Tag: A/B testing

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
Unlock Data-Driven Growth: My Take on Analytics, Experimentation, and Personalization Mastery

I’m sharing a focused set of insights on analytics, experimentation, and personalization designed to help teams ship smarter, reduce risk, and accelerate outcomes. Drawing on years of leading product teams, I translate complex data practices into practical playbooks you can apply immediately to improve user activation, conversion, and retention.

My approach starts with a strong measurement foundation. I lean on a unified analytics platform—often powered by tools like Amplitude analytics—to centralize product, marketing, and customer success signals. With clear event taxonomies, consistent governance, and trustworthy dashboards, teams gain a single source of truth to prioritize the right problems and sequence roadmap bets with confidence.

Experimentation turns insight into evidence. I emphasize A/B testing discipline, including minimum detectable effect (MDE), guardrail metrics, and pre-registered hypotheses. This repeatable system lifts decision quality, shortens feedback loops, and aligns cross-functional partners around what actually moves the needle, not what merely sounds promising.

Personalization compounds the value of experimentation by delivering the right value to the right segment at the right moment. Thoughtful in-app guides and product tours—rooted in behavioral signals—nudge users through friction points and increase the likelihood of early wins. The result is a more intuitive path to first value, stronger user activation, and healthier long-term engagement.

Retention is the ultimate scoreboard. I rely on retention analysis, cohorting, and leading-indicator metrics to connect feature usage to durable outcomes. When paired with product-led growth motions, teams can identify activation thresholds, build habit loops, and scale what works without overextending sales or support capacity.

If you’re getting started, begin with a crisp instrumentation plan, shared definitions, and a lightweight review ritual. Use continuous discovery practices, opportunity solution tree mapping, and driver trees to tie data signals to real user problems. From there, iterate: test small, learn fast, and scale what is proven. Over time, this system becomes a flywheel for product strategy—fewer debates, more evidence, better products.

In this series, I distill the frameworks, templates, and real-world lessons that have consistently improved outcomes for product teams: how to structure experiment backlogs, how to read funnel breakpoints, how to detect false positives quickly, and how to operationalize analytics for day-to-day decisions. Expect practical guidance you can copy, adapt, and run with immediately.

Inspired by this post on Amplitude – Perspectives.

February 20, 2026
Design Smarter with Amplitude + Figma Make: AI-Powered Prototyping, Testing, and Learning

I rely on Amplitude analytics and Figma Make to turn real user insights into high-fidelity prototypes in hours, not weeks. This pairing compresses our continuous discovery loop and helps my team prioritize what truly moves the needle for customers and the business.

Design smarter with Amplitude and Figma Make. Use AI and product analytics together to prototype, test, and learn faster.

Here’s how I put that into practice: I start with product analytics to isolate a measurable opportunity—often around user activation, conversion drop‑offs, or retention analysis. Amplitude cohorts and funnels surface where friction hides; I translate those signals into design prompts and flows in Figma Make, so we can visualize and validate potential solutions before a single line of production code is written.

Once a promising direction emerges, I convene the product trio—design, engineering, and product—around a clear outcome metric, not output. We build a lightweight driver tree, align on a hypothesis, and define the minimum detectable effect (MDE) so our A/B testing has enough statistical power to be decision‑worthy. From there, we create a small set of Figma Make variations that reflect distinct value hypotheses, not cosmetic tweaks.

On the experimentation front, I gate risky changes behind feature flags and ship via our CI/CD pipeline to limit blast radius and accelerate feedback. I monitor the experiment with a unified analytics platform mindset: the same definitions and segments in Amplitude power both pre‑launch discovery and post‑launch evaluation. That continuity lets us compare prototype expectations against production reality with far fewer translation errors.

A few principles keep this workflow sharp and responsible: I use privacy-by-design patterns, apply data governance guardrails to keep datasets consent‑aligned, and set AI risk management standards so generated designs respect accessibility and brand constraints. Critically, I avoid vanity metrics—I measure learning speed, decision quality, and downstream impact on activation or retention, which are what sustain product-led growth.

