I see the rise of Customer Forward Deployed Engineering (FDE) as a pivotal bridge between FinOps engineering, AI strategy, and measurable customer outcomes. When we align internal platforms and agentic AI with real-world use cases, we don’t just reduce cloud costs—we accelerate adoption, de-risk deployments, and create durable product value that compounds over time.
"Hac Phan leads FinOps engineering at Amplitude, where he builds internal platforms and AI agents that help teams understand and optimize cloud spend. He now heads Amplitude's Customer Forward Deployed Engineering team." That evolution—from building internal capabilities to leading a customer-facing FDE function—captures a pattern I’ve seen repeatedly: the skills that tame complexity inside the company are exactly the skills customers need most at the edge.
In my experience, Customer FDEs thrive when they embed with strategic accounts to translate product capabilities into concrete outcomes: lower unit economics, faster time-to-value, and cleaner governance. They partner closely with solutions engineering, product management, and customer success, using platform building blocks and AI workflows to illuminate the cost drivers that matter—then engineering the shortest path to savings and scale.
The operating model is straightforward but disciplined. Set a clear mission (optimize cost-to-value while expanding usage), define a small set of leading indicators (time-to-first-value, cost per active workload, deployment frequency, NRR lift on FDE-supported accounts), and establish crisp handoffs with core product teams. When FDEs surface repeatable patterns, those insights should flow back into the roadmap as native features, guardrails, and in-product guidance—so every customer benefits, not just the lighthouse few.
Tooling matters. Internal platforms that unify telemetry, usage metering, and pricing logic give FDEs the levers to diagnose and fix issues quickly. Layering AI agents on top of that foundation enables proactive recommendations—think unit-economics dashboards, anomaly detection on spend spikes, and automated playbooks that right-size workloads. With agent analytics in place, we can measure the value of each recommendation and continuously tune the system.
I’ve seen this model turn tense, cost-focused conversations into strategic planning sessions. Instead of debating line items, we co-design architectures that scale efficiently, with platform scalability and governance built in from the start. Customers appreciate the candor and the engineering rigor; teams appreciate how those field insights sharpen product strategy.
For leaders considering this path, start small and design for leverage. Stand up a single FDE pod focused on 2–3 high-potential customers. Codify playbooks for cloud cost optimization, instrument agent analytics from day one, and publish a weekly learning loop back to product. Within a quarter, you’ll know which interventions to automate, which to turn into product features, and which require deeper solutions engineering support.
The broader lesson is simple: when we merge FinOps discipline with customer-embedded engineering and AI-driven insights, we create a force multiplier. Customer FDEs don’t just help accounts spend less; they help them achieve more—sustainably, transparently, and with the confidence that comes from a platform (and a team) built to scale.
Inspired by this post on Amplitude – Perspectives.
I lead Growth & AI at Amplitude, where I focus on viral and core growth strategies, user acquisition, and product engagement. My north star is to architect durable growth loops that compound over time while elevating the customer experience—from the first onboarding moment to deep, habitual use.
Day to day, I combine Amplitude analytics and behavioral analytics to power product-led growth. By instrumenting the right events, mapping activation journeys, and running disciplined A/B testing, I drive user activation and accelerate time-to-value. That work extends into onboarding, in-app guides, and retention analysis, ensuring we optimize not just for acquisition but also for sustainable engagement and expansion.
On the AI front, I define and execute the AI Strategy that responsibly applies gen ai and LLMs for product managers to increase experimentation velocity and personalize experiences at scale. This includes deploying intelligent nudges, next-best actions, and adaptive UX while honoring privacy-by-design and strong data governance practices. The outcome is a feedback-rich system that learns from user behavior and continuously improves product-market fit signals.
My playbook is simple but rigorous: align on a clear North Star, translate it into activation and retention metrics, size lift using minimum detectable effect (MDE), and iterate fast with product trios. I use an opportunity solution tree to prioritize bets, validate with continuous discovery, and then harden winning patterns into repeatable growth loops. This approach keeps teams focused on outcomes, not output, and creates a shared language across product, design, data, and engineering.
If you’re exploring how to scale product-led growth with AI, this is the path I follow: turn rich product analytics into actionable insights, test with scientific precision, and ship experiences that feel personal, timely, and trustworthy. The result is a growth engine that compounds—driving efficient acquisition, stronger activation, and enduring product engagement.
Inspired by this post on Amplitude – Best Practices.
