Engagement starts with a single, repeatable moment: activation. Over the years, I’ve learned that when we obsess over activation, everything downstream—retention, expansion, and product-led growth—gets easier and more predictable. As I often remind my teams, "Discover how winning teams drive engagement by obsessing over activation. Learn to define, measure, and improve the moments that keep users coming back."
When I say activation, I mean the specific behavior that reliably predicts long-term value for a new user or account. In different products, the activation moment could be connecting a data source, inviting a teammate, sending the first campaign, or completing an initial automation. My first move is to define that moment precisely, set an activation threshold (for example, “within 7 days of signup”), and align the team around it as a primary outcome.
From there, I track three core metrics: activation rate (the percentage of new accounts that hit the activation threshold), time-to-activation (how quickly they get there), and early retention curves by cohort. Cohort-based retention analysis gives me the most honest read on whether our activation definition truly predicts stickiness or if we’re celebrating vanity milestones. Tools like Amplitude analytics and Pendo make it straightforward to instrument these events, segment users, and visualize the funnel from first touch to activation and beyond.
Instrumentation quality is non-negotiable. I map the activation journey into discrete events, add clear event properties (role, plan, channel, use case), and validate tracking end-to-end before I trust any dashboard. A strong unified analytics platform lets me slice activation by persona, acquisition source, and onboarding path, so we can see where friction lives and where momentum builds.
Improving activation is where design and data meet. I lean heavily on in-app guides, product tours, and contextual tooltips to reduce cognitive load at the exact moment a user needs help. We run A/B testing with a minimum detectable effect in mind, prioritize experiments that remove steps or shrink time-to-value, and iterate quickly based on user feedback gathered through continuous discovery. The goal is simple: shorten the distance from curiosity to value.
Onboarding is the frontline of activation. I favor progressive disclosure, crisp checklists tied to the activation moment, and “just-in-time” education rather than dumping documentation up front. Clear wayfinding—what to do next, why it matters, and how success is measured—pushes users toward that first “aha” moment with confidence.
Cross-functionally, I align activation to outcomes vs output OKRs so everyone—from product and design to marketing and customer success—pulls in the same direction. For example, lifecycle emails and in-app messaging should reinforce the same activation path that product guides inside the app. This harmony lowers friction, speeds time-to-activation, and compounds engagement.
As we scale, I keep a living experiment backlog focused on activation levers: simplifying setup, removing form fields, auto-detecting configurations, and pre-populating defaults. Each change gets measured against activation rate and time-to-activation, with guardrail metrics to protect quality and retention. Over multiple releases, these small wins stack into durable growth.
I’ve seen teams unlock double-digit improvements by treating activation as a product, not a project. When we define the right moment, instrument it well, and iteratively remove friction with data-informed design, engagement rises naturally—and sustainably. That’s the power of an activation-obsessed culture.
Inspired by this post on Amplitude – Best Practices.
I’ve spent the last year pressure-testing where marketing analytics is really headed, not just in slide decks but in the messy reality of product roadmaps, stakeholder management, and revenue targets. From my seat leading product teams and partnering closely with CMOs and growth leaders, I see 2026 as the year analytics stops being a rearview mirror and becomes a real-time operating system for growth.
Start 2026 off with a bang with exclusive insights and predictions from some of marketing analytics’ most influential voices. See what they have to say.
Prediction 1: The unified analytics platform becomes non-negotiable. Fragmented dashboards and manual spreadsheet reconciliation will give way to an integrated, privacy-by-design measurement layer that stitches product, marketing, and revenue data. Expect tighter CRM integration (think HubSpot), product analytics (Amplitude analytics, Pendo), and revenue systems in one source of truth. The practical upside: faster decision cycles, cleaner attribution, and a shared language for product-led growth.
Prediction 2: Gen ai and agentic AI move from novelty to necessity. Analysts and product managers will deploy AI Strategy playbooks that pair retrieval-first pipeline patterns with governance to answer open-ended questions and trigger actions safely. “Agent Analytics” will summarize trends, generate experiments, and draft stakeholder updates, while LLMs for product managers become standard tooling. The bar is explainability: every AI-assisted insight must show its lineage and assumptions.
Prediction 3: Experiments scale, rigor deepens. We’ll treat A/B testing as a system, not an event—standardizing guardrails like minimum detectable effect (MDE), pre-registration, and sequential testing where appropriate. As teams embrace continuous discovery, we’ll graduate from single-page tests to multi-surface learning agendas spanning pricing, onboarding, and lifecycle activation. The goal isn’t more tests; it’s faster time-to-learning with lower decision risk.
Prediction 4: Causality beats correlation in measurement. Last-click and naive attribution will yield to incrementality testing, holdouts, and lightweight MMM for channels that don’t click. Retention analysis gains prominence as the north star for sustainable growth, linking value proposition clarity to user activation and downstream LTV. Outcomes vs output OKRs will force teams to track what truly moves customer behavior.
Prediction 5: Activation loops go real-time. Unified analytics will trigger in-product nudges, product tours, and contextual in-app guides the moment a signal crosses a threshold. This closes the loop between insight and action, shrinking the distance from analysis to impact. Teams that instrument these loops well will win on speed and compounding effects.