If you’re looking for a playbook, try this cadence: 1) define the customer outcome and success metric; 2) map a simple driver tree to narrow the solution space; 3) explore multiple flows in Figma Make; 4) validate quickly with concept tests and usability checks; 5) run A/B testing with a clearly defined MDE; 6) ship iteratively behind feature flags; 7) close the loop in Amplitude with cohort‑level retention analysis; 8) refine copy and UX writing to reinforce the core value proposition. Repeat until the signal is undeniable.

Blending Amplitude analytics with Figma Make has become my fastest path from insight to impact. It keeps my team focused on learning that compounds, features that matter, and outcomes customers can feel—so we truly make what matters.

Inspired by this post on Amplitude – Best Practices.

February 19, 2026
Can AI Agents Master Enterprise Analytics? My Proven Task Framework and Amplitude Insights

Every week, product and data leaders ask me the same question: can AI agents truly shoulder enterprise analytics without sacrificing trust, governance, or speed? I’ve spent the past year putting agentic AI through its paces in real product workflows, and I’ve distilled what works into a practical, task-driven evaluation approach you can adopt immediately.

Learn how to evaluate AI analytics agents with a task-based framework across analytics tasks. See how Amplitude’s Global Agent scores.

When I say “enterprise analytics,” I’m talking about far more than chatty dashboards. The bar includes consistent metric definitions, privacy-by-design, RBAC and data governance, audit trails, low-latency decision support, and repeatable outcomes across retention analysis, funnels, cohorts, A/B testing, instrumentation planning, and anomaly detection—ideally within a unified analytics platform.

My task-based framework evaluates eight capability pillars I expect from an enterprise-ready Agent Analytics solution: task coverage and depth across common product analytics workflows; data fidelity and governance (lineage, access controls, PII handling); instruction-following and reasoning transparency; evaluation rigor and reliability (repeatability, error modes, regressions); security and compliance posture; latency and cost efficiency; integration into existing product strategy workflows (e.g., CRM integration, CI/CD-linked instrumentation, experiment platforms); and human-in-the-loop controls for approvals and guardrails.

Operationally, I define canonical tasks that reflect day-to-day product management: codify a North Star metric; perform retention analysis by cohort; generate and explain a funnel with drop-off drivers; recommend an event taxonomy and tracking plan; analyze an A/B test with minimum detectable effect (MDE) considerations; and propose a driver tree that maps inputs to outcomes. Each task comes with ground-truth datasets, acceptance criteria, and edge cases to stress the agent—an eval-driven development practice I’ve found indispensable.

I then score maturity across four levels. L0: a pure chat UI that summarizes existing charts. L1: a retrieval-first pipeline that grounds responses in your analytics catalog and metric store. L2: a tool-using agent that is schema-aware, can write safe SQL, and reconciles results to canonical definitions. L3: a governance-aware autonomous workflow that executes analytics tasks end-to-end with approvals, audit logs, feature flags, and rollback plans. Most teams discover they’re between L1 and L2; reaching L3 requires serious investment in data governance and eval automation.

Risk management is non-negotiable. I require strict data governance and privacy-by-design controls, including scoped credentials, PII redaction, policy-aware retrieval, and comprehensive observability (query traces, prompt/response logs, lineage). Feature flags and approval gates prevent unintended metric redefinitions. Red-teaming tasks expose prompt injection, schema drift, and hallucination failure modes before they hit production stakeholders.

Where do agents shine today? Rapid exploration, SQL generation from schema context, summarizing experimentation results, and turning natural-language questions into actionable charts. Where do they struggle? Ambiguous metric semantics, under-specified experiment designs, and edge-case-heavy analyses where ground truth depends on organizational nuance. The cure is disciplined product management: codify definitions, maintain a living analytics taxonomy, and continuously harden your eval suite.

In the context of product analytics stacks, Amplitude analytics is a common anchor for product teams, and many are evaluating “Amplitude’s Global Agent” to accelerate insight generation. In my framework, I look for how well it grounds to canonical metrics, handles retention and funnel tasks, explains trade-offs, and respects governance boundaries—before I consider expanded autonomy. I share the full task matrix and scoring rubric so you can replicate the assessment in your environment.