Data has always been my compass for building products that customers love and businesses depend on. Few sentences distill that imperative as crisply as the one below—and it continues to inform how I prioritize, experiment, and scale outcomes across the roadmap.
Krista is a digital analytics leader, product strategist, and industry evangelist. She helps businesses use data to drive growth, retention, and monetization.
That mandate mirrors how I run product: leverage behavioral analytics to uncover patterns, translate those insights into hypotheses, and validate them through rigorous A/B testing. I start by instrumenting the user journey end to end, then use cohort analysis, funnel diagnostics, and retention analysis to pinpoint where activation, engagement, or monetization is stalling. From there, I map driver trees to connect inputs (feature adoption, time-to-value, onboarding friction) to outputs (retention, conversion, revenue), so every experiment has a clear line of sight to business impact.
On experimentation, I hold the bar high: define the minimum detectable effect (MDE) up front, ensure clean experiment design, and size samples to reduce noise. I combine Amplitude analytics with qualitative signals from continuous discovery to prioritize tests that move the needle, not just the vanity metrics. When a variant wins, I don’t stop at the lift—I track downstream effects on user activation, long-term retention, and monetization, ensuring we’re compounding gains rather than optimizing in silos.
For product-led growth, I focus on the moments that matter most: first-value, aha, and habit formation. Journey mapping helps me identify the shortest, clearest path to value, while targeted in-app experiences and contextual nudges accelerate activation without adding friction. Every iteration feeds a learning loop—measure, learn, and ship—so we can pursue step-change outcomes, not incremental tweaks.
Ultimately, the craft is in translating analytics into action. When teams can trace a feature idea to a specific behavioral pattern, test it with a well-powered A/B experiment, and observe durable improvements in retention and revenue, momentum takes care of itself. That’s how I operationalize data to deliver growth, retention, and monetization at scale.
Inspired by this post on Amplitude – Best Practices.
By the end of 2024, we were already all-in on Fin, and our customer support organization was deep in its own transformation. Resolution rates were strong, efficiency was improving, and for the first time, something new was emerging: capacity.
That newfound capacity wasn’t just a relief; it was a strategic opening. As we became less reactive day to day, I saw how support’s unique vantage point—rooted in customer needs and aligned with company goals—could evolve into a consultative function that actively drives value for customers and the business.
This is the story of how we built consultative support. I’ll walk you through how we got started, the results we achieved, and the lessons I’d carry forward if I were doing it again from scratch.
We didn’t begin from zero. A few years earlier, we partnered closely with research and data science to drive product adoption. In a project we called “next best step,” we tested offering proactive guidance inside already-established conversations. It worked well, and as Fin accelerated how we worked, we realized we were ready to push into broader, more ambitious opportunities.
Instead of dictating a solution from the top, I opened the floor. We hosted a support town hall and asked the team to share concrete ways support could directly drive company outcomes. The conversation was electric—practical, creative, and grounded in real customer moments.
Right there, we spun up campaign concepts. One idea was an always-on in-product banner offering a call with a member of our team to help customers set Fin up to the best of its ability. Another was the “Fin upsell campaign,” where, once a customer had a positive interaction with Fin and clicked the “that helped” button, a tailored message would share details about our own success with Fin and invite the customer to book a call to learn more and ask questions.
The energy from that session made one thing obvious: the team already knew how to help customers extract more value from the product. They just needed focus, permission, and a clear path to act.
We started small on purpose. I recruited a group of volunteers who dedicated part of their week to exploring new, proactive ways to support customers. We kept the group tight for two reasons: first, even with Fin freeing up significant capacity, we still had to deliver excellent day-to-day support; second, this was an experiment, and we weren’t going to overhaul a 100+ person organization without proof.
One of our first campaigns focused on proactive engagement with self-serve customers—those without a dedicated sales or success touchpoint. Our goal was to give this group direct access to teammates with first-hand experience in AI transformation and help them see the value they could get from Fin.
Early use cases included guiding customers through Fin trials, working with mature customers on optimization to get more out of Fin, and proactively identifying high-potential accounts that looked ready for Fin. None of this required a new team or a big budget—just attention and intention.
To make consultative support stick, we trained for a mindset shift. I encouraged the team to move beyond solving the immediate issue and instead probe deeper to understand each customer’s unique context. We leaned on our sales and success peers to refine our outreach—learning how to time our messages, frame value succinctly, and meet customers at the right moment rather than waiting for them to come to us.
