Tag: product-led growth

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
Inside My Product Marketing Playbook: Amplitude Analytics Tactics That Drive PLG Wins

I’ve curated a focused set of product marketing insights that zero in on what actually moves the needle—turning data into decisions. You’ll find a special emphasis on Amplitude Analytics, because its behavioral analytics foundation makes it easier to translate product usage into clear messaging, sharper positioning, and measurable growth.

In my day-to-day as a product leader, I’m constantly bridging the gap between product discovery and go-to-market strategy. The best outcomes come when we connect quantitative signals to narrative: using behavioral analytics to inform the value proposition, refining product positioning with cohort trends, and driving product-led growth with activation and retention insights.

Here’s how I put this into practice. I start with user activation and retention analysis to identify the few behaviors that predict long-term value. Then I run tightly scoped A/B testing to validate messaging and in-product prompts that nudge those behaviors. When the numbers move, I translate wins into a consistent story—one that sales, success, and marketing can all rally around.

One pattern keeps repeating: clarity beats complexity. Instead of piling on more features, I focus on the minimum, verifiable set of behaviors that correlate with outcomes. That discipline makes it easier to craft a crisp value proposition, streamline go-to-market strategy, and accelerate feedback loops between product, design, and marketing.

As you explore this collection, expect practical playbooks over platitudes. You’ll see how to apply Amplitude Analytics to uncover hidden friction, validate hypotheses faster, and operationalize product-led growth motions that compound over time. My goal is to help you move from interesting dashboards to decisive actions that strengthen your roadmap and your revenue.

If you care about building empowered product teams that learn continuously, you’ll feel at home here. Dive in, borrow what works, and adapt the rest to your context—then measure it, iterate, and share the wins with your team.

Inspired by this post on Amplitude – Best Practices.

March 25, 2026
Designing AI-Powered CX at Scale: Lessons Inspired by Amanda Sime at Amplitude

Customer experience is where strategy, data, and execution converge—and where AI can deliver compounding value when thoughtfully designed. In my work, I’ve seen how the right CX vision becomes a growth engine when it’s operationalized through clear measures, robust analytics, and disciplined product practices.

"Amanda Sime is the Customer Experience Strategy Lead at Amplitude. She shapes CX strategy and partners across orgs to design and scale AI-powered solutions." That concise description captures a model I deeply respect: start with a strong CX strategy, then partner across the organization to make AI real in the day-to-day. It’s not just about new technology; it’s about aligning teams, systems, and incentives to deliver consistent customer value.

Translating that approach into practice requires a rigorous AI Strategy, anchored in measurable outcomes and informed by behavioral analytics. I prioritize journey mapping to expose friction, then connect those insights to AI workflows that enhance customer success and in-product guidance. When cross-functional partners—from solutions engineering to support—operate from a shared driver tree, the roadmap balances speed with sustainability.

Data is the backbone. A unified analytics platform—often centered on Amplitude analytics—helps teams move beyond vanity metrics to track user activation, feature adoption, and retention analysis with precision. With that foundation, we can test responsibly, iterate quickly, and validate impact with product-led growth motions that scale across segments without sacrificing quality.

Operational excellence matters just as much as vision. I’ve learned to treat CX programs like enduring products: build reliable feedback loops, connect customer support AI strategy to clear service-level outcomes, and empower product management leadership to make evidence-based tradeoffs. When teams have clarity on the problem space and access to trustworthy insights, they deliver solutions that feel both intelligent and human.

The real win is cultural: empowering product trios and partner teams to co-own outcomes, not just outputs. That’s how AI moves from a promising experiment to a durable capability—by aligning strategy, analytics, and execution so customers experience value at every touchpoint.

Inspired by this post on Amplitude – Perspectives.

March 25, 2026
Stop Flying Blind with AI Agents: Put Users at the Center with Pendo Agent Analytics

I’ve watched too many AI agent deployments celebrate velocity while overlooking the one thing that determines long-term success: whether real users are actually getting value. Dashboards tend to spotlight model upgrades, prompt tweaks, and launch counts, yet they rarely quantify task completion, trust, or time-to-value. That blind spot isn’t technical—it’s human.

Enterprises are spending 93% of their AI budget building agents and almost none know if those agents are actually working for users. Pendo Agent Analytics closes the gap.

In my product reviews, I look for evidence that agentic AI is improving outcomes across the customer journey, not just the demo path. Without behavioral analytics and observability, teams optimize for throughput instead of resolution, for novelty instead of reliability. This is where eval-driven development, A/B testing, and rigorous cohort analysis become non-negotiable: they translate agent performance into user impact we can measure and improve.

