Plug and Play collapses that friction by bringing the answers to where you already think and build—inside Claude and Cursor.

In practice, this means I can ask natural-language questions such as “Show me the funnel from signup to activation by region,” “Compare retention week over week for new users from our latest release,” or “Summarize our last A/B testing results on onboarding” and get structured, context-aware responses. The goal is to keep me in flow while still honoring the rigor of a unified analytics platform.

What I love most is how this elevates both discovery and delivery. Product managers can accelerate continuous discovery by querying cohorts, drivers, and anomalies mid-conversation. Engineers working in Cursor or with Claude Code can validate event definitions, sanity-check metrics, and spot regressions without leaving their IDE. The result is tighter feedback loops and better decision quality.

Just as importantly, the experience is designed for clarity and consistency. When I ask about activation, I expect the same canonical definition every time. When I explore a retention analysis, I want clear assumptions and transparent logic. By anchoring responses to well-defined metrics and event taxonomies, the plugin helps reinforce good data governance while keeping the interaction fast and conversational.

Getting started takes only a few minutes. Open the Claude or Cursor marketplace, search for Amplitude Plug and Play, complete the single-install flow, and connect to your Amplitude analytics workspace. From there, start prompting as you normally would—only now your AI client can reason with product context.

This launch is part of how I see gen ai reshaping AI workflows for product teams: less context switching, more signal per prompt, and a shared, accessible understanding of what’s really moving the business. If you’re ready to turn your AI assistant into a trusted partner for product insight, Amplitude Plug and Play is a powerful next step.

Inspired by this post on Amplitude – Best Practices.

May 1, 2026
How We Taught Agentic AI to Speak Product Analytics—and Unlocked Actionable Insights

I set out to solve a deceptively simple problem: help our teams ask product questions in plain English and get trustworthy, analysis-grade answers—fast. That required more than a powerful model; it demanded agents that genuinely understand the language of product analytics, from behavioral analytics nuances to the messy reality of event taxonomies, funnels, and cohorts. In this post, I share how we engineered agentic AI that speaks our domain fluently and turns questions into decisions.

The core challenge wasn’t data volume or dashboard sprawl; it was semantics. Different teams said “activation,” “onboarding,” or “first value” and meant overlapping but distinct things. Our PMs, analysts, and engineers navigated a maze of synonyms across Amplitude analytics, Pendo, and our unified analytics platform. Generic LLMs stumbled on these nuances, so we built a shared ontology—driver trees anchored to a clear North Star—with canonical definitions for activation, retention, and conversion, plus consistent event naming and cohort logic.

We started with a rigorous metric catalog: every KPI linked to its drivers, exact formulas, cohorts, and time windows; every event mapped to a product taxonomy; every dashboard and SQL snippet versioned with ownership and lineage. That catalog became the ground truth for agents. We embedded data governance and privacy-by-design from the start—permissioning for fields and queries, PII redaction, and scoped access that reflected how product teams actually work.

Next, we built a retrieval-first pipeline to ground the agents in our corpus before generation. We indexed metric definitions, dashboards, experiment readouts, runbooks, and high-signal Slack threads so the agent could cite relevant artifacts, not just predict plausible text. With careful context window management and prompt engineering, the agent retrieves definitions and prior analyses, then plans multi-step actions: run a query, compare cohorts, check “minimum detectable effect (MDE)” for an A/B test, and summarize findings with references.

Architecturally, we treated this as “Agent Analytics”: an orchestrator that selects tools based on intent—querying Amplitude analytics or Pendo for behavioral paths and funnels, hitting our warehouse for cohort tables, or pulling experiment metadata and anomaly detection alerts. Tool use is permission-aware, auditable, and designed to fail safe. The agent’s outputs include citations back to the exact definitions, dashboards, and SQL used, so reviewers can validate and iterate.

Quality came from eval-driven development, not intuition. We built a gold set of representative product questions (activation inflections, retention analysis by segment, funnel drop-offs after feature launches) and scored the agent on faithfulness to definitions, numerical accuracy, latency, and actionability. We incorporated regression checks to catch drifts after schema changes, and we tuned prompts to reduce overconfident answers and push for clarifying questions when context was missing.