To validate our approach, we needed data—not vibes. We built a simple but rigorous comparison: accounts we engaged with versus accounts we reached out to but didn’t hear back from. Over a six month period, we tracked feature adoption, Fin usage, and expansion revenue across both groups.
The result was clear: engaged accounts grew roughly twice as fast in both usage and expansion.
To further prove the value of proactive support, we also tracked direct Fin resolutions generated after consultative interactions, resolution and automation rate improvements across engaged accounts, and influenced expansion ARR across everything we worked on over the year.
Seeing those numbers was a turning point. This wasn’t a side project anymore—it was a repeatable motion with measurable business impact.
As results became visible, partnerships multiplied. Self-serve engineering teams saw the value of well-timed human touchpoints. Customer lifecycle marketing tapped us to handle responses to their campaigns. Product teams began partnering with us to identify high-impact engagement opportunities. We also deepened our collaboration with digital, scale, and high-touch success teams—stepping in where they lacked capacity and offering deep technical guidance to help customers get the best from the platform.
What began as simple outreach matured into targeted, strategic initiatives tied directly to company goals.
Within a year, our volunteer crew grew to ~16 teammates across regions—curious, motivated, and eager to try new things. We continued expanding the consultative support function and took on new projects end to end. Most recently, we assumed ownership of the new “sales assist” team to drive self-serve trial conversions and help new customers get the most from their first experience.
Here are the practices that mattered most in making consultative support real and durable:
Start with your team, not a strategy doc. The best ideas came from the people closest to customers. That town hall shaped our initial direction more than any top-down plan could have.
Don’t scale before you’ve proved it. A small, motivated group moved faster, learned quicker, and produced clearer results than a broad rollout. When you need organizational buy-in, a rigorous proof point beats a promising concept.
Train for a different mindset. Consultative work requires curiosity, commercial awareness, and the ability to hold broader context—not just product knowledge. Invest deliberately in coaching and frameworks that strengthen these muscles.
Measure against a control group. Without a control, you have a story. With it, you have a business case—and that’s what unlocks resources, headcount, and prioritization.
Lean into being different. It’s helpful to take cues from sales and success, but you don’t have to operate exactly like them. There’s real power in support’s distinct perspective and tone.
Building this consultative support function fundamentally changed how we think about our remit. Support is no longer just there to respond; it now drives adoption, influences retention, generates expansion revenue, and, for many self-serve customers, serves as the primary human touchpoint.
In an AI-first world, where Fin handles all of the transactional work, this kind of work becomes even more important. Because the question for support leaders is no longer “how do we handle more tickets?” but rather, “how do we use support to grow the business?”
I’m excited to share a resource I recommend to every product and growth team I mentor: the Amplitude Quickstart Series. It’s a concise, approachable way to build confidence in “Amplitude analytics” and turn behavioral data into decisions that actually move the needle.
Discover user-friendly videos that walk you through Amplitude’s most essential products and features.
In my role leading product teams, I’ve seen how a clear, opinionated path through a “unified analytics platform” reduces time-to-insight from weeks to days. The Quickstart format makes it easy for product managers, analysts, and marketers to align on a common language for “behavioral analytics,” so we spend less time debating definitions and more time shipping value.
What I appreciate most is how quickly these lessons translate into outcomes: crisper instrumentation practices, cleaner dashboards, and sharper questions that drive “product-led growth.” That foundation accelerates “user activation,” improves “retention analysis,” and ultimately leads to better prioritization and stronger roadmap bets.
My recommended workflow: watch the entire series once to map the mental model, then revisit each segment as you operationalize it. Pair the guidance with a lightweight tracking plan, establish clear event naming conventions, and document your first key use cases (e.g., activation funnel, onboarding drop-off, core feature adoption). This cadence helps teams institutionalize good habits without over-engineering.
For cross-functional leaders, the series is also a powerful alignment tool. Ask product, data, design, and customer success to watch the same modules, then run a joint working session to define success metrics and accountability. When everyone sees the same “north-star” dashboards, decision-making speeds up and the quality of trade-offs improves.
As your practice matures, amplify the impact by pairing insights with action: connect findings to experiments, “feature flags,” and iterative product tours; complement quantitative patterns with “session replay” for richer context. This closed-loop approach helps you move from reporting to repeatable, insight-to-execution cycles.
If you’re new to Amplitude or scaling a growing practice, this Quickstart Series is the shortest path I know from curiosity to competence. Watch it, implement one improvement per week, and share progress broadly—momentum compounds.