If you’re getting started, pick your top ten high-frequency analytics tasks and define crisp success metrics for each (accuracy, explainability, latency, and reusability). Build a small eval harness with golden datasets, assertions, and regression tests. Favor a retrieval-first pipeline tied to your taxonomy and metric store, add human-in-the-loop approvals for sensitive actions, then pilot with a cross-functional tiger team. Measure time-to-insight, analyst hours saved, and stakeholder trust—then iterate.

Enterprise analytics isn’t a single feature; it’s a system of definitions, workflows, and governance. With a task-based, eval-driven approach, agentic AI can become a reliable partner—not just a novel interface. If you’re evaluating options, apply this framework first, then expand scope as reliability and trust climb.

Inspired by this post on Amplitude – Best Practices.

February 17, 2026
What I Learned Scaling Analytics: Candid Lessons on Product Strategy and Product-Market Fit

I write from a place many product leaders know well—the moment when the data you need to make decisions simply doesn’t exist, and you have to build the capability from the ground up. That firsthand experience with gaps in analytics shaped how I think about product strategy, product discovery, and the relentless pursuit of product-market fit lessons.

In my work, I lean on continuous discovery to surface the most meaningful problems, then translate those insights into outcomes vs output OKRs that keep teams focused on impact. When we anchor roadmaps to real user behavior and business results, we avoid vanity metrics and create a durable plan that compounds learning over time.

Execution matters just as much as insight. I rely on rigorous A/B testing, clear minimum detectable effect (MDE) thresholds, and retention analysis to separate signal from noise. This discipline ensures that every iteration—whether it’s a small UX nudge or a bold bet—moves us closer to measurable value for customers and the business.

None of this works without empowered product teams. I build around product trios that partner tightly across design, engineering, and product, and I foster a product-led growth mindset so we earn activation, engagement, and expansion through the experience itself. The goal is to create a system where learning is fast, ownership is clear, and the user’s job-to-be-done stays front and center.

On the tooling side, I favor a unified analytics platform so insights are consistent from discovery to deployment. Whether I’m instrumenting funnels with Amplitude analytics or stitching together qualitative and quantitative inputs, the principle is the same: give teams trustworthy, real-time visibility so they can make better decisions, faster.

If you’re looking to operationalize these practices, you’ll find practical playbooks, decision frameworks, and real-world examples here—built for leaders who want clarity, speed, and confidence in how they discover, ship, and scale products.

Inspired by this post on Amplitude – Best Practices.

February 17, 2026
Reinventing Product Management Workflow: The AI Upgrade I Use to Ship Faster, Smarter

The most valuable upgrade I’ve made to my product management workflow isn’t a new framework or a shiny dashboard—it’s an AI-first operating model that compresses discovery-to-delivery cycles while increasing confidence in every decision. I built this approach to reduce context switching, remove toil, and keep the team relentlessly focused on outcomes over output. The result is a faster, clearer, and more reliable path from insight to shipped value.

Here’s how I run an AI-powered product workflow end to end: continuous discovery, opportunity sizing, solution shaping, planning, execution, and iteration—each step instrumented with automation, retrieval, and evaluation so we learn faster without compromising rigor.

Intake and triage start with a retrieval-first pipeline that unifies customer feedback, support tickets, sales notes, research transcripts, and usage analytics. I use embeddings to cluster themes, de-duplicate signals, and surface the most representative examples. This gives me an instant, always-fresh view of customer jobs, pains, and opportunities without manually combing through noise.

For discovery, I rely on “LLMs for product managers” to accelerate the hard parts without replacing judgment. I generate interview guides, summarize transcripts, extract entities, and tag moments of friction. Prompt engineering and context window management ensure the model sees the right evidence at the right time. I keep all sensitive data governed by privacy-by-design and data governance controls.

Opportunity sizing is where I connect insights to business impact. I map problems to a driver tree, quantify potential lift, and align to outcomes vs output OKRs. When relevant, I apply the Kano Model to balance performance, basic, and excitement attributes. To maintain rigor, I use eval-driven development on my prompts and heuristics so prioritization is repeatable, not anecdotal.

