Tag: onboarding

  • Operating Lessons from Plaid’s COO for Scaling Through Change

    Operating Lessons from Plaid’s COO for Scaling Through Change

    Scaling an operating organization is not simply a matter of adding process. It requires leaders to decide where the company needs control, where teams need discretion, and how much capacity must remain available for opportunities that cannot be predicted.

    First Round’s conversation with Plaid COO Eric Sager offers a useful operating model for making those choices. His experience at Plaid, following leadership roles at Bluevine and Square, connects organizational resilience, customer ownership, executive leverage, and employee onboarding into one coherent discipline.

    Key takeaways for operating leaders

    • Preserve capacity for unexpected, strategically important work instead of scheduling every team to its theoretical limit.
    • Give each customer relationship one accountable owner, supported by specialists who can enter when their expertise is needed.
    • Evaluate speed alongside risk and cost; faster execution is not automatically the better business decision.
    • Measure an executive’s impact partly by whether the organization can function without constant executive intervention.
    • Use direct contact with new employees to test how onboarding and culture are experienced, not merely how they were designed.

    Operating slack is a strategic resource

    Sager argues against running an organization at 100% capacity. First Round reports that retaining flexibility helped Plaid respond to opportunities involving OpenAI, Perplexity, and Replit. The broader lesson is not that teams should operate without discipline. It is that a plan consuming every available hour leaves no room for high-value work that appears after planning is complete.

    This is especially relevant to product and go-to-market leaders. A team optimized entirely for utilization can look efficient while becoming slow to respond. Spare capacity acts like an option: the company incurs a visible short-term cost in exchange for the ability to pursue an important customer, solve an urgent problem, or adapt to a market shift.

    The practical challenge is protecting that slack from becoming unowned time. Leaders still need clear priorities and decision rights. Capacity should be available for defined classes of work, such as strategic deals, urgent customer risks, or emerging product opportunities, with explicit authority over who can redirect it.

    Customer ownership should remain simple as expertise grows

    As a company scales, generalist roles often give way to specialized customer segments and expert functions. That specialization can improve the quality of advice while making the customer’s experience fragmented. Plaid’s reported answer is a quarterback model: one person owns the full relationship while specialists remain available to contribute.

    The distinction between accountability and expertise matters. Specialists may understand a product, industry, or technical issue more deeply, but the customer should not have to coordinate the internal organization. A single owner maintains context, aligns the contributors, and remains responsible for the overall outcome.

    This model also exposes weak handoffs. When ownership is shared ambiguously, teams can complete their individual tasks while the customer’s larger problem remains unresolved. A named quarterback makes escalation clearer without requiring that person to solve every issue personally.

    Speed, risk, and cost belong in the same decision

    The source describes Sager treating speed, risk, and cost as a three-way trade-off. This is a more useful framing than a blanket instruction to move faster. Accelerating work may require more people, introduce operational exposure, or reduce the time available to validate a consequential choice.

    A sound operating review therefore asks what the company gains by acting sooner, what can go wrong, and what additional resources acceleration requires. Reversible decisions can often move quickly because errors are easier to correct. Decisions with material customer, regulatory, or organizational consequences may justify a slower path. The objective is not maximum speed; it is an appropriate speed for the consequences involved.

    That discipline becomes more important during turbulence. According to First Round, Sager helped lead Plaid through the pandemic, the collapse of its planned Visa acquisition, a fintech downturn, and the AI boom. The source also says Plaid remained focused after the Visa transaction fell through and later raised at nearly three times the price. These are reported outcomes, but the transferable insight is the importance of separating a changed circumstance from a changed mission.

    An effective COO reduces organizational dependency

    Sager’s view that strong COOs deliberately make themselves obsolete challenges the image of the executive as permanent chief problem-solver. If routine decisions repeatedly rise to the same leader, the organization may be borrowing that person’s judgment without developing its own.

    Reducing dependency does not make the role irrelevant. It shifts executive attention toward the ecosystem, the business, and the team – the three areas First Round says shape Sager’s working week. The COO can then concentrate on cross-functional constraints, leadership quality, and new operating problems instead of repeatedly compensating for missing ownership.