Prediction 6: Governance becomes a growth enabler. Data governance and privacy-by-design aren’t just compliance—they’re a competitive advantage. Clear definitions, consent-aware pipelines, and transparent AI risk management will increase trust in insights, accelerate deployment, and reduce rework. When stakeholders trust the data, they make bolder, faster decisions.
Prediction 7: Go-to-market precision improves. With cleaner signal and shared context, we’ll price with confidence (SaaS pricing and, in many cases, consumption SaaS pricing), sharpen product positioning, and focus spend where incrementality is provable. Expect fewer vanity metrics, more revenue-linked scorecards, and tighter integration between product roadmapping and sprint planning and growth experiments.
What to do now: 1) Audit your stack for a unified analytics platform and eliminate redundant tools. 2) Invest in first-party instrumentation and CRM integration to future-proof measurement. 3) Operationalize experimentation: document MDE, power, and decision rules. 4) Deploy gen ai responsibly with clear governance and retrieval-first context. 5) Build activation loops that turn insights into targeted in-app actions. Teams that execute on these fundamentals in 2025 will set the pace in 2026.
Inspired by this post on Amplitude – Best Practices.
I love when a bold platform bet translates into tangible product impact. Watching a team commit to a unified analytics platform and then operationalize it across the business is a master class in strategic focus and change management. That’s exactly what this story captures—and why it resonates with my own experience leading complex analytics migrations.
Learn how Dan Grainger led Haven's migration to Amplitude, focusing on user-friendly analytics and data governance for non-technical teams.
That single sentence distills what matters most: if analytics aren’t accessible to non-technical teams, you won’t get the adoption needed to drive outcomes. “User-friendly analytics” isn’t window dressing; it’s the linchpin for empowered product teams and true product-led growth. When teams can ask and answer their own questions—without waiting on analysts—velocity and quality of decision-making improve immediately.
From a product management lens, two elements stand out. First, the choice of Amplitude analytics as the central system of insight—consolidating scattered tools into a unified analytics platform—creates one source of truth for activation, adoption, and retention analysis. Second, a rigorous approach to data governance ensures that trust in the data scales alongside usage, especially for non-technical stakeholders who need clarity, not caveats.
Execution matters. In my playbook, these transformations succeed when you treat them as product initiatives, not IT projects. I partner early with stakeholder management champions, form product trios to define the measurement plan, and use in-app guides, product tours, and targeted onboarding to drive behavior change. The goal is simple: shorten time-to-insight for frontline teams while keeping the instrumentation robust and consistent.
Data governance is the quiet force multiplier. Clear tracking plans, consistent event taxonomies, role-based access, and privacy-by-design guardrails prevent entropy. When everyone speaks the same analytics language, you avoid “metric du jour” debates and keep the focus on outcomes vs output OKRs. That’s where scalable impact comes from.
Measurement closes the loop. I’ve found that when non-technical teams can self-serve retention analysis, funnel drop-off, and user activation patterns, they start running continuous discovery by default—asking better questions, testing smarter hypotheses, and accelerating learning cycles. Amplitude’s strength is not just visualizing what happened, but making it easy to connect behavior to outcomes teams care about.
The broader leadership lesson is straightforward: choose a platform that your broadest set of contributors can and will use daily, invest early in governance, and build enablement into your rollout plan. That’s how a migration becomes a multiplier. When the right platform meets the right operating model, the win is less about a tool and more about a learning culture that compounding value over time.
If your analytics stack feels fragmented or underused, this is your nudge. Align on a unified analytics platform, meet teams where they are with user-friendly analytics, and let governance do the heavy lifting behind the scenes. The payoff—in speed, alignment, and smarter bets—comes faster than most teams expect.
Inspired by this post on Amplitude – Best Practices.
I recently revisited a topic I once pushed back on: using AI to analyze (and maybe even synthesize) customer interviews. After six months of real-world experiments and countless conversations with seasoned product leaders, I’ve evolved my perspective. There is meaningful value here—but only when we’re clear about where AI helps and where it quietly erodes the hard-won customer understanding that powers great product decisions.
If you want to experience the conversation that sparked this reflection, you can listen to the episode on Spotify or Apple Podcast, and watch the discussion here: YouTube. It’s a candid, practical exploration of AI’s role in continuous discovery, and it mirrors what I’m seeing on the ground with product trios and empowered product teams.
Here’s the crux: AI raises the floor for beginners but accelerates experts even more. That matches my experience—early-career PMs get structure, momentum, and a confidence boost, while experienced interviewers can move faster without sacrificing nuance. But there’s a catch. If your interviewing skills aren’t solid yet, AI can create a veneer of insight that masks shallow understanding. In other words, it can help you go wrong more efficiently.
The conversation makes an important distinction between analysis and synthesis. Analysis is about extracting signals from the interview. Synthesis is about building meaning—connecting patterns, weighing contradictions, and deciding what to do next. AI can speed up the former with summaries and highlights. The latter—true synthesis—still demands expert judgment, context, and empathy.