Solution shaping is a collaborative exercise with product trios. I draft problem narratives and PRDs, generate acceptance criteria, and create first-pass UX flows. For speed, I use gen ai for product prototyping to explore alternatives quickly, then gate final choices through usability feedback and feasibility checks. Where uncertainty is high, I define a minimum detectable effect (MDE) and design A/B testing plans upfront.

Planning ties strategy to execution through product roadmapping and sprint planning. I break work into sequenced bets, enable feature flags for controlled exposure, and wire quality signals into CI/CD. DORA metrics—like deployment frequency and change failure rate—help me keep the system honest. Observability ensures we see the “why” behind behavior, not just the “what.”

Execution is instrumented with in-app guides, Intercom messaging, and Pendo to shape onboarding and activation. I connect Amplitude analytics to measure habit formation, retention analysis, and feature adoption. When experiments run, I monitor leading indicators in near real time while protecting against peeking and p-hacking. The point isn’t to prove we’re right; it’s to learn fast enough to get right.

Iteration closes the loop. I use a unified analytics platform to compare expected vs actual outcomes, harvest qualitative feedback, and push new evidence back into discovery. The system improves with each cycle because the retrieval-first pipeline and eval harness both get smarter as data grows.

Governance is non-negotiable. AI risk management, cybersecurity, and regulatory compliance sit alongside model evaluations to prevent drift, leakage, or bias. I document decisions, model versions, and test artifacts so we can audit how we got to a call—especially when trade-offs are nuanced.

If you’re standing up this AI workflow from scratch, I recommend a 30/60/90 rollout. In the first 30 days, audit your data sources and build a retrieval-first pipeline. In days 31–60, pilot two high-leverage workflows—continuous discovery and PRD drafting—backed by eval-driven development. By days 61–90, scale to prioritization and experiment design, then thread the outputs into your planning and CI/CD rhythms.

Common pitfalls I watch for: over-automation that blurs context, lack of evaluation frameworks, ungoverned data that undermines trust, and vanity metrics that celebrate activity over outcomes. The antidote is simple but disciplined—clear decision criteria, measurable hypotheses, and automated evaluations that run as guardrails, not bottlenecks.

This AI upgrade doesn’t replace the craft of product management; it amplifies it. By combining judgment, clear strategy, and reliable automation, we ship value faster, reduce risk, and make better calls under uncertainty. The payoff is durable: compounding learning velocity and a team that spends more time solving the right problems—and less time wrestling the process.

Inspired by this post on Product School.

February 11, 2026
The Customer Feedback Playbook: AI-Powered Tactics I Use to Make Better Product Decisions

Customer feedback is the most reliable compass I have for product strategy and execution. Over the years leading product at HighLevel, I’ve built and refined a system that turns raw signals from users into clear, prioritized decisions our teams can confidently ship.

A practical guide to collecting and using product feedback in product management (from AI tools to early-stage tactics) for better product decisions.

My playbook starts with continuous discovery. I keep a steady flow of insights from sales calls, customer support threads, community forums, and in-product behavior so I can triangulate patterns rather than chase loud anecdotes. This mix of quantitative and qualitative data helps me separate urgent noise from strategically meaningful trends.

On the quantitative side, I rely on product analytics to ground the conversation. Amplitude analytics gives me activation, retention cohorts, and feature engagement, while controlled experiments and A/B testing validate whether an idea actually moves a target metric. Tying these signals to specific customer segments helps me see where product-led growth is working—and where it’s stalling.

For qualitative insight, I combine in-app guides and lightweight surveys (via tools like Pendo) with structured interviews and support escalations (often surfaced through platforms like Intercom). I map problems using the Kano Model to understand which requests are basic expectations, which are performance drivers, and which are potential delights. This keeps our roadmap focused on outcomes, not just outputs.

AI now accelerates the synthesis step. With LLMs for product managers in my AI product toolbox, I summarize interview transcripts, cluster themes across thousands of notes, and quantify sentiment without losing nuance. I still review raw artifacts to avoid hallucinations and preserve context, but AI reduces the time from signal to insight dramatically—freeing me to spend more energy on judgment and storytelling.