Here’s the pattern that works for me: define user-centric success metrics first, then let the AI follow. I prioritize signals like successful task completion, low-friction activation, reduced escalations, and sentiment lift—tied directly to product-led growth indicators such as retention and expansion. When these metrics move in the right direction, I know the agent is creating compounding value, not just answering faster.

Practically, I operationalize this with an analytics spine that captures end-to-end agent interactions: intents, prompts, responses, clarifying turns, handoffs, and final outcomes. I segment by persona, journey stage, and account tier to uncover where agents delight and where they degrade trust. With this foundation, I can run controlled experiments, spot anomalies early, and connect improvements in agent behavior to improvements in business performance.

Pendo Agent Analytics closes the loop by making these user outcomes visible and actionable. Instead of guessing whether an agent helped or hindered, I can analyze where users stall, which prompts or skills drive completion, and how interventions like in-app guides or product tours change behavior. That visibility lets me tune models and experiences in days, not quarters—and gives stakeholders confidence that our AI investments are paying off for customers.

If you’re scaling agents today, start small but instrument deeply: map top user intents, define offline and online evals, A/B test prompts and policies, monitor regressions, and tie every improvement to activation, adoption, and retention. The result is a durable feedback loop that keeps agents aligned with user value as your surface area grows.

AI agents are not a destination—they’re a capability. When we anchor that capability to clear user outcomes and measure it with the right analytics, we stop flying blind and start compounding advantage. That’s how we turn promising demos into dependable products.

Inspired by this post on Pendo – Best Practices.

March 25, 2026
Meet Amplitude’s Always‑On AI Analysts: Instant Answers Without Dashboards or Reports

For years, I’ve watched product, growth, and data teams burn cycles stitching together manual dashboards and reports, then slogging through replay review just to validate a hunch. That overhead slows discovery and delays decisions. The promise here is different: "Discover how Amplitude AI Agents help product, growth, and data teams turn questions into action without manual dashboards, reports, or replay review." As someone obsessed with decision velocity and evidence-based product strategy, that shift is exactly what I’ve been waiting for.

In practice, I think about "Amplitude AI Agents" as always-on data analysts embedded in our workflow. Instead of queuing requests or context-switching into tooling, I can ask targeted questions, get synthesized insights, and move directly to action. This is a powerful example of agentic AI meeting behavioral analytics in a unified analytics platform—removing friction between inquiry and impact while keeping teams focused on outcomes, not artifacts.

What changes for my day-to-day? I can interrogate customer behavior in real time, pressure-test hypotheses from discovery interviews, and quickly understand whether activation, retention, or monetization is the current constraint. If I’m probing a driver tree for activation or a retention analysis for a specific cohort, I can get to a decision faster—without waiting on someone to build a bespoke dashboard. That means more cycles spent shaping product strategy and fewer sunk into report wrangling.

This matters beyond speed. When product, growth, and data leaders anchor discussions in the same source of truth, we shorten the distance from signal to decision. That alignment is the backbone of product-led growth and continuous discovery: shared context, faster feedback loops, and clearer trade-offs. It also reduces the long tail of analytics debt—those one-off reports and stale views that quietly accumulate across teams.

Of course, adopting any AI workflow in analytics demands governance. I hold these systems to the same bar I set for my teams: clarity of assumptions, consistent metric definitions, and auditable reasoning. Pairing "Amplitude analytics" with strong data governance, CI/CD for analytics definitions, and lightweight evals helps ensure the recommendations we act on are reliable, reproducible, and explainable. AI should accelerate our judgment, not replace it.

The strategic shift is simple and profound: move from building dashboards to making decisions. With always-on analysis, we can spend less time instrumenting analytics theater and more time delivering customer value. That is how we translate insights into impact—and why I’m excited to operationalize this capability across our product trios and go-to-market partners.

Inspired by this post on Amplitude – Best Practices.

March 23, 2026
Kaizen for the AI Era: Tiny Daily Wins That Build Smarter, Scalable Customer Support

Every day, I challenge my teams to make one small, meaningful improvement—something so lightweight it’s impossible to ignore and easy to repeat. That tiny daily motion compounds, and over time it reshapes customer experience, operational quality, and team culture.

That’s the essence of Kaizen, the Japanese philosophy of continuous improvement. Developed in post-war Japan and popularized by companies like Toyota, Kaizen proves that small, steady changes lead to significant long-term results. In product management and customer support, this approach transforms big ambitions into daily behaviors that actually stick.