One line from the episode stuck with me: your unpolished interview skills matter more than any shiny new AI workflow. I’ve felt that firsthand. When interview quality is uneven, dropping transcripts into an LLM won’t save you. You still need to synthesize every interview individually so the signals remain traceable and credible. That discipline keeps teams aligned, prevents overfitting to noise, and builds the organizational memory that fuels better bets.
We also explored the operational reality most teams face: interviews pile up. Backlogs grow. Leaders want speed. This is where “expert + AI” shines. With the right prompts, templates, and context, tools like ChatGPT and Claude can help transform raw transcripts into structured artifacts you can trust—provided a strong interviewer sets the frame and makes the calls. That balance preserves both velocity and quality.
What changed my mind most was the evidence from experiments—running sets of interviews through different LLMs and comparing outcomes. The patterns were consistent: beginner + AI is usually better than nothing, but the real performance gains come from expert + AI. When experts guide the process, AI becomes an accelerant rather than a crutch.
A favorite story in the episode takes a detour into building a gaming PC—an unexpected but perfect metaphor for AI’s limits. You can get great step-by-step guidance from a model, but when context shifts or edge cases appear, expertise is what keeps you from making expensive mistakes. Customer interviews are like that. Empathy comes from human interaction; AI can’t replace the experience of talking directly to your customers.
My practical guidance for teams integrating AI into continuous discovery: start with interviewing fundamentals, separate analysis from synthesis, and standardize how you capture single-interview learnings. If you need a tight template for this, refer to “The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview.” Use AI for summaries, clustering, and draft artifacts—but have an expert finalize the narratives, evaluate trade-offs, and document assumptions.
If you’re scaling this across an organization, invest in training first, then in workflows. Build a lightweight operating system for discovery: consistent interview guides, “story-based” techniques, and a shared library of prompts. Consider resources like “The Interview Coach,” as well as practical write-ups such as “Customer Interview Analysis: Where AI Helps and Hurts.” These help teams avoid common pitfalls and make better use of AI in high-judgment moments.
My bottom line: AI isn’t magic. It can help, but only if your interviews are strong and you provide the right context. Customer understanding is a competitive moat; outsourcing it entirely will cost you in the long run. Use AI to accelerate—not replace—the human judgment that makes product discovery work.
Resources and links worth exploring: ChatGPT, Claude, The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview, The Interview Coach, and Customer Interview Analysis: Where AI Helps and Hurts.
I’d love to hear how your team is using AI in discovery. What’s working, what’s risky, and where do you draw the line between automation and judgment? Share your experiences in the comments—our community learns faster when we compare notes.
When I push our organization to adopt the product operating model, I’m emphasizing a foundational shift—from “shipping roadmaps of features (output)” to solving real customer and business problems, measured by “business results (outcomes)”. That’s the difference between activity and impact, and it’s the only way to build durable value at scale.
This change inevitably reaches beyond the product organization. It reshapes how company stakeholders in Sales, Marketing, Customer Success, Finance, Legal, Security, and Operations engage with product teams, and it reframes what they expect from us. Instead of asking, “When will feature X ship?” they learn to ask, “How will we move the outcome that matters?”
In practice, the product operating model is a contract: product teams commit to outcomes, and stakeholders commit to partnership. That partnership means we co-own the problem, align on evidence, and share accountability for results. The reward is clarity—everyone sees how their work ladders to strategy and why the sequence of work makes sense.
Here’s how I align stakeholders around this model. First, I ground everything in outcomes vs output OKRs. We replace feature roadmaps with a clear strategy, prioritized problems, and measurable objectives. Our product roadmapping and sprint planning then serve the objectives—not the other way around—so capacity is allocated to the highest-leverage bets.
Second, I build empowered product teams around product trios (product, design, engineering). We practice continuous discovery with stakeholders: we share opportunity trees, test riskiest assumptions early, and bring partners into research when it informs go-to-market strategy, pricing, or enablement. This keeps us honest and avoids late-stage surprises.
Third, I establish operating rhythms that make outcomes visible. Monthly stakeholder reviews focus on progress toward objectives and what we’re learning—not status theater. Quarterly, we connect OKRs to business performance so leaders can see the throughline from discovery and delivery to pipeline, retention, or margin. If priorities shift, we renegotiate objectives explicitly.
Fourth, I define metrics that stakeholders trust. We use a balanced set of leading indicators (activation, engagement, cycle time) and lagging indicators (revenue, retention, unit economics). We socialize definitions early so no one debates the scoreboard mid-game. The result: faster decisions and less “data whiplash.”
Fifth, I invest in change management. Moving from outputs to outcomes can feel threatening if your success has historically been measured by launch volume or roadmap commitments. I address this head-on with training, transparent comms, and clear decision rights. The message is simple: outcomes create more autonomy for empowered product teams and more predictability for stakeholders.
At HighLevel, this approach has been especially powerful when cross-functional dependencies are high. For example, when we set an objective to improve user activation for a new CRM integration, we didn’t promise a bundle of features. We committed to a measurable lift in activation and a shorter time-to-value, co-owned with Customer Success and Marketing. That alignment unlocked smarter experiments, tighter enablement, and a more credible launch narrative.