In early-stage contexts, I bias toward speed and proximity to users. I schedule founder- or PM-led discovery calls weekly, instrument product tours early, and launch scrappy in-product prompts to validate demand before over-investing. When data is sparse, I focus on high-signal channels (power users, churned customers with qualified use cases) and document crisp problem statements that connect directly to activation, retention analysis, and revenue outcomes.

Prioritization ties everything together. I translate insights into hypotheses aligned to outcomes vs output OKRs, then pressure-test them with feasibility and strategic fit. We run small, measurable experiments, track deltas in activation and retention, and adjust the product roadmapping and sprint planning cadence based on what the data and customers teach us.

This approach builds trust with stakeholders and creates empowered product teams. By grounding decisions in a transparent trail of feedback, analytics, and experiments, we reduce thrash, move faster, and—most importantly—ship product moments that customers value.

If you’re refining your own feedback engine, start by instrumenting the basics, set a weekly discovery rhythm, and let AI handle the heavy lifting on aggregation and synthesis. The compounding effect is real: better insights lead to better bets, which lead to better outcomes for your users and your business.

Inspired by this post on Product School.

January 26, 2026
Stop Drowning in Dashboards: Real-Time Digital Analytics for Finserv Contact Centers

I’ve sat in enough finserv contact center reviews to know the pattern: wall-to-wall dashboards, weekly exports, and colorful charts that still leave teams asking, “So what should we do next?” The truth is, more dashboards rarely create better decisions. What we need is digital analytics that translates signals into action—fast, precise, and privacy-safe.

When I say digital analytics, I mean a unified analytics platform that captures real-time behavioral data across voice, chat, IVR, email, and in-app journeys, then operationalizes it for agents, supervisors, and automated workflows. See how real-time behavioral analytics helps finserv contact centers lower costs, improve resolution speed, and deliver better member experiences.

Dashboards tend to be lagging, siloed, and optimized for reporting, not resolving. They spotlight vanity metrics, bury journey-level friction, and rarely surface the “next best action” that actually moves a member request toward resolution. By the time a trend shows up in a weekly readout, the expensive part—handle time, repeat contacts, churn risk—has already accumulated.

Real-time digital analytics flips that script. Instead of passively describing performance, it continuously detects intent, risk, and friction as interactions unfold—then powers targeted responses. For example, it can route high-risk transactions to specialized agents, prompt dynamic guidance during an escalated call, or trigger a proactive message that deflects a repeat contact. In practice, that means fewer transfers, faster resolution speed, and measurable reductions in operating costs.

For finserv specifically, the payoff is immediate. Agent Analytics surfaces coaching opportunities (e.g., where scripts stall or compliance steps get missed). Retention analysis identifies members at churn risk after a negative experience. Journey analytics exposes where authentication fails or balance inquiries overwhelm queues, so you can intelligently deflect to self-service. And when a potential fraud signal appears mid-session, real-time insights can prioritize routing and alerting without sacrificing compliance.

Implementation should be iterative and outcomes-driven. Start by instrumenting the top five journeys that drive the most cost or dissatisfaction (lost card, fraud dispute, loan status, password reset, payment issue). Tie each to clear outcomes vs output OKRs—think first-contact resolution, repeat-contact reduction, containment rate, and average time-to-resolution—so every analytic signal earns its keep. Then activate insights inside the workflow: agent assist prompts, smart routing, and targeted follow-ups that close the loop.

Governance matters just as much as speed. In a regulated environment, privacy-by-design and data governance are non-negotiable. Build data access controls, audit trails, and consent management into your operating model from day one. Align analytics with regulatory compliance requirements to ensure that what you measure and automate is defensible, explainable, and safe for members and the business.

To accelerate learning, pair digital analytics with controlled experiments. Use A/B testing on IVR flows, authentication steps, and post-call follow-ups to quantify what truly reduces transfers and repeat contacts. Define a minimum detectable effect (MDE) upfront so tests are fast and conclusive. Run continuous discovery with cross-functional product trios (operations, data, compliance) to turn insights into shippable improvements every sprint.