    A useful test is whether teams have the context, authority, and mechanisms to proceed when the executive is unavailable. Delegation without context produces guesswork; context without authority produces escalation. Both must travel together.

    Cold-calling new hires turns onboarding into evidence

    One of Sager’s more unusual practices is personally cold-calling brand-new employees. The source does not provide enough detail to judge the full method or its results, but the practice points to a valuable principle: senior leaders need unfiltered signals from people experiencing the organization for the first time.

    New hires notice unclear language, missing context, and mismatches between stated culture and everyday behavior. Direct outreach can reveal whether onboarding is creating confidence or merely completing administrative steps. It can also make leadership more tangible, although leaders should avoid turning the conversation into a test in which employees feel pressured to give reassuring answers.

    The forward-looking opportunity is to connect those conversations to operating improvement. When recurring confusion becomes visible, leadership can clarify ownership, revise onboarding, or remove unnecessary process. That is how a personal executive habit becomes a scalable management system.


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  • Stop Support Tickets Before They Start: How AI Unsticks Users and Lifts Conversions

    Stop Support Tickets Before They Start: How AI Unsticks Users and Lifts Conversions

    Every moment of friction in a product carries a hidden cost: attention drifts, motivation wanes, and the next click becomes a support ticket—or worse, silent churn. Over the years, I’ve learned to treat “stuck” as an urgent product signal, not just an operational nuisance. When we unstick users in the flow, we protect revenue, brand trust, and the momentum that powers product-led growth.

    Learn how Amplitude’s Global Support team uses AI Assistant to reduce support tickets, prevent user churn, and increase conversions.

    I reference that line often because it captures a proven pattern: meet users where confusion peaks and resolve it instantly. In my practice, the formula is straightforward—pair behavioral analytics and session replay with a just-in-time AI Assistant, routed by clear driver trees. This transforms support from reactive firefighting into a proactive, in-product experience that accelerates onboarding and boosts user activation.

    Here’s how I operationalize it. First, I use Amplitude analytics and behavioral analytics to surface high-friction steps—pages with elevated drop-off, loops, or rage clicks. Session replay clarifies the “why” behind the numbers, while cohort and retention analysis reveal who’s most at risk. Then I deploy targeted in-app guides and tooltip design to preempt known pitfalls, while an AI Assistant handles real-time questions with context from our knowledge base and product docs.

    The AI Assistant is more than a chatbot. With well-structured AI workflows, it detects intent, pulls precise snippets from docs-as-code, and handles routine issues instantly. When complexity spikes, it executes a graceful handoff to consultative support via Intercom or a Zendesk integration—preserving conversation history and sentiment cues—so humans spend time where judgment matters. This hybrid model keeps response times low without sacrificing quality.

    To de-risk changes, I lean on A/B testing and feature flags. I measure time-to-value, activation rate, and funnel conversion as leading indicators, while tracking ticket deflection, CSAT, and NRR as trailing indicators. The goal isn’t just fewer tickets; it’s faster learning loops and a compounding improvement in user outcomes. When we see activation curves steepen and onboarding friction flatten, we know the system is working.

    Practically, I start with the top three friction points in onboarding, implement narrow in-app guides, and deploy the AI Assistant with strict guardrails and clear escalation paths. Weekly reviews align product, customer success, and solutions engineering around shared telemetry—so we tune prompts, content, and UI patterns together. Over time, I’ve seen ticket volume decline meaningfully, while conversion and retention rise as users experience fewer dead ends.

    If you’re evaluating where to begin, identify the moments where confusion compounds—pricing configuration, integrations, and data mapping are common culprits. Then introduce targeted, context-aware help right where users hesitate. You’ll not only prevent “every stuck user” from turning into a ticket—you’ll convert friction into confidence, and confidence into growth.


    Inspired by this post on Amplitude – Best Practices.


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  • Join Me in April: Build a Continuous Interviewing Habit and Unlock Real Customer Insights

    Join Me in April: Build a Continuous Interviewing Habit and Unlock Real Customer Insights

    “Continuous Discovery Habits” turns five this year, and I’m celebrating by reading it with our community—together, in practice, not just in theory. Each month, I’m publishing an in-depth reading guide with the chapters we’ll cover, a preview of the most important concepts, short videos you can share with your teams, individual and team discussion questions, practical exercises to apply what you read, and additional resources to go deeper.