Safety and reliability were non-negotiable. We layered AI risk management with role-based access, guardrails that block destructive queries, and risk scoring for unfamiliar joins or sudden spikes in metric deltas. The agent logs every step—what it retrieved, which tools it called, and why—so analysts can replay and refine the chain of thought with transparent provenance.

The payoff: product teams now self-serve nuanced questions in minutes instead of days, and our analysts spend more time on discovery than report wrangling. Retention analysis improved as the agent standardized cohort logic; conversion investigations accelerated thanks to consistent funnel definitions; and cross-functional decisions aligned around the same driver trees and shared language. Most importantly, the agent turned ambiguous asks into structured analyses that stand up to scrutiny.

For fellow product leaders, my lesson is simple: start with semantics, not models. A crisp ontology, disciplined taxonomy, and clear ownership will outperform a flashy stack riddled with ambiguity. Avoid technology FOMO; favor retrieval-first grounding, small sharp tools, and continuous discovery with your product trios. When your organization speaks a common analytics language, agents can finally think with you, not just for you.

Next, we’re extending the agent’s planning skills to recommend experiment designs, estimate power and “minimum detectable effect (MDE),” and propose driver-tree-informed bet sizing. We’re also tightening feedback loops so every accepted answer, edit, or override strengthens the retrieval corpus and evaluations. The vision: a calm, reliable layer that makes rigorous product analytics feel conversational—and helps teams move from questions to confident action.

Inspired by this post on Amplitude – Best Practices.

April 13, 2026
Inside Amplitude’s ML Playbook: Practical Strategies for Smarter A/B Tests and Growth

I’m continually asked how machine learning can make product analytics more actionable. Drawing from Amplitude analytics in real-world settings, I’ve distilled what matters most for product teams that want faster, smarter decisions without sacrificing rigor.

When I design experiments, I start with minimum detectable effect (MDE) to size samples correctly and avoid costly, inconclusive tests. I pair that with disciplined A/B testing hygiene—clear hypotheses, thoughtful stop rules, and guardrails for key metrics—so results translate into credible product strategy choices instead of noisy dashboards.

For growth and retention, I map behavioral analytics to activation and long-term value. Driver trees help me connect feature adoption to revenue or retention, and anomaly detection keeps me from overreacting to outliers when seasonality or data quality shift.

I segment cohorts by user intent and lifecycle stage, measure user activation with crisp event definitions, and monitor leading indicators across a unified analytics platform. This keeps cross-functional conversations grounded, accelerates product-led growth, and reduces the risk of optimizing for vanity metrics.

Operationally, that means building self-serve views that flag MDE-ready experiments, surface retention analysis by cohort, and trigger anomaly detection alerts only when the signal outpaces noise. The payoff is fewer meetings debating data quality and more time shipping value.

If you’re leveling up your analytics stack, start by tightening experimentation basics, instrumenting activation and retention with behavioral analytics, and wiring in anomaly detection as a safety net. You won’t just move faster—you’ll learn faster, and with the confidence to bet big when the data earns your trust.

Inspired by this post on Amplitude – Perspectives.

April 2, 2026
Unlock Confident Decisions with Bayesian Statistics: Smarter A/B Tests from Small Samples

Shipping great products is a game of making high‑quality decisions under uncertainty. In my role leading product management, I’ve seen teams stall when classic methods demand huge sample sizes before we can say anything useful. Bayesian statistics has become my go‑to approach for turning sparse data into clear, decision‑ready insights—especially when traffic is limited or experimentation windows are tight.

Understand Bayesian statistics vs. frequentist methods and learn how Bayesian approaches improve experiment insights with small sample sizes.

Here’s why I rely on it in A/B testing: frequentist methods focus on p‑values and long‑run error rates, which are tough to translate into action. With a Bayesian lens, I can express outcomes as intuitive probabilities—“Variant B has a 92% chance to outperform A”—and use credible intervals to communicate likely ranges of impact. That clarity reduces decision friction and helps the team move faster with confidence.