On the stack side, focus on connecting systems you already trust. CRM integration ensures that context follows the member, while tools like Amplitude analytics, Pendo, or Intercom can instrument key digital touchpoints. Whether you choose build vs buy, the principle is the same: consolidate signals into a unified analytics platform, then push decisions and guidance back into the tools agents and members already use.

The cultural shift is from reporting to decisioning. Instead of celebrating more charts, celebrate faster resolutions and fewer escalations. Replace static executive reports with alerting and action playbooks. Make it trivial for supervisors to see what changed, why it mattered, and which play to run next. That’s how you convert data into durable operating advantage.

The mandate is clear: stop drowning in dashboards. Move to digital analytics that captures behavior in real time, respects compliance, and powers operational decisions where they matter most—in the member journey. When you do, cost curves flatten, resolution speed climbs, and member trust compounds.

Inspired by this post on Amplitude – Perspectives.

January 23, 2026
AI-Powered Growth Loops: Transform Your PLG Product into a Self-Optimizing Engine

Across my teams and portfolio, I’m watching AI fundamentally reshape product-led growth—from static funnels and one-off playbooks to adaptive, compounding growth loops that learn in real time. The shift isn’t just technological; it’s an operating model change that rewards continuous discovery, rigorous instrumentation, and outcome-driven product strategy.

"Learn how AI is transforming PLG with a new generation of growth loops that can turn your product into a self-optimizing platform." That line captures what I’ve been building toward: systems that sense user intent, decide the next best action, act contextually, and learn to improve the loop with every interaction.

Here’s the core pattern I rely on. First, sense: unify product analytics and behavioral signals (think Amplitude analytics, Pendo events, Intercom conversations) into a single, queryable, privacy-safe layer. Second, decide: apply AI Strategy—LLMs for product managers, rules, and retrieval—to segment users by intent and probability of success. Third, act: deliver in-app guides, product tours, tooltips, or personalized nudges that accelerate user activation and time-to-value. Finally, learn: run A/B testing with a clear minimum detectable effect (MDE), then feed outcomes back into the model for continuous optimization.

Activation is where the gains start compounding. With gen ai, I can auto-generate tailored onboarding checklists, dynamic walkthroughs, and contextual help that adapts to the user’s role, data maturity, and current friction points. We’ve moved from generic product tours to precision guidance that updates based on real-time behavior—often lifting first-week activation and shortening time-to-first-value without adding support load.

Experimentation is the governor that keeps speed and quality in balance. I instrument every growth loop end to end and pair eval-driven development with A/B testing to confirm incremental impact. Amplitude analytics gives me cohort views and path analysis; Pendo or Intercom can deliver in-app variants; a unified analytics platform closes the loop on retention analysis so I’m not optimizing for click-through at the expense of long-term value.

Retention and expansion are where AI shines as a compounding engine. Retrieval-first pipeline patterns allow instant, contextual support that deflects tickets and boosts perceived product competence. Agentic AI can orchestrate next-best actions—prompting power users toward advanced features, surfacing value moments, or timing expansion prompts when success signals appear. The result is a virtuous cycle: better guidance drives deeper adoption, which improves model accuracy, which unlocks more relevant guidance.

None of this works without guardrails. I bake in AI risk management from the start: strict data governance, privacy-by-design, human-in-the-loop review for high-impact actions, transparent user consent, and continuous drift monitoring. The goal is reliable automation that users trust—augmented by clear fail-safes when confidence drops.

Operationally, I anchor the work in empowered product teams and product trios, focus on outcomes vs output OKRs, and practice continuous discovery to validate problems and solutions before scaling. The baseline metrics I watch: activation rate, time-to-value, week-four retention, PQL/PQA conversion, expansion revenue, and support deflection—each tied to a specific growth loop hypothesis.

If you’re starting fresh, begin with the highest-leverage loop: user activation. Instrument your onboarding journey, define the critical path to value, ship two to three personalized interventions, and measure impact with a precommitted MDE. Scale what wins, drop what doesn’t, and iterate weekly. Once activation is compounding, extend the same approach to adoption depth, collaboration features, and expansion triggers.