Inspired by this post on Amplitude – Best Practices.
Churn isn’t just a retention problem—it’s a product, go-to-market, and strategy signal that shows up everywhere in the customer journey. Over the past few years, I’ve evaluated and implemented churn prediction tools across high-growth SaaS environments, and the difference between reactive firefighting and proactive, data-driven retention is night and day.
Compare the top 8 churn prediction tools for SaaS teams. Features, use cases, and how each stacks up, so you can act before customers quietly leave.
When I assess churn prediction tools for product-led growth, I start with a simple question: will this help my team see risk early enough—and clearly enough—to intervene with precision? The best platforms combine behavioral analytics, retention analysis, and anomaly detection to surface leading indicators before Net Recurring Revenue (NRR) takes a hit.
First, signal coverage matters. Strong churn models draw from product usage events, CRM integration, support tickets, billing health, and even session replay to capture real-world behavior. I look for native connectors to systems like Intercom, Pendo, and Amplitude analytics, plus flexible ingestion for custom events. Without comprehensive signals, even the smartest models will miss critical moments such as stalled onboarding, shrinking active seats, or feature disengagement.
Second, I require transparent risk scoring and clear drivers. Black-box scores erode trust with Customer Success and Product teams; explainability builds alignment. Tools that expose driver trees, cohort-based retention analysis, and segment lift help me translate insights into prioritized experiments. When possible, I tie predicted churn segments to A/B testing with a thoughtful minimum detectable effect (MDE) so we can quantify impact quickly and avoid overfitting to noise.
Third, actionability is non-negotiable. Predictions must trigger targeted AI workflows, in-app guides, and product tours—not just dashboards. My ideal setup routes high-risk cohorts to tailored journeys (e.g., an onboarding rescue path) while notifying the right owner in CRM and Customer Success. Playbooks should be easy to operationalize, measurable, and reversible if the signals change.
Fourth, I evaluate platform scalability, data governance, and privacy-by-design. Enterprise readiness means clear role-based access, auditability, robust SLAs, and an architecture that can evolve into a unified analytics platform as the product and data footprint grows. I also weigh total cost of ownership, implementation time, and maintenance burden against expected gains in NRR and expansion.
In my experience, the winning tools are the ones that make it simple to connect predictions to outcomes: reduce onboarding drop-off, increase user activation, prevent seat contraction, and accelerate expansion. They align Product, Customer Success, and Growth around shared metrics, shorten time-to-value, and make proactive retention part of the operating rhythm—not a last-ditch effort at renewal.
In this 2026 comparison, I’ll outline how each tool handles data breadth, model quality, explainability, and workflow automation. I’ll also share implementation checklists and decision criteria so you can choose the right fit for your stage, stack, and motion—whether you’re primarily product-led growth, sales-led, or hybrid.
If you’ve ever felt like customers “quietly leave” despite solid top-of-funnel metrics, this guide will help you turn churn signals into concrete actions—and convert at-risk accounts into durable advocates.
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.
Session replay should illuminate user behavior, not slow it down. That belief drove us to rebuild the delivery layer behind our Session Replay from the ground up so it’s lighter on your pages while capturing richer, more reliable signals for behavioral analytics and product insights.
Our objective was clear: preserve page performance and Core Web Vitals while improving data completeness under real-world conditions. We focused on reducing client-side overhead, smoothing network bursts, and scaling the pipeline so it performs consistently during long sessions, high-traffic spikes, and complex interactions—without compromising observability or user experience.
To get there, we redesigned how events flow from the browser to our edge and storage layers. We decoupled capture from delivery, introduced adaptive batching and backpressure-aware controls, tightened compression strategies, and prioritized critical events to reduce jitter and dropped packets. The result is a delivery path that’s resilient to network variance, efficient in payload size, and friendlier to the main thread—key ingredients for platform scalability and SRE-grade reliability.
Get a glimpse into how we overhauled Session Replay’s data delivery, and how you can expect more complete data, lower payload sizes, and more. In practice, that means steadier capture across long sessions, fewer gaps during rapid DOM changes, and leaner, faster uploads that respect the constraints of modern browsers and mobile networks. It’s an upgrade designed to protect page speed while strengthening the fidelity of what you see in replay.