Crucially, Kaizen isn’t passive or unstructured. It thrives on three principles I reinforce across my org. First, small changes reduce resistance—when you lower the activation energy, teams move faster. Second, improvement is continuous, not occasional; instead of waiting for quarterly reviews or major releases, you ask: “What can we improve right now?” Third, everyone participates—the people closest to the work are best positioned to improve it. That’s how momentum spreads.

In practice, the cycle is simple: identify a small problem, test the change, measure the result, refine, and repeat. The point isn’t radical transformation in a single swing; it’s steady progress guided by data and observation—a rhythm that aligns beautifully with eval-driven development and continuous discovery.

At Intercom, we apply this same philosophy to how we manage our Agent Fin through a process we call the “Fin Flywheel”. Here’s how this works.

Train: Teach Fin how to handle and resolve the most complex customer queries.

Test: Run fully simulated customer conversations from start to finish to see exactly how Fin will behave before going live.

Deploy: Launch Fin across all channels so customers get consistent support wherever they reach out.

Analyze: Use AI-powered insights to review and improve Fin’s performance so it can deliver better customer experiences.

This isn’t a one-time setup; it’s a continuous loop where every interaction feeds ongoing improvement. Rather than deploying AI and assuming it will perform as expected, improvement is built into the system itself. The more Fin is used, the better it gets. That’s the hallmark of agentic AI done right—tight feedback loops, purposeful conversation design, and clear Agent Analytics that illuminate what to tune next.

But continuous improvement doesn’t stop with AI. Within our Human Support operations, I emphasize the same mindset that drives great LLMs for product managers: you instrument the experience, learn from real usage, and close gaps fast. We operate with a simple mindset: the first time that you solve a customer issue should be the last time it happens.

When a conversation reaches a human, we pause to diagnose and prevent recurrence. Why did this reach me? Why couldn’t Fin resolve it? How can we prevent this from happening again? Those questions anchor a culture of root-cause thinking and accelerate product-led growth by removing friction at the source.

To make this effortless, we’ve built a lightweight, AI-powered way to log suggestions in the moment—no long explanations or heavy admin required. Ideas are reviewed quickly and implemented by subject matter experts or by the team themselves. This keeps the flywheel spinning: insights flow in, fixes go out, and measurable outcomes improve.

The result is a frontline that evolves from reactive problem-solvers into a proactive improvement engine. The people closest to customers spot friction, suggest fixes, and see their insights shaped into meaningful change. It’s continuous discovery embedded in everyday work, not a side project.

Kaizen demonstrates that lasting progress doesn’t come from occasional transformation; it comes from intentional, everyday refinement. The “Fin Flywheel” applies that philosophy to AI. Our Human Support continuous improvement process applies it to human insights. Together, they create a shared system where both people and AI learn continuously from customer interactions.

When improvement is built into the mechanics of how you work, it stops being a one-off project and becomes an ingrained capability. Over time, those small daily improvements don’t just add up—they compound into a sustainable, data-driven advantage that elevates customer experience and differentiates your customer support ai strategy.

Inspired by this post on The Intercom Blog.

March 19, 2026
Ship Smarter with Amplitude + Lovable: See Behavior, Fix Friction, Iterate Faster

I build products with a simple mantra: launch, learn, repeat. Shipping fast is necessary, but shipping smart is what compounds. To do that, I keep analytics close to the work—inside the builder—so every decision is tied to real user behavior, not assumptions.

Connect Amplitude MCP to Lovable to understand user behavior, spot frictions, and ship better updates without leaving your builder.

In practice, this integration lets me bring Amplitude analytics and behavioral analytics directly into the creative flow. I can explore funnels, cohorts, and drop‑offs the moment I’m crafting an experience, then translate those insights into concrete changes without context switching. The result is tighter feedback loops and more confident iteration.

My typical loop looks like this: identify a friction point from funnel analysis, design two or three variants in the builder, and run A/B testing to validate the improvement. I focus on user activation and retention analysis as leading signals, because sustained engagement is the clearest indicator that we’ve solved a real problem. When the data confirms it, we promote the winning experience and move to the next opportunity.

Keeping the work inside the builder also supports continuous discovery. I can pair quantitative insights with qualitative observations, refine journey mapping, and document learnings while the context is fresh. That makes prioritization and product discovery more reliable, and it turns each iteration into a teachable moment for the team.

Strategically, this builder‑first approach enables product-led growth. With fewer handoffs and a unified analytics platform, we compress time from insight to impact. It helps me defend roadmap decisions with evidence, communicate trade‑offs clearly, and keep the team focused on outcomes that matter to customers and the business.