The anti-patterns are predictable: treating OKRs as a renaming of the roadmap, equating discovery with indecision, or isolating product decisions from go-to-market strategy. The cure is equally consistent: bring stakeholders into discovery, attach every bet to an objective, and show progress with evidence—not just demos.
Ultimately, the product operating model is a leadership choice. It asks us to trade certainty theater for learning velocity, and feature checklists for business impact. When stakeholders see that shift pay off—in faster cycles, clearer priorities, and results that matter—support for the model moves from compliance to conviction.
In my role leading product teams, the AI product roadmap isn’t just a plan—it’s the operating system for how we discover value, prioritize with rigor, and ship with confidence. The pace has changed, the stakes are higher, and the best product managers are now orchestrating AI capabilities, data, and customer insight in near-real time.
Master the evolving art of the AI product roadmap. Prioritize smarter, turn data into direction and insight into action, only much faster.
When I say “AI product roadmap,” I’m talking about a living system that blends strategy, discovery, and delivery. It’s less about dates and more about outcomes, risk reduction, and sequencing learning. In practice, that means combining AI Strategy with product roadmapping and sprint planning, then validating each bet with real customer signals.
For prioritization, I anchor on outcomes vs output OKRs and connect them to measurable signals across the funnel. Continuous discovery keeps insights flowing, while a unified approach to analytics and retention analysis tells me where the lift is. This lets me rank initiatives not just by impact and effort, but by how quickly we can learn, iterate, and compound value.
On discovery, product trios are non-negotiable. We prototype early with gen ai and LLMs for product managers to accelerate concept validation and reduce ambiguity. When customers can co-create through in-app guides or lightweight product tours, we turn vague needs into crisp problem statements and testable hypotheses far faster.
On delivery, I pair tight feedback loops with experimentation. A deliberate cadence of A/B testing and strong instrumentation ensures we’re learning every sprint, not just launching. The goal is to de-risk decisions quickly, keep momentum high, and translate signals into roadmap movement without thrash.
Under the hood, the AI stack matters. I rely on a retrieval-first pipeline to ground models in trusted data, and I’m intentional about privacy-by-design and data governance from day one. As agentic AI patterns emerge, I put evaluation workflows in place so we can ship confidently—and safely—without slowing down innovation.
Finally, alignment is the multiplier. Clear narrative roadmaps tied to customer outcomes help stakeholders see trade-offs, while crisp interfaces with go-to-market and CRM integration close the loop from roadmap to revenue. When everyone can trace a line from AI strategy to shipped value, prioritization becomes easier and trust grows.
If you’re feeling the acceleration, you’re not alone. With the right AI product toolbox—rooted in discovery, grounded in data, and delivered through tight feedback loops—you can move faster, learn smarter, and build products your customers can’t live without.
By 2026, the AI Product Owner will be the keystone role that turns AI strategy into measurable business outcomes. In my teams, this seat bridges market insight, model capability, data governance, and shipping velocity—so product decisions are not just clever, but compliant, reliable, and fast.
I often describe the remit simply: "Here is your clear guide to the AI product owner role (skills, responsibilities, how it differs from PM) and ways AI tools supercharge delivery." In practice, the AI Product Owner translates business goals into model-backed experiences, aligns cross-functional execution, and ensures the product’s AI behavior remains safe, lawful, and on-brand under real-world constraints.
How does this differ from a traditional PM? While Product Management sets portfolio strategy, positioning, and market narratives, the AI Product Owner owns the AI experience end-to-end—data readiness, evaluation harnesses, safety guardrails, and the iterative model improvements that drive outcomes vs output OKRs. I anchor the role inside empowered product teams and product trios (PM/Design/ML Eng) to keep discovery continuous and delivery disciplined.
On responsibilities, I expect four pillars. First, discovery: continuous discovery with customers and internal experts to uncover use cases where generative AI or LLMs beat the status quo. Second, experience: define the right interaction patterns for AI UX, including retrieval-first pipeline choices, context window management, and feedback loops for human-in-the-loop correction. Third, governance: privacy-by-design, AI risk management, data governance, and regulatory compliance baked into the roadmap. Fourth, delivery: CI/CD for models and prompts, observable evaluation with A/B testing and minimum detectable effect (MDE), and SRE-grade incident management when AI behavior drifts.
Skills-wise, I look for product sense plus technical fluency. That includes LLMs for product managers (prompting, grounding, RAG), analytics mastery (Amplitude analytics, retention analysis, activation metrics), and comfort with DORA metrics and deployment frequency to keep iteration high but safe. Strong stakeholder management and clear writing are non-negotiable—AI capabilities evolve fast, and leaders must see risk, cost, and ROI with no ambiguity.
AI tools truly supercharge delivery when they eliminate bottlenecks. My practical stack: an AI product toolbox with Claude Code and a ChatGPT connector for rapid prototyping; CustomGPT workflows for support triage and internal knowledge; Pendo product tours and in-app guides to validate behavior changes; Intercom for customer support ai strategy; and tight CRM integration via HubSpot to measure revenue impact. The outcome is faster idea-to-learning cycles, sharper telemetry, and far cleaner handoffs.