In practical terms, AI-powered PLG is less about flashy features and more about disciplined feedback loops. Build the sensing fabric, keep the decision layer auditable, ship small actions quickly, and treat learning as the product. Do that, and your product doesn’t just grow—it becomes a self-optimizing platform.

Inspired by this post on Product School.

January 21, 2026
How I Harness AI to Supercharge Product Discovery for Faster Research, Prototyping, and Validation

I’ve led product teams through countless discovery cycles, and nothing has accelerated our learning loops like AI. By weaving AI into our continuous discovery practice at HighLevel, I cut time-to-insight, reduce risk earlier, and keep our product strategy relentlessly focused on customer outcomes.

AI streamlines product discovery by accelerating research, prototyping, and validation, enabling teams to make faster, smarter, and user-driven decisions.

In the research phase, I use gen ai and LLMs for product managers to synthesize interviews, cluster themes, and surface unmet needs in minutes instead of days. Pairing those qualitative insights with behavioral signals in Amplitude analytics helps me spot high-intent cohorts and friction points at scale, so our problem framing is both human-centered and data-backed.

From there, I translate insights into crisp hypotheses and prioritize with the Kano Model and outcomes vs output OKRs. To keep experiments honest, I define a minimum detectable effect (MDE) up front and design A/B testing plans that reflect realistic traffic and seasonality, ensuring our decisions are statistically grounded rather than anecdotal.

Prototyping is where gen ai for product prototyping really shines. I spin up multiple UX flows, UI copy variants, and edge-case scenarios using prompt engineering, then iterate with rapid feedback from product trios. When needed, I mock in-app guides and product tours to validate onboarding concepts before we commit to code, preserving velocity without sacrificing quality.

For validation, I lean on a mix of lightweight experiments—fake-door tests, concierge pilots, and targeted A/B testing—augmented by in-product surveys via Pendo or Intercom. For AI-powered features, I apply eval-driven development to measure relevance, latency, and safety, so we can ship responsibly while maintaining the pace of learning.

This approach only works when the team is structured to move fast. Empowered product teams and product trios own discovery end-to-end, with clear guardrails around data governance, privacy-by-design, and AI risk management. That alignment lets us shift from opinions to evidence, and from output to outcomes, without friction.

If you’re getting started, pick one discovery loop to transform: automate research synthesis, prototype two to three variants with AI, and validate with a tightly scoped experiment. Instrument your analytics, track time-to-insight and time-to-prototype, and iterate your product roadmapping and sprint planning with what you learn. The payoff is immediate: faster cycles, stronger conviction, and a more user-driven path to product-led growth.

Inspired by this post on Product School.

January 19, 2026
Turn Every Support Ticket into Product Truth: My Playbook for Data-Driven CX Wins

Support tickets are the rawest signal of product truth. Leading product teams at HighLevel, I’ve learned that the fastest way to build what customers value is to transform frontline conversations into a repeatable, data-driven system for discovery, prioritization, and execution.

What if your support and product teams could unlock CX insights to turn every ticket into strategic product intelligence? Explore how.

Here’s the operating system I rely on. First, I connect our support stack (think Intercom and our CRM integration) into a unified analytics platform so every conversation, tag, and resolution is queryable. I don’t just count tickets—I segment them by product area, customer segment, lifecycle stage, and revenue impact to reveal patterns that roadmaps can act on.

Next, we standardize a shared taxonomy. Agents apply concise, high-signal labels (problem type, severity, intent), and we augment that with AI-driven auto-tagging to reduce noise and improve recall. The result is trustworthy “voice of the customer” data that product managers and support leaders can both stand behind.

Prioritization then becomes rigorous and fair. I weight themes by severity, frequency, ARR exposure, and time-to-value, and tie them directly to outcomes vs output OKRs. Amplitude analytics helps me quantify impact—what’s breaking activation, what’s dragging conversion, what drives retention analysis—so the backlog reflects business outcomes, not opinions.

Discovery is continuous by design. Product trios (PM, design, engineering) run weekly reviews of the highest-signal themes, recruit users straight from recent tickets, and prototype solutions quickly. We validate ideas with A/B testing when appropriate and ship targeted in-app guides to reduce confusion before it becomes a ticket.