If your goal is to iterate with speed and precision, bring analytics to where you build. Keep the loop tight, measure what moves the needle, and let the data guide your next best update.

Inspired by this post on Amplitude – Best Practices.

March 11, 2026
Inside Amplitude’s AI Acquisition: Career Lessons Product Managers Can Use to 10x Impact

I’m often asked how to translate early-stage experience into outsized product impact at scale. In my own practice, I study real career arcs that crystallize the habits of high-leverage product managers—especially those operating at the intersection of analytics and AI strategy.

Consider this path: Lucas is a Product Manager at Amplitude. Previously, he was employee #1 at Command AI, acquired by Amplitude in October 2024. Lucas studied computer science at Princeton.

What stands out to me is the compounding effect of being an early builder. When you are employee #1, you live close to the user problem, own outcomes end-to-end, and develop a bias toward focused, continuous discovery. That foundation creates durable instincts around product strategy, sharp prioritization, and empowered product teams—skills that transfer directly to later-stage environments where clarity and speed become competitive advantages.

Acquisition integration is where those instincts meet enterprise rigor. Folding Command AI into a unified analytics platform like Amplitude requires disciplined product roadmapping and sprint planning, precise stakeholder management, and a strong POV on where AI augments core “Amplitude analytics” versus where it creates net-new value. The north star remains unchanged: deliver measurable customer outcomes that strengthen product-led growth and reduce time-to-value.

On the AI front, I’ve seen the most successful PMs treat gen ai and LLMs for product managers as means, not ends. They anchor use cases to concrete analytics workflows—accelerating insight generation, surfacing anomaly detection, improving retention analysis, and driving user activation—while validating each step through continuous discovery and rigorous experiment design. This balance of ambition and evidence protects teams from shiny-object drift and keeps investment tethered to business impact.

Execution-wise, the playbook is straightforward but unforgiving: clarify the problem through customer interviews; define crisp outcomes vs output OKRs; map the journey end-to-end; ship in thin slices; and iterate with observability baked into every release. Along the way, keep your cross-functional partners close—solutions engineering, customer success, and GTM—so that your learning loops extend beyond the product surface and into real adoption dynamics.

If you’re building analytics or AI-powered experiences today, borrow these lessons: translate early-stage builder energy into enterprise-scale focus; make AI serve the product, not the other way around; and use Amplitude analytics to close the loop from idea to impact. That is how PMs compound credibility, accelerate careers, and, most importantly, create products customers can’t live without.

Inspired by this post on Amplitude – Best Practices.

March 11, 2026
Unlock High-Impact Mobile Engagement: Amplitude Guides & Surveys for iOS, Android, React Native

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

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

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

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

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

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

Inspired by this post on Amplitude – Best Practices.

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

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

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

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

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

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

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

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

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

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

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

Inspired by this post on Pendo – Best Practices.

March 11, 2026
Real-Time Answers in Slack and Teams: How Amplitude’s Global Agent Elevates Product Decisions

I’ve been looking for a pragmatic way to put product analytics where my teams already work—inside Slack and Microsoft Teams. The moment insights are one message away, cycle time shrinks, debates get crisper, and experiments move faster. That’s why I’m bringing Amplitude Global Agent into our daily decision flow to deliver instant, source-backed answers with visual clarity and actionable next steps.

Connect Amplitude Global Agent to Slack or Microsoft Teams to answer questions with source-backed analytics, charts, and recommended actions like A/B tests.

What excites me most is the shift from dashboards to dialogue. Instead of digging through reports, I can ask a focused question in Slack—“How did activation change week-over-week for our self-serve cohort?”—and get a chart in-channel, complete with recommendations that point me toward the next best move. This is Agent Analytics done right: faster insight loops, reduced context switching, and more confidence in the decisions we make every day.

From a product management perspective, this integration strengthens continuous discovery and aligns product trios around the same truth. Engineers, designers, and PMs see the same chart, discuss trade-offs in the same thread, and can agree on an action—often an A/B test—within minutes. It’s a lightweight but powerful way to support product-led growth and keep our roadmap tied to measurable outcomes.

In practice, the questions I ask the most look like this: “Which onboarding step causes the biggest drop-off this month?”, “Which channels drive the highest L28 activation rate?”, and “Where did retention improve after our pricing change?” In each case, the Agent returns charts we can share instantly with stakeholders, plus recommended actions like A/B test ideas to validate hypotheses quickly. The result is a reliable rhythm: ask, see, align, act.

Governance matters just as much as speed. We’re configuring strict permissions, role-based access, and purposeful channel placement so analytics land where they should—no broader, no narrower. We’re also leaning into clear query prompts and naming conventions for events and properties to help the Agent retrieve precisely what’s needed, every time. The aim is a high-signal, low-noise system that maintains trust while accelerating decisions.