    We’ll keep the conversation active in the comments each month and meet live once a quarter to compare notes, share what’s working, and troubleshoot what’s not. If you’re joining late, no problem—start with the current month or go back to January. You can also find all of the book club articles here.

    If you want to participate, grab a copy of the book (or dust off your old one), share the “Spread the Love” videos with colleagues, block time for the team exercises, and register for the community sessions. Let’s dive in together.

    This Month’s Reading

    Chapter: Chapter 5: Continuous Interviewing. Estimated reading time: ~37 minutes.

    This chapter grounds us in why interviewing on a regular cadence is critical to the success of any product trio; how cognitive biases affect what we learn from direct questions; the difference between research questions and interview questions; how to use story-based interviewing to uncover actual customer behavior (not ideal behavior); the interview snapshot, a one-page tool for synthesizing what you learned from a single interview; how to automate the recruiting process so interviewing becomes easier than not interviewing; and why product trios should interview customers together.

    Need a copy? Grab the book.

    Share the Love with Friends and Colleagues

    We learn best in community. To help your team rally around these practices, share these concise primers and invite them to join the book club discussion with you.

    What are customer interviews? – Build a competitive advantage that compounds over time.

    What should we ask in customer interviews? – Mitigating cognitive biases.

    Research questions vs. interview questions – And why the difference matters.

    Getting reliable feedback from customer interviews – Ask the right questions.

    Who should conduct customer interviews? – My answer might surprise you.

    How do you find customers to interview? – Automate the recruiting process.

    The Interview Snapshot – How to synthesize a single customer interview.

    Reflect and Discuss What You Read

    Reflection cements learning. This month, I’m challenging you—as I challenge my own teams—to build a weekly habit of interviewing customers and to shift from direct questions (which trigger bias) to collecting specific stories about past behavior. For many teams, this is a big mindset change: from infrequent “big research projects” to lightweight, continuous conversations that fuel daily decision-making.

    Individual Reflection: Think about your last customer interview or conversation. Did you rely on direct questions, or did you excavate a specific story about what happened? How might the answers have changed if you had used the other approach?

    Consider your own behavior—buying jeans, going to the gym, choosing what to watch on Netflix. Where do your ideal intentions differ from what you actually do? How might that same gap show up in your customers’ answers to direct questions?

    Scan your calendar from the past month. How many customer interviews did you conduct? If it’s fewer than four, what got in the way? What needs to change to make weekly interviewing sustainable?

    Team Discussion: As a team, discuss your current interview cadence. If you’re not interviewing at least weekly, name the biggest obstacle—recruiting, time, or synthesis—and commit to reducing one barrier this month.

    Try this together: Ask a teammate, “How does a product idea go from concept to launch at our company?” Have them write it down. Then ask for the last specific feature or improvement that launched and capture the story. Compare the two. What’s different? What does this reveal about the gap between ideal process and actual process?

    If you already interview regularly, ask: Who participates? Is it just one person (like the designer or product manager), or does the whole trio join? What value might you be missing by not having all three perspectives in the room?

    Put It Into Practice

    Understanding the “why” is easy; building the habit is the work. The following exercises are how my teams operationalize continuous interviewing week over week.

    Exercise: Conduct a Story-Based Interview (Time: 20–30 minutes. Do this with your product trio.) Schedule a conversation with a current customer. Instead of drafting a long script, identify a handful of research questions (what you need to learn) and translate them into one story-based interview question (what you’ll ask).

    For example, research questions might include: What challenges do customers face when onboarding? Where do they get stuck? What are we asking them to do that they don’t understand? How can we make it easier for them to get to the activation moment? The corresponding interview question could be: Tell me about the first time you used our product.

    During the interview, excavate the story with temporal prompts like “What happened first?”, “What happened next?”, and “What happened before that?” If the participant drifts into generalities (“I usually…” or “In general…”), gently bring them back to the specific instance.

    After the interview, debrief as a trio. What did each of you hear? Which opportunities surfaced? What surprised you? If you want personalized, detailed feedback on your technique, consider the Interview Coach available through the Story-Based Customer Interviews course.