For roadmapping, I prioritize thin slices that prove value early—shipping narrowly scoped assistants or copilots, then expanding with product roadmapping and sprint planning that ties capability unlocks to outcomes. A unified analytics platform helps compare human-only baselines to augmented workflows, while agentic AI patterns automate routine steps under strict guardrails.
Risk is a product surface, not a side task. I require explicit policy gates (PII handling, red-teaming, bias audits), clear escalation paths, and incident playbooks. When we treat policy and reliability as features, customers reward us with deeper adoption and higher trust.
If you’re pursuing the AI Product Owner path, build a portfolio around shipped learnings: the experiment you killed with data, the safety constraint you designed, the postmortem you led, and the business metric you moved. That story—evidence of disciplined discovery, responsible delivery, and real-world results—is exactly what teams (and boards) want to see in 2026.
I’m stepping into 2026 with a practical playbook for marketing analytics—one forged at the intersection of product management, go-to-market strategy, and AI Strategy. My lens is simple: connect data to decisions, decisions to outcomes, and outcomes to revenue. If you’re serious about product-led growth, this is the year to turn your unified analytics platform into a true competitive advantage.
Start 2026 off with a bang with exclusive insights and predictions from some of marketing analytics’ most influential voices. See what they have to say.
The biggest shift I expect is from channel-centric dashboards to journey-centric systems that stitch together product usage, CRM integration, and campaign performance. When Amplitude analytics or Pendo data sits alongside HubSpot pipeline metrics, we stop arguing about attribution models and start instrumenting the full revenue motion. That’s how marketing, product, and sales align around one truth: activation, engagement, and expansion drive sustainable growth.
I’m betting on deeper adoption of A/B testing with a rigorous minimum detectable effect (MDE) discipline and cohort-led retention analysis. Vanity metrics won’t cut it. Teams that operationalize outcomes vs output OKRs and tie experiments to LTV, CAC, and payback will outperform. The win is not more tests—it’s better tests that translate into compounding user activation and retention.
Gen AI will supercharge analysis, but not replace analytical thinking. I see LLMs for product managers accelerating root-cause analysis, surfacing anomalies, and explaining drivers behind conversion shifts. The craft moves from “pulling reports” to “asking higher-quality questions,” then validating with sound statistical methods. The highest-leverage teams will pair gen ai with strong taxonomies, clean event schemas, and clear definitions of North Star metrics.
Data governance becomes a growth enabler, not a compliance cost. With privacy-by-design, consented data, and well-documented schemas, your models become more accurate and your campaigns more resilient. When governance is strong, personalization sharpens, lookalike models improve, and executive confidence in the numbers rises—unlocking faster, bolder bets.
Product-led growth analytics will mature from “feature usage” to “value moments.” I’m focusing my teams on measuring time-to-value, depth-of-use, and expansion signals embedded in in-app guides, product tours, and contextual tooltips. The companies that make value visible earlier—and measure it precisely—will see outsized improvements in trial-to-paid and expansion.
Operationally, I expect tighter cadences between discovery and delivery. Product trios will partner with marketing to run continuous discovery on messaging, onboarding friction, and pricing signals. When insights flow directly into campaign creative and in-product experiments, learning cycles compress and the cost of delay drops.
If you’re building your 2026 roadmap, here’s my short list: consolidate tools into a unified analytics platform, standardize event taxonomies across web, product, and CRM, formalize MDE for every A/B test, and align OKRs to activation and retention milestones. Do this, and you’ll turn fragmented data into a durable growth engine—one that compounds every quarter.
Inspired by this post on Amplitude – Perspectives.
Customer expectations have never been higher. People expect fast, accurate, and effortless support, every time—and across industries, from ecommerce to financial services to healthcare, customer experience has become one of the most strategic levers for achieving durable competitive advantage.
Here’s the challenge I’ve seen again and again: you can’t improve what you can’t see. For years, most support organizations have been making decisions based on only a tiny slice of their customer interactions, captured through surveys that reach only the most motivated (or frustrated) voices. In my own program reviews, the most revealing insights often hid in the conversations that never made it into CSAT or NPS.
We created CX Score to change that. CX Score gives teams a complete view of the customer experience across every meaningful conversation—no CSAT or NPS surveys required. I wanted a signal that reflected reality, not just a vocal minority.
After launching CX Score, we saw many teams immediately use it to understand performance trends, highlight experience issues, and surface gaps across support operations. That early momentum validated the approach and showed us where to go deeper.
As adoption grew, new opportunities emerged. CX leaders found value from CX Score—but they also wanted the model to capture more nuance and identify the specific drivers leading to negative or positive scores, giving them clearer direction on where to focus. I heard the same ask from my own leadership peers: make it explainable and actionable.
That’s what we’ve built into the latest iteration of CX Score. If you’ve been using CX Score for a while and have noticed it shift recently, that’s an expected evolution. A recent shift in scores does not mean your support quality has dipped or that Fin or your team is performing worse than before—this one-time shift reflects a more advanced, more complete model that understands customer experience more deeply with even greater coverage.