To embed this into our operating cadence, I plug the Agent into three moments: daily standups (to scan activation, conversion, and incidents), weekly product reviews (to align on experiment status and next bets), and executive QBR prep (to pull clean, shareable charts fast). Because the insights arrive in Slack or Microsoft Teams, our conversations stay focused and traceable, and decisions get documented in the same place they were discussed.

We’ll measure impact with simple, telltale indicators: fewer ad-hoc analytics requests, faster time from question to decision, increased A/B test velocity, and clearer links between recommended actions and outcome metrics like activation and retention. My bar is straightforward—if this Agent can help one team make a better decision per day, it will more than pay for itself across the org.

If you’re considering a similar move, start small: connect one high-signal channel, curate a handful of common queries, and coach your team on good prompts. Within a week, you’ll feel the difference. When analytics become conversational, momentum follows—and your product strategy benefits from sharper, faster, and more transparent decision-making.

Inspired by this post on Amplitude – Best Practices.

March 4, 2026
From Chaos to Clarity: My Proven Playbook to Scale an Analytics Taxonomy That Sticks

I’ve stepped into too many product reviews where teams argued over numbers that should have been obvious. Three names for the same “signup” event, properties scattered across tools, and no shared definitions—classic analytics chaos. As VP of Product Management at HighLevel, I’ve learned that scaling an analytics taxonomy isn’t just a data exercise; it’s a leadership mandate that unlocks decision velocity, alignment, and confident product bets.

Learn best practices our professional services team has compiled in helping customers move from scattered events to a scalable, user-friendly data structure.

Why does this matter so much? A robust taxonomy powers a unified analytics platform across Amplitude analytics, Pendo, and our CRM stack, reduces rework, and strengthens data governance. When events are clear and consistent, product-led growth accelerates: onboarding becomes measurable, activation is trackable, and retention analysis turns into a weekly ritual rather than a quarterly scramble.

I always start with outcomes, not events. We define a North Star metric and use driver trees to map how user behaviors ladder up to that outcome. Then we ground the plan in journey mapping: what signals mark activation, aha moments, and long-term engagement? This ensures our taxonomy mirrors real user intent, not just engineering convenience.

Next comes naming conventions and structure. We standardize on a readable, durable pattern (for example, actor_action_object), apply consistent property naming, and document required vs. optional properties. We version events deliberately, so we can evolve without breaking dashboards. Most importantly, we align events to product strategy—tracking less, but better.

Governance makes it scale. We establish a clear DRI for the tracking plan, a lightweight review process for changes, and a schema registry that serves as the single source of truth. Privacy-by-design is non-negotiable: we treat sensitive fields deliberately and audit access. Observability closes the loop—schema validations and alerts catch drift before it confuses teams.

Tooling and process turn good intentions into muscle memory. We keep the tracking plan “as code” in a repository, run CI/CD checks to validate events, and use feature flags to roll out new instrumentation safely. Pendo helps us annotate in-app experiences, while Amplitude provides the exploratory lens for cohorts, funnels, and retention. Together, these systems reduce guesswork and speed up discovery.

Migrations are where many teams stall, so I de-risk them with a clear, time-boxed plan. We audit the current event surface, map scattered events to the new taxonomy, and deprecate duplicates with guardrails. We communicate changes broadly, provide easy-to-scan documentation, and pair enablement sessions with hands-on examples from live dashboards. The goal is confidence, not just compliance.

We measure success like a product. Are we answering critical questions faster? Are duplicate events trending down? Are activation and retention questions easy to answer in under five minutes? When the taxonomy is working, stakeholders stop asking, “Do we trust this?” and start asking, “What should we build next?”

One of the most rewarding shifts I’ve seen: product trios moving from ad-hoc analyses to repeatable, weekly rituals. With crisp definitions, onboarding flows become testable, PLG motions are predictable, and leadership reviews focus on outcomes, not definitions. That’s the moment analytics transforms from a cost center into a growth engine.

If you’re staring at a wall of scattered events, start small: clarify outcomes, align your journey map, set conventions, and ship a minimum viable taxonomy to one critical flow. Iterate quickly. The compounding payoff—clarity, speed, and trust—will be obvious to every team you partner with.

When we do this well, analytics becomes a strategic asset. Our teams spend less time reconciling numbers and more time building what matters. That’s the real meaning of moving from chaos to clarity.

Inspired by this post on Amplitude – Best Practices.

March 3, 2026