    Exercise: Create Your First Interview Snapshot (Time: 30 minutes. Do this with your product trio immediately after the interview.) Using the interview snapshot template, capture a photo of the participant (or a visual that represents their story), quick facts about their context, a memorable quote you’ll still recall months from now, the opportunities (needs, pain points, desires) you heard, notable insights that aren’t yet opportunities, and an experience map that illustrates the story. Over time, aim to complete each snapshot in 15–20 minutes.

    Go Deeper: Additional Reading

    If you prefer audio, I’ve included an audio summary for paid subscribers that covers this month’s chapter plus the resources below.

    Related In-Depth Guides: Customer Interviews: How to Recruit, What to Ask, and How to Synthesize What You Learn.

    The Value of Continuous Interviewing: Why Product Trios Should Interview Customers Together – How interviewing together ensures research is timely, actionable, and believable.

    How to Find Customers to Talk To: Customer Recruiting: Get Easy Access to Customers Week Over Week – Practical strategies for automating your recruiting process. Ask Teresa: How Do You Select Customers for Customer Interviews? – Who to interview and how to recruit them. Tools of the Trade: Finding People to Interview Before You Have Customers – Recruiting strategies for early-stage products.

    What to Ask in Your Interviews: Why You Are Asking the Wrong Customer Interview Questions – Understanding the gap between ideal behavior and actual behavior. Story-Based Customer Interviews Uncover Much-Needed Context – Why collecting specific stories is more reliable than asking direct questions. Ask Teresa: What Are the Best Customer Interview Questions? – Common questions and how to improve them. Ask About the Past Rather than the Future – Why memories about recent instances are more reliable than speculation.

    How to Take Notes and Synthesize What You Are Learning: How to Take Notes During Customer Research Interviews – Practical tips for capturing what you hear. The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview – A comprehensive guide to creating and using interview snapshots. Customer Interview Analysis: How AI Helps and Hurts – Learn how to use AI effectively.

    Videos: All Things Product Podcast: Customer Interview Analysis – Petra and I discuss using AI to analyze customer interviews, the risks and benefits, and why your interviewing skills matter more than any AI tool.

    Other Resources from Around the Web: The Top 5 Mistakes Product Teams Make With Customer Interviews by Pragmatic Live. Continuous interviewing with Kristian Collin Berge (CEO & Co-founder at UX Signals) by Afonso Franco. How to Make Time for Customer Interviews & Validation by Rich Mironov. Brave UX: An interview with Teresa Torres by Brendan Jarvis.

    Related Courses: Customer Recruiting for Continuous Discovery – Get easy access to customers week over week. Story-Based Customer Interviews – Collect reliable feedback from every customer conversation.

    Our Live Discussion Schedule

    Our live discussion sessions are for paid subscribers. Sessions are not recorded. Invitations will go out to members two weeks before each event—add these to your calendar now: Tuesday, June 16, 2026: 9am–10am PDT. Thursday, September 17, 2026: 9am–10am PDT. Wednesday, December 16, 2026: 9am–10am PST.

    Audio Summary

    This summary was produced by NotebookLM. The sources supplied were the book chapters as well as all of the additional reading.

    This article is part of the CDH Book Club celebrating the five-year anniversary of Continuous Discovery Habits.


    Inspired by this post on Product Talk.


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  • 5 powerful ways I use Pendo MCP to bring product analytics into ChatGPT, Claude, and Cursor

    5 powerful ways I use Pendo MCP to bring product analytics into ChatGPT, Claude, and Cursor

    I’ve wanted my product analytics to follow me into every conversation, doc, and code review. Now they do—and it changes how quickly I can move from question to insight to decision.

    Pendo is now available as an MCP (Model Context Protocol) server, easily accessible in Claude, ChatGPT, and Cursor.

    Practically, this means my core product analytics, segments, and qualitative feedback can be surfaced right where I plan sprints, refine opportunity solution trees, and write specs. Fewer context switches, tighter feedback loops, and faster product decisions.

    Here are five ways I put Pendo MCP to work across my day-to-day workflows—grounded in product management leadership habits and built for speed and clarity.