Bayesian methods shine when sample sizes are small and the minimum detectable effect (MDE) of a frequentist test would be impractically large. I incorporate prior knowledge—historical conversion trends, seasonality, and learnings from related experiments—to stabilize noisy early data. Done thoughtfully, priors improve estimate quality without overfitting; I always run sensitivity checks to ensure the posterior is driven by the data we’re observing, not wishful thinking.

In practice, my workflow is straightforward. I set a prior from historical performance in Amplitude analytics, run the experiment, and update the posterior daily. I track the probability of superiority, expected lift, and a credible interval that the CRO role can rally around. When the probability of a meaningful win crosses a pre‑agreed threshold, we ship. When it doesn’t, we bank the learning and move on—no prolonged debates about p‑values that few stakeholders truly understand.

This approach also strengthens product discovery. By using behavioral analytics and retention analysis as informative priors, I can evaluate early signals from narrower cohorts—new geographies, niche segments, or enterprise accounts—where traffic is scarce. The result is faster iteration in product‑led growth environments, even when a full‑funnel test would take weeks to reach frequentist significance.

Operationally, I treat Bayesian experimentation as part of a unified analytics platform strategy. The same posterior machinery that powers A/B testing can support anomaly detection during releases, quantify risk in phased rollouts, and estimate lift from in‑app guides or product tours. Because results are framed in plain language probabilities, cross‑functional teams make better, faster decisions aligned to outcomes rather than outputs.

A few guardrails keep me honest. I preregister decision rules (stop/go thresholds, guardrail metrics), run prior sensitivity analyses, and document assumptions alongside results. That discipline prevents overconfidence, improves reproducibility, and builds trust with leadership.

If your experiments are bottlenecked by low traffic or you’re tired of waiting weeks for a binary “significant/not significant,” consider a Bayesian upgrade. You’ll get earlier readouts, clearer stakeholder communication, and a repeatable path to compounding learning—without sacrificing rigor.

Inspired by this post on Amplitude – Perspectives.

March 31, 2026
Unlocking Impact: What Amplitude’s MCP server and experimentation platform teach product leaders

In my role leading product management at HighLevel, I study the architectures and operating models behind high-velocity learning. I often reference "Amplitude's MCP server and its experimentation platform" as a benchmark for how to operationalize scale, reliability, and speed of insight across complex product ecosystems. That lens informs how I design processes, data flows, and decision loops that turn ambiguity into measurable outcomes.

Experimentation is the heartbeat of eval-driven development. In practice, that means running disciplined A/B testing, deploying targeted feature flags to de-risk rollouts, and sizing experiments with a clear minimum detectable effect (MDE) so we avoid vanity wins. When teams internalize these habits, we shift from opinion-led debates to evidence-led decisions—and that’s where product-led growth compounds.

I'm an AI enthusiast, so I think a lot about how experimentation accelerates AI roadmaps. The same rigor that validates UI changes should govern prompts, retrieval strategies, and policy settings for LLM-backed features. By treating AI behaviors as first-class experiment surfaces—and tying them to user activation, retention analysis, and value proposition metrics—we move faster without compromising safety, privacy-by-design, or customer trust.

Making this work in production demands clean instrumentation and a unified analytics platform. I look for stacks that combine Amplitude analytics with robust observability and CI/CD to ensure we can ship, measure, and iterate continuously. When platform scalability and data governance are baked in from the start, product trios can focus on product discovery rather than firefighting pipelines or reconciling metrics.

My playbook is straightforward: define decision-worthy questions, map them to crisp success metrics, run right-sized experiments with feature flags, and use consistent analytics to close the loop. Do this well, and you create a durable advantage—faster learning cycles, sharper product positioning, and a culture that lives by outcomes over output. That’s the real lesson I take from platforms that execute experimentation at scale: process and technology are table stakes; what wins is the discipline to learn relentlessly.

Inspired by this post on Amplitude – Perspectives.

March 27, 2026