Crucially, we close the loop. When we release a fix or improvement, we notify affected customers and the agents who flagged the issue. We track downstream effects—ticket deflection, CSAT, feature adoption, and time-to-resolution—so everyone sees how customer support ai strategy accelerates product-led growth.

This approach also builds culture. Empowered product teams treat support as a strategic partner, not a cost center. Agents become co-creators of the roadmap, and PMs gain a steady stream of product discovery opportunities grounded in real user outcomes.

If you’re getting started, a simple 30-60-90 can help: in 30 days, unify the data and agree on taxonomy; in 60, instrument dashboards and adopt a weekly insights ritual; in 90, align priorities to OKRs, launch targeted fixes, and measure business impact. That’s how tickets turn into product truth—and how CX insights drive compounding wins.

Inspired by this post on Amplitude – Perspectives.

January 8, 2026
PMs and Developers Need Different AI Metrics—Here’s How That Builds Faster, Better Products

I’ve sat in countless AI measurement debates and noticed a recurring gap. One major voice has been noticeably underrepresented in the AI measurement conversation: the product manager (PM) that’s leading development. From experience, PMs and developers do need different measurement tools—and making those differences explicit is exactly what speeds up decisions and improves outcomes.

Developers optimize the model and system layer. Their toolkit centers on eval-driven development: offline evals, regression suites, red-teaming, latency and throughput monitoring, token cost tracking, and hallucination rate reduction. On the delivery side, engineering teams watch DORA metrics alongside CI/CD performance to keep iteration fast and safe. When building LLM-backed experiences, they also care deeply about retrieval-first pipeline quality and context window management because those mechanics determine grounding, relevance, and consistency.

PMs, by contrast, own outcomes. We instrument user journeys end to end and define a clear north-star tied to value: activation, time-to-value, task success rate, retention analysis, support deflection, and revenue contribution. We rely on A/B testing frameworks and minimum detectable effect (MDE) planning to separate real impact from noise, and we consolidate behavioral signals in a unified analytics platform like Amplitude analytics and Pendo to understand adoption, friction, and cohort differences. This is the heart of product-led growth and continuous discovery: evidence, not anecdotes.

The fact that these toolboxes differ is a strength, not a weakness. Specialized metrics keep responsibilities crisp: developers guarantee model quality and reliability; PMs guarantee that quality translates into customer and business outcomes. What we need is an explicit metrics ladder that connects layers—model-level quality floors and SLOs, feature-level KPIs, and company-level results—so trade-offs are transparent and prioritization is principled.

In practice, I create a shared measurement contract for every AI initiative. It links eval sets to user-facing success criteria, defines acceptance thresholds, and spells out observability across the stack. We include governance from day one—AI risk management, privacy-by-design, and data governance—so we can scale responsibly without slowing teams down.

Here’s the AI product toolbox I give my teams: start with a concise value hypothesis; define a success rubric the customer would recognize; instrument the happy path and the failure path; plan experiments with MDE up front; segment results by persona and job-to-be-done; and close the loop with qualitative feedback inside the product via in-app guides, product tours, and lightweight surveys. For AI features specifically, add Agent Analytics for agentic AI, capture grounding sources for explainability, and log model/context inputs to make debugging and iteration repeatable. That way, LLMs for product managers stop being magic and start being manageable.

When we roll out a new assistant—whether a retrieval-augmented copilot or a voice AI agent—we set two dashboards: one for developers (eval pass rates, latency, context integrity, error budgets) and one for PMs (activation, task completion, deflection, satisfaction). The dashboards read differently by design, yet they are joined at the hip by shared definitions and experiment IDs. This lets us move quickly with confidence: engineering can tighten quality loops while product steers toward the outcome that matters most.

If you’re feeling the tension between model metrics and product metrics, don’t collapse them—connect them. Start with a thin slice, agree on 3–5 measurable outcomes, and let your evals and A/B tests work together. With a clear metrics ladder and a unified analytics platform, PMs and developers can each excel at their craft and still ship AI that customers love.

Inspired by this post on Pendo – Perspectives.

January 7, 2026