    1) Daily triage and decision support: In ChatGPT or Claude, I quickly query product analytics to spot anomalies, usage spikes, or drop-offs by segment. Prompts like “Highlight top features by week-over-week growth and flag statistically notable anomalies” help me focus standups on what matters, tightening the loop between observability and action.

    2) Continuous discovery prep: Before customer interviews, I pull recent NPS verbatims, feature adoption by persona, and journey mapping signals. In seconds, I have a concise brief that blends behavioral analytics with customer interviews, so I can ask sharper questions and validate assumptions faster—without leaving my AI workspace.

    3) Evidence-based prioritization: When shaping the roadmap, I bring in retention analysis, user activation metrics, and cohort views to weigh impact vs. effort. Using Pendo MCP inside Claude or ChatGPT, I translate insights into driver trees and a clear product strategy narrative that aligns stakeholders around outcomes, not output.

    4) Product-led growth and onboarding: I review onboarding funnels, identify friction in first-run experiences, and draft in-app guides and tooltip copy that meets users at the exact drop-off points. With Pendo MCP, the context for product tours and in-app guides is right where I’m writing, so iteration cycles stay tight and data-informed.

    5) Customer success and QBR prep: For account health and QBRs vs OKRs alignment, I generate succinct summaries of feature adoption, sentiment, and value realization—ready to paste into email, decks, or a CRM integration. This keeps sales-led and product-led growth motions unified, with a single source of truth visible in ChatGPT, Claude, or when I’m coding in Cursor.

    The net effect: higher-quality decisions, faster. By bringing product analytics into my AI workflows, I reduce context switching, improve context window management, and keep my team anchored to real user behavior. Wherever I’m working—ideating in Claude, drafting in ChatGPT, or reviewing code in Cursor—my Pendo context is right there with me.

    If you’re leading empowered product teams, this is a pragmatic way to operationalize continuous discovery, speed up alignment, and turn insights into outcomes. It’s a simple shift with outsized leverage.


    Inspired by this post on Pendo – Best Practices.


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

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

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

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

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

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

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

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


    Inspired by this post on Amplitude – Best Practices.


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

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

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

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

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

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

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

    Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.

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

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

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

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


    Inspired by this post on Pendo – Best Practices.


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  • From Chaos to Clarity: My Proven Playbook to Scale an Analytics Taxonomy That Sticks

    From Chaos to Clarity: My Proven Playbook to Scale an Analytics Taxonomy That Sticks

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

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

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

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

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

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

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

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

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

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

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

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


    Inspired by this post on Amplitude – Best Practices.


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  • How Deep AI Transforms Support Into Proactive, Omnichannel CX—No Extra Headcount Needed

    How Deep AI Transforms Support Into Proactive, Omnichannel CX—No Extra Headcount Needed

    For years, I chased the elusive goal of delivering a perfect customer experience. Today, with AI embedded in our support operations, that standard is finally within reach—and it’s reshaping how we prioritize, design, and scale service.

    In “The 2026 Customer Service Transformation Report,” teams report early, tangible wins from AI: faster responses, higher efficiency, and consistent coverage across languages and time zones. Those gains create the capacity we’ve always needed. The more we push the technology, the more quality improvements we unlock.

    This marks a fundamental shift. As AI takes on more, our focus can finally move from firefighting to crafting the customer experience. When the AI is working, the measure of success becomes how well it’s working—across accuracy, tone, resolution, and end-to-end journey quality.

    I’ve seen this transformation firsthand. Mature AI deployment gives my team “breathing room,” so we can design for consistently excellent outcomes rather than obsess over deflection. That means widening access to support, removing friction on the path to resolution, and anticipating customer needs before they escalate.

    In our own support organization, we opened support to trial customers, accelerated first response times, and added consultative sessions during onboarding. We absorbed a 300% increase in total demand without adding headcount—made possible by deep integration of an AI Agent and a disciplined AI strategy.

    Infographic comparing ability to meet rising customer expectations: 27% of organizations with mature deployments say support always meets expectations, versus 9% at initial deployment, shown as orange and gray bubbles.
    Teams with mature customer service deployments are nearly three times likelier to say they always meet increasing expectations—27% vs 9% at initial rollout—highlighted by bold orange and gray comparison bubbles.