These changes elevate how product teams, analysts, and support engineers diagnose issues and optimize funnels. With higher-fidelity replay and lighter page impact, you can connect the dots faster—from anomaly detection and conversion bottlenecks to subtle UX friction—within a unified analytics platform. It’s a meaningful step forward for data-driven product strategy and for keeping your observability toolkit both accurate and performance-aware.
While performance guided every decision, privacy and governance stayed first-class. Our delivery patterns work hand-in-hand with data governance practices to help teams maintain responsible capture boundaries while still achieving the completeness and granularity they need. This balance lets you scale replay confidently across surfaces and teams.
We’ll continue monitoring downstream impact across Web Vitals, long tasks, error rates, and event integrity—iterating as we learn. If you rely on session replay to inform roadmaps, triage incidents, or accelerate product-led growth, you should feel the difference: a lighter footprint on your page and a stronger foundation for trustworthy insights.
Inspired by this post on Amplitude – Best Practices.
I build products to translate noisy interaction data into clear, actionable decisions. Few capabilities deliver that clarity like session replay. It closes the gap between what analytics tells us and what users actually experience, empowering product, design, and SRE teams to learn faster, resolve issues sooner, and improve customer trust.
Lew Gordon is a Senior Staff Engineer at Amplitude focusing on Session Replay. He was formerly an engineer at Twilio.
In my practice, session replay complements Amplitude analytics and behavioral analytics by adding rich context to the unified analytics platform—turning charts into stories we can act on. When I can see the precise clicks, hesitations, and error states behind a spike or a drop, prioritization becomes straightforward and the path to product-market fit becomes easier to navigate.
Operationally, replay deepens observability. I correlate console errors, network traces, and layout shifts with user intent, then tie those signals to Web Vitals, performance budgets, and SRE workflows. The result is a tighter feedback loop from incident to insight—one that shortens mean time to resolution and raises the bar on reliability without guesswork.
Privacy-by-design is non-negotiable. I start with strong data governance: selective capture and redaction, explicit consent and retention policies, role-based access, and environment-aware sampling. These controls keep sensitive data protected while still providing the fidelity product and engineering need to diagnose issues and improve experiences responsibly.
Strategically, I deploy replay where it moves the needle most: onboarding and activation moments, high-friction conversion flows, and critical paths with outsized revenue or trust impact. I track signals like rage clicks, dead clicks, scroll depth, and error states to inform product strategy and reduce UX debt, while linking improvements to activation and retention analysis, time to resolution, and DORA metrics.
At scale, success requires platform scalability: efficient indexing, low-latency retrieval, and smooth playback across browsers and devices—all while maintaining tight CPU, memory, and bandwidth budgets. When integrated with CI/CD and experimentation, replay becomes a force multiplier for continuous discovery and confident, rapid iteration.
My takeaway: session replay is not just a debugging tool—it’s a shared language across product, engineering, and design. With the right guardrails and operating model, it elevates decision quality, accelerates learning, and builds the trust customers feel with every interaction.
Inspired by this post on Amplitude – Best Practices.
I just finished listening to "Taste – All Things Product Podcast with Teresa Torres & Petra Wille," and as a product leader shipping AI-powered capabilities at HighLevel, Inc., I wanted to pressure-test the sudden obsession with "taste."
If you're curious, you can listen to this episode on Spotify or Apple Podcasts.
The core question landed perfectly for our moment: Is "taste" the must-have skill of the AI era — or just the latest tech buzzword in a world where AI is eating through design, delivery, and discovery?
Teresa pushes back hard, highlighting how slippery the term can be. "It's just this month's flavor of founder mode." She points out that "taste" is rarely defined, can't be easily taught, and too often becomes shorthand for "my preference trumps yours." Just as importantly, "It's not about your taste. It's about your customer's taste."
Petra adds needed nuance from years in the craft: pattern-recognition is real, and some people do develop sharper product sense over time. As she put it, "I am a strong believer that you develop product sense and taste over time. It's never finished."
Both threads lead back to familiar roots in product: product sense, founder mode, and the enduring myth of the lone visionary. They even grapple with the big question on everyone’s mind—Will AI Eat Taste Too?—and where that leaves product teams navigating GenAI, LLMs for product managers, and evolving product strategy.
Here’s my take. "Taste" can be useful as a personal north star, but it is not a decision system. In my teams, we bias toward evidence: continuous discovery, customer interviews, discovery synthesis with opportunity solution trees, and tight collaboration in product trios. Opinion can start the conversation, but evidence should end it.