Why CX Score needed to evolve
In the initial release, CX Score evaluated each conversation using a combination of sentiment, resolution, and support quality signals. It provided strong early insight and surfaced experience trends that were previously invisible. But as we analyzed real-world conversations across thousands of companies, it became clear that even these combined signals didn’t fully capture the nuance of how customers actually experience support—especially in moments where the outcome was technically correct, but the path to get there involved unnecessary friction, repeated explanations, or unresolved product limitations.
This evolution of CX Score builds on that foundation. It incorporates deeper contextual understanding of the entire interaction, creating a more complete and accurate reflection of the customer experience. As a product leader, that depth matters because it turns a lagging metric into a coaching and prioritization system.
How CX Score has evolved: deeper, more actionable insights
We expanded the CX Score evaluation criteria. CX Score now looks beyond just how your team replied, and into the broader context of the customer’s experience—including reasons that may be outside your support team’s direct control but still influence how your customers feel.
Alongside core support quality signals, we’ve introduced several new dimensions that capture what customers are actually reacting to:
Answer quality (Fin): How well Fin answered the customer’s queries—were responses clear, accurate, and able to resolve the issue without contradiction or repeated clarification?
Answer quality (Teammate): How well a human teammate answered the customer’s queries, using the same criteria: clarity, accuracy, and resolution without contradiction or repeated clarification.
Customer effort: How much effort the customer had to put in to get help (e.g. repeating themselves, multiple handovers, chasing follow-ups).
Strong emotion: Whether the customer expressed strong positive or negative emotions (e.g. joy, gratitude, frustration, anger).
The new CX Score adds context to every conversation: a donut chart surfaces drivers like policy feedback and effort, while a side panel explains why this interaction earned a 3 based on signals from an AI agent chat.
Product/Service feedback: Whether the customer praised or criticized the product (e.g. features, bugs, design gaps, etc.) or the service (e.g. delivery, reliability, onboarding, performance, etc.).
Policy feedback: Whether the customer praised or criticized a company policy (e.g. refunds, returns, account rules, limits, eligibility, etc.).
Broader coverage: more of your support volume now contributes to CX Score
Previously, some conversations couldn’t be scored reliably, especially short, simple, or low-context exchanges—which meant your CX Score was based on only a subset of your total support volume. With this update, CX Score now uses a wider set of criteria to evaluate each interaction. The result: more conversations qualify for scoring, fewer gaps in coverage, and a CX Score that reflects your true support mix—not just the longest or most detailed threads.
Greater transparency with richer, more informative summaries
We’ve made it much clearer why each conversation received the score it did. Right inside the product, every scored conversation now surfaces the specific reasons that influenced its rating—things like high customer effort, strong negative emotion, or product feedback. This added visibility makes it much easier to understand what’s driving your CX Scores, build trust in how they’re calculated, and confidently use them in reporting, coaching, and decision-making.
On top of that, conversation summaries now weave these reasons together with context from the customer’s original query. Instead of scanning the full thread, you can quickly see what happened (the core issue and how it was handled) and why it was scored that way (the key signals that impacted the rating). In my workflow, this shift lets me move from reading transcripts to taking action much more quickly.
From visibility to taking action
As customer experience becomes one of the clearest ways businesses can differentiate, teams need more than visibility—they need clarity on where to invest their time and how to improve. With deeper context and clearer reasoning behind every score, CX leaders can quickly identify what’s working, what needs fixing, and what to prioritize. CX Score moves from being a measurement tool to a system for continuous improvement.
What this unlocks for CX teams: Automatically flag conversations for review. Route threads with high customer effort, strong negative emotion, or low answer quality to QA, team leads, or specialists. Auto-forward product feedback to the right teams. Send conversations with product or policy criticism directly to Product, Engineering, or Ops channels, with no manual triage required. Spot operational issues such as handoff loops, unclear answers, or inconsistent workflows. Share transparent, explainable insights directly with leadership.
The future of CX measurement
CX Score isn’t just another metric. It’s becoming a new standard. Some customers have already chosen to replace CSAT entirely, using CX Score as their primary measure of experience quality because of the broader coverage, deeper context, and clearer paths to action it offers. This reflects a broader shift across the industry: as new competitors emerge and product differentiation narrows, customer experience is becoming one of the most strategic ways to stand out; measuring it accurately and understanding it deeply is now essential.
Our focus going forward is to help teams diagnose issues faster, prioritize with confidence, and improve at scale. This is the foundation we’ll continue to build on: turning every conversation into insight, and every insight into action.
The new CX Score is rolling out gradually to all customers and will be in your workspace by December 3rd.
Want to see CX Score in your workspace? Get started →
When I think about how I stay sharp as a product leader, one principle anchors my approach: design your learning system—don’t leave it to chance. Communities of practice are that system. They turn curiosity into a habit, accelerate product discovery, and strengthen product management leadership across empowered product teams.
I recently dug into a powerful conversation on the All Things Product podcast that explores how product people can intentionally design their own communities of practice—and why that matters for long-term learning and growth. The insights apply whether you operate as an independent coach or you’re scaling continuous discovery inside a product org.