    Across the industry, the pattern is similar. When teams initially deploy AI, only 9% say they can always meet customer expectations. That number triples as teams reach a mature level of deployment. Even as expectations rise, the organizations that deeply integrate AI—complete with clear ownership, robust instrumentation, and continuous improvement loops—are the ones most likely to meet (and exceed) the bar.

    Looking ahead to 2026, I expect omnichannel consistency to become a key differentiator. The data shows planned investment is distributed nearly equally across chat, email, and social messaging (36% each), closely followed by phone/voice (31%). The question is no longer “Which channel should we optimize?” but “How do we deliver a consistent, AI-powered experience everywhere our customers are?”

    Teams that solve for omnichannel consistency will bridge the long-standing gap between what customers expect and what support can deliver. Every touchpoint becomes an opportunity to exceed expectations and build durable trust.

    Consider Clay, a team that scaled support without sacrificing quality. Support is one of their main growth drivers, and as their customer base expanded, ticket volume surged. Early on, they concentrated much of their effort in Slack, cultivating close, transparent community relationships. But relying on a single channel created friction as they grew; customers wanted the flexibility of email and in-app chat, and Clay needed to deliver the same high standard everywhere.

    Infographic showing channels where teams plan to expand AI usage in 2026: chat 36%, social 36%, email 36%, and phone/voice 31%, displayed as four bold orange blocks with labels.
    Where AI investment is headed for customer service in 2026: chat, social, and email lead at 36%, with phone/voice close behind at 31%. A bold visual snapshot of shifting channel priorities in CX.

    By unifying their support experience with an AI Agent, Clay brought consistency across channels. Today, AI is involved in 90% of all queries and handles half of Clay’s total volume, upwards of 7,000 queries a month. First response rates improved significantly, freeing the team to focus on proactive, high-impact work.

    That work includes identifying content gaps for education and content marketing, reaching customers before they need to ask for help, and surfacing feature requests and recurring challenges to product teams. Clay proves that when support is truly great, it becomes a competitive edge.

    So how do you build a superior customer experience with an AI Agent? Here are five principles I use when scaling toward mature deployment.

    1) Treat customer experience like a product. Treating support as a product means designing, building, and managing the support experience with the same rigor as your core product. You define goals (faster onboarding, higher CSAT or CX Score, lower churn). You map flows (AI starts the conversation, human handovers, proactive nudges). You instrument the journey (track handoffs, drop-offs, success states). You run tests and ship improvements (tone tweaks, fallback paths, training updates). You own the outcomes (gather feedback, measure performance, use insights to continuously improve the system).

    Neon green hero graphic reading 'The 2026 Customer Service Transformation Report', with subhead 'The AI deployment gap is widening' and a black 'Get the report' button over a bar-chart pattern.
    Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

    2) Lead with AI, back with humans. AI isn’t replacing the human touch. It’s redefining when, where, and how it’s most valuable. In a scaled model, AI is the first responder and the end point for most conversations. Humans step in where they add the most value—particularly during high-stakes issues—and those handoffs should feel seamless. Meanwhile, your team focuses on improving AI performance and optimizing the end-to-end journey.

    3) Be proactive. Use AI to anticipate needs, guide customers before problems arise, and nudge them toward successful outcomes. This is where customer support AI strategy shines—moving from reactive triage to journey orchestration that protects momentum and builds trust.

    4) Build for trust. Many customers still carry the legacy of clunky chatbots that delivered vague answers and dead ends. You earn trust by showing that your system works. Don’t hide your AI Agent behind layers of “choose an option.” Get customers to the AI quickly, demonstrate real problem-solving, and ensure that when a human is needed, they join with full context to resolve complex issues efficiently.

    5) Make it feel personal. Your AI Agent represents your brand. The way it speaks, follows policies, and responds matters. Use tone control, fallback logic, and language preferences to align the experience to your standards. Consistency builds trust; personality builds connection and loyalty.

    Perfect really is possible. With deep AI implementation, you can scale comprehensive, fast, and personal support across channels—so customers feel supported not just when they reach out, but throughout their journey. That’s the promise of modern AI workflows in support, and it’s what will separate leaders from laggards in the years ahead.


    Inspired by this post on The Intercom Blog.