Practically, that means investing in the skills that compound: Discovery skills — understanding customers, matching solutions to real needs. Human-to-human interaction skills. Learning to collaborate with AI effectively. Critical thinking and judgment grounded in evidence.
On AI collaboration specifically, we treat GenAI as a force multiplier, not a decider. We prototype with AI to explore breadth, then narrow with qualitative and quantitative signals, ablation-style experiments, and clear success criteria. The bar I hold myself to is simple: taste without evidence is just opinion.
Three lines I underlined from the conversation:
"It's just this month's flavor of founder mode." — Teresa Torres
"It's not about your taste. It's about your customer's taste." — Teresa Torres
"I am a strong believer that you develop product sense and taste over time. It's never finished." — Petra Wille
If you want to go deeper, these references are helpful for sharpening judgment without falling into the "great man" theory trap.
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Founder mode
Marty Cagan: Founder-Style Leadership
Vercel/v0 CEO Guillermo Rauch on building taste: from Lenny Rachitsky’s Linkedin post
Continuous discovery (Read Teresa’s Everyone Can Do Continuous Discovery—Even You! Here’s How
The "great man" theory
Steve Jobs and the myth of the lone product visionary
Have thoughts on this episode? Leave a comment below and share how your team balances product sense with evidence in the age of AI.
Weekly product reviews are where strategy meets execution, and over the past year I’ve turned them into a high-signal, low-friction ritual by leaning on agentic AI. As VP of Product Management at HighLevel, Inc., I’ve standardized a set of agent skills that compress preparation time, surface the right insights, and keep PMs, engineers, and designers focused on decisions—not document wrangling.
"Learn how our teams use agent skills with claude, cursor and codex to run product reviews as PMs, engineers, and designers. Here are 5 killer use cases for builder."
Below, I walk through the five skills I rely on most in our weekly cadence—each one mapped to a clear product management outcome. They’re simple to set up, easy to govern, and aligned with core practices like continuous discovery, product roadmapping and sprint planning, and eval-driven development.
Skill 1 — Backlog triage with signal extraction: I point an agent at fresh tickets, customer notes, and experiment results to cluster themes, tag impact, and flag regressions. Using a retrieval-first pipeline and Agent Analytics, the assistant ranks items by value, effort, and risk so our meeting starts with a prioritized, explainable shortlist instead of a raw queue.
Skill 2 — PRD and spec synthesizer: Ahead of the review, an agent drafts a one-page PRD update from design diffs, git history, and decision logs. With Claude Code and Cursor, it highlights interface changes, acceptance criteria, and open questions, linking back to sources. The result is a crisp, auditable brief that keeps product trios aligned without re-litigating context.
Skill 3 — Experiment and metrics analyzer: An analytics agent pulls A/B testing readouts, checks minimum detectable effect assumptions, and annotates anomalies. It turns raw telemetry into a narrative: what moved, by how much, and whether we trust it. This makes our discussion about tradeoffs, not spreadsheets, and speeds commitments on next steps.
Skill 4 — Voice-of-customer synthesizer: The assistant clusters interviews, support threads, and NPS verbatims into jobs-to-be-done and pain themes. It proposes opportunity solution tree updates and calls out places where our roadmap diverges from customer signal. That keeps continuous discovery alive in the room—even when time is tight.
Skill 5 — Roadmap and sprint planning co-pilot: After decisions, an agent converts outcomes into scoped backlog items, engineering tasks, and stakeholder updates. It drafts sprint goals, flags dependency risks, and aligns work to objectives. Because it’s grounded in the meeting record, it preserves intent while removing ambiguity.
Under the hood, prompt engineering patterns and guardrails keep these workflows predictable: a retrieval-first pipeline for context, eval-driven development for quality checks, and role-specific prompts for PMs, engineers, and designers. With Claude Code I generate structured diffs and test scaffolds; with Cursor I accelerate code-review summaries; and with codex I bootstrap utility scripts that keep the loop tight between insights and implementation.
The payoff is tangible: higher decision velocity, fewer meetings to “re-clarify,” and clearer accountability across the product organization. Just as important, governance and privacy-by-design are built in—every agent logs rationale, cites sources, and respects data boundaries—so leaders can scale AI workflows confidently.
If you’re looking to level up your product reviews, start with these five skills, measure impact with Agent Analytics, and iterate. Small automations compound quickly, and the more consistently you run them, the more your team’s attention shifts from preparing content to making better product decisions.
Inspired by this post on Amplitude – Perspectives.
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.