I appreciated the contrast in learning styles. Teresa shares an introvert-friendly approach to continuous learning: curating a personal learning network (PLN) filled with people she wants to learn from. Petra contrasts that with a more collaborative style—learning with others through small peer groups, hackathons, and local meetups. Together, they unpack how each approach supports curiosity-driven development, how to find your “definition of good” when starting something new, and the habits that make learning a deliberate practice.
In my own practice leading product trios and shaping outcomes over output, I rotate between these modes. When I need speed or depth on topics like product discovery or stakeholder management, I learn from people: I curate a tight set of voices, reverse-engineer their decisions, and study how they frame trade-offs. When I need new patterns or accountability, I learn with people: I form small peer circles to review experiments, pressure-test roadmaps, and critique discovery plans. Both paths create momentum—one by focus, the other by feedback.
Key takeaways I’m acting on right now:
– What a “community of practice” really means in modern product work: the infrastructure that makes continuous discovery sustainable—and keeps empowered product teams aligned on craft.
– The difference between learning from people vs learning with people—and when to use each depending on whether you need depth, breadth, or accountability.
– How to find like-minded peers for collaborative learning: start with one person you respect, ask who they regularly spar with, attend one local meetup with a clear learning goal, and follow up with a structured exchange.
– Building your Personal Learning Network (PLN): set a theme (e.g., pricing, product roadmapping and sprint planning), prune it quarterly, and track “who I’m learning from” with the same rigor you track stakeholders.
– Personal knowledge management as a product skill: treat notes, highlights, and artifacts as a system, not a junk drawer—so insights compound and are easy to retrieve when you need them.
– Why curiosity-driven learning builds stronger product intuition: schedule time for curiosity and socialize it with peers so it scales beyond individual motivation.
– How committing to talks, books, or courses drives deeper learning: public commitments create productive pressure and force you to clarify your thinking.
Here’s the simple playbook I use with my team: define a quarterly learning theme; curate a small PLN aligned to that theme; assemble a peer circle (PM, Design, Eng) for monthly critiques; commit to shipping one artifact publicly (a talk, guide, or internal workshop); and close the loop with a short write-up on what changed in our decisions, discovery cadence, or bets. It’s lightweight, measurable, and fits neatly alongside product-led growth priorities.
Two quotes from the discussion capture the spirit perfectly:
“Nobody on that list knows they’re in my personal community of practice.” — Teresa Torres
“Sometimes you don’t know your new definition of good until you start learning.” — Petra Wille
If you’d like to go deeper, you can listen to the episode on your favorite platform:
Listen to this episode on: Spotify | Apple Podcasts
Curation – All Things Product with Teresa & Petra episode
Hamel’s Blog
AI Evals for Engineers and PMs course by Hamel Husain (get 35% off through Teresa’s link) on Maven
Harold Jarche’s Personal Knowledge Management workshop
Petra’s book, Strong Product Communities – The Essential Guide to Product Communities of Practice
I’d love to hear how you’re designing your own community of practice. What’s your learning theme this quarter? Which peers are you building with, and what commitments are helping you go deeper? Drop your thoughts—I’ll share my own PLN stack and peer-circle cadence in a future post.
I’ve learned the hard way that the fastest, lowest-risk growth lever is hiding in plain sight: reactivating the users we already earned. When our team prioritized win-back programs over new acquisition, we unlocked higher net revenue retention, shorter payback periods, and stronger product-market signal—with a fraction of the spend.
"Discover why reactivating dormant users delivers better ROI than new acquisition. Learn how to identify and bring back at-risk users via targeted campaigns." That insight matches what I see daily: win-back campaigns compound value because they capitalize on existing familiarity, prior data, and stored intent.
Here’s the ROI logic I use. New acquisition burns budget on education and trust-building before value is realized. Reactivation, by contrast, taps into latent demand and prior setup, which means lower effective CAC, faster time-to-value, and higher LTV recapture. In retention analysis, these programs often outperform prospecting by a wide margin because the user already knows how to get value—they just need a relevant nudge.
To find the right users to re-engage, I start with leading indicators of risk: declines in weekly active use, feature decay (e.g., key workflows not triggered), shrinking session depth, and unresolved outcomes. Amplitude analytics or a unified analytics platform help me segment cohorts by recency, frequency, and monetary signals, then rank accounts by churn propensity. I also track intent proxies like billing pauses, reduced seat utilization, and cooling support contact.
I group users into three practical tiers: “at-risk” (recent value decay), “dormant” (no critical events in the past 30–60 days), and “churned-eligible” (post-cancel window with a viable path back). Each tier gets a distinct message strategy, incentive structure, and time horizon. The goal is to match the intervention to the activation friction each group faces.
For creative strategy, I anchor on the outcome they originally hired us to deliver. I lead with the value proposition they care about, not the features. A strong win-back narrative reminds users of the job-to-be-done, showcases what’s improved since they last engaged (new capabilities, performance, integrations), and offers an effortless next step—often a guided “return-to-value” flow or a one-click way to pick up where they left off.
Channel orchestration matters. I use Intercom and Pendo to deliver contextual nudges, in-app guides, and lightweight product tours that meet users at the precise moment and screen of friction. With CRM integration, we coordinate email and SMS for timely follow-ups, then reinforce success in-product with progressive tooltips and checklists. The best-performing sequences pair a personalized message, a sharp call-to-outcome, and a low-friction path back to activation.
Experimentation is non-negotiable. I run A/B testing on subject lines, offers, and in-product prompts, and size tests with a minimum detectable effect (MDE) that’s realistic for each segment. We personalize content by prior feature use, industry, and plan tier to avoid generic blasts that underperform. Over time, the library of proven treatments compounds, and the system becomes predictively better at catching risk earlier.
Measurement should be unambiguous. I define “reactivation” as the return to a qualifying level of usage that mirrors healthy customers (e.g., core event completion in a set window), not just a login. I track reactivation rate, time-to-reactivation, reactivated revenue, payback, and LTV uplift versus holdout cohorts. Cohort views in Amplitude analytics reveal whether improvements are persistent, and whether we’re driving true behavior change or short-term spikes.
Trust is part of the strategy. We build privacy-by-design into all outreach and respect user preferences. Clear value exchange (why this message, why now, how to opt out) consistently improves response rates and strengthens long-term relationships—win-backs should feel helpful, not harassing.
Operationally, I pair product-led growth with lifecycle marketing: product teams ship the “return-to-value” experiences; growth teams run the orchestration; customer success brings context from the field; and analytics sets guardrails and success criteria. When executed as a system, win-backs turn from occasional campaigns into a durable, compounding growth engine.
If you’re chasing growth in a tight market, start here. Your next quarter’s ARR may be sitting in dormant cohorts that are one relevant nudge—one fast path to value—away from coming back.
Inspired by this post on Amplitude – Best Practices.
I’ve spent years helping talented engineers explore what’s next when pure coding no longer feels like the only—or best—path. From hiring across cross-functional teams to mentoring career pivots, I’ve seen firsthand how engineering strengths translate into high-leverage roles that shape product, strategy, and growth.
Software engineers have alternative career options leveraging their skills in roles like product manager, data scientist, business analyst, and 22 more.
When an engineer moves into product management, they’re not starting from scratch—they’re redirecting problem-solving, systems thinking, and customer empathy toward outcomes. In practice, that means mastering product discovery, strengthening stakeholder management, and getting fluent in product roadmapping and sprint planning, so decisions are guided by impact rather than “outputs vs outcomes” confusion. I’ve watched this transition unlock empowered product teams and clearer prioritization across complex backlogs.
Data-oriented paths are equally compelling. If you enjoy experimentation and evidence-based decisions, roles in analytics or data science reward rigor. Think A/B testing, identifying the minimum detectable effect (MDE), and using tools like Amplitude analytics to translate behavioral signals into product bets. Pair that with retention analysis and you’ll become indispensable to growth conversations.
Business-facing roles such as business analyst or product marketing manager are ideal if you’re energized by customer problems and market narratives. Your engineering fluency sharpens value propositions, product positioning, and go-to-market strategy in a way that resonates with both buyers and builders. In my teams, the best bridges between product and revenue often came from former engineers who could articulate trade-offs with clarity.
If operational excellence is your edge, consider SRE, DevOps, or cybersecurity. The same instincts that push you toward clean CI/CD pipelines and resilient architectures translate well into incident management, threat detection and response, and privacy-by-design practices. These roles reward systems thinking and the ability to balance reliability with delivery speed.
For engineers who love community and storytelling, developer evangelism is a natural fit. You’ll translate complex concepts into actionable guidance, from in-app guides and product tours to UX writing and documentation. The best evangelists I’ve worked with turn feedback loops into product insight, strengthening activation and product-led growth without heavy sales pressure.
Customer-facing technical roles—solutions engineer, forward deployed engineer, or technical consultant—let you stay close to the product while solving real-world problems. You’ll drive onboarding quality, user activation, and adoption while surfacing insights that influence roadmaps. Done well, this work tightens the loop between customer outcomes and product decisions.
AI-centered roles are expanding rapidly. If you’re curious about AI Strategy, retrieval-first pipelines, or the practical use of LLMs for product managers, you can bring an engineer’s discernment to a noisy space. The most valuable contributors here pair pragmatic architecture choices with clear risk management and measurable business value, not hype.
Leadership tracks remain a strong option too. The IC to manager transition isn’t about title; it’s about raising the ceiling for others. You’ll coach empowered product teams, shape organizational development, and align initiatives to defensible metrics—think DORA metrics for flow, leading indicators for value, and OKRs that measure outcomes over output.
If you’re exploring a pivot, start small and intentional. Run “career A/B tests” by taking on cross-functional projects, shadowing adjacent roles, or shipping a lightweight portfolio that demonstrates the new muscle. Join a ProductCon session, practice conference networking, and refine a narrative that links your engineering foundation to the outcomes your target role owns.
Finally, map your personal unfair advantages—domain knowledge, systems thinking, customer empathy, or operational rigor—to the roles that value them most. With focus, you can reposition your engineering experience into a differentiated story that accelerates your next chapter. The breadth of options is real, and with a deliberate plan, you’ll turn curiosity into conviction—and conviction into impact.