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  • Stop Losing Customers: Predict Churn with Digital Analytics and Act Before It’s Too Late

    Stop Losing Customers: Predict Churn with Digital Analytics and Act Before It’s Too Late

    I stopped treating churn as a postmortem and started treating it as a forecasting problem. When we instrument our product, connect the dots across journeys, and embed those signals into our daily operations, churn becomes predictable—and preventable. This shift has been one of the most impactful product strategy moves my teams have made for product-led growth and retention analysis.

    "Discover why and how CS teams can use digital analytics to take a proactive, predictive approach to churn, stopping it before it happens." That is exactly the mindset I bring to customer success and product collaboration: anticipate risk, intervene with precision, and demonstrate measurable impact.

    The practical work starts with leading indicators. I look at user activation milestones, time-to-first-value, feature adoption depth, frequency and recency of key events, account-level coverage (are multiple users active or just one champion?), usage volatility, and friction signals like repeated errors or stalled onboarding. These behavioral inputs are stronger predictors of churn than survey sentiment alone.

    From there, I create a churn risk score. Early on, a transparent rules-based model is usually enough to separate healthy from at-risk accounts. Over time, we can layer in supervised learning if the data supports it. I rely on Amplitude analytics, Pendo, or a unified analytics platform to tag events, build cohorts, and compute risk in near real time. This is where we consistently see the patterns that matter—especially around user activation and sustained adoption.

    Signals without action won’t save a customer, so I connect the model to our systems of engagement. Through CRM integration, at-risk accounts trigger clear playbooks for CSMs and lifecycle marketers. Inside the product, in-app guides address gaps exactly where they occur—guiding users to the next best action, unblocking onboarding, or showcasing the value hidden behind underused features.

    Because not every nudge works for every segment, we treat intervention design as a product problem and run A/B testing on copy, timing, channel, and offer. We test whether a contextual tooltip outperforms an email sequence, whether a short product tour beats a knowledge base link, and which incentives accelerate onboarding without cannibalizing expansion.

    Operationally, this is a team sport. Product, CS, and marketing meet in product trios to review risk cohorts, prioritize root-cause fixes, and tune playbooks. We run a weekly risk review to turn insights into decisions, and we use monthly business reviews to connect leading indicators to lagging outcomes like retention, expansion, and NRR.

    Measurement is non-negotiable. We pair retention analysis with qualitative feedback to understand whether our interventions truly change behavior. The goal is to close the loop: when a risk cluster improves, we codify the playbook; when a tactic underperforms, we learn, adjust, and try again. Over time, the organization builds a muscle for proactive, data-informed customer health management.

    If you’re getting started, begin by instrumenting events tied to value moments, define a simple health score, and stand up a basic alerting workflow. Pilot one or two interventions, measure lift, and iterate. Within a single quarter, you’ll have enough signal to prioritize product improvements and scale the practices that reliably reduce risk.

    Churn rarely surprises teams that listen to their data and respond in real time. With disciplined analytics, thoughtful in-product guidance, and tight alignment across CS and product, we can move from reacting to predicting—and keep more customers succeeding with far less effort.


    Inspired by this post on Amplitude – Perspectives.


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  • I Built a ‘Pendo Wrapped’ in 10 Minutes with Pendo MCP to Boost Adoption and Delight Users

    I Built a ‘Pendo Wrapped’ in 10 Minutes with Pendo MCP to Boost Adoption and Delight Users

    I set out to create a lightweight, high-impact “Pendo Wrapped” experience for our users—and I did it in under 10 minutes with Pendo MCP. As a VP of Product Management, I’m constantly looking for fast, pragmatic ways to turn product insights into moments that drive engagement. This experiment was about transforming raw analytics into a concise, celebratory year‑in‑review that motivates customers to explore more value. When I say “Pendo Wrapped,” I mean a simple, narrative-style summary of usage highlights: what got adopted, which moments mattered, and where value showed up most clearly. Framed well, that story reinforces product‑led growth by reminding users why they chose us, nudging them toward the next best action, and strengthening activation and retention without heavy development work. My approach was straightforward: define a clear objective (celebrate milestones and prompt the next step), choose a focused set of metrics (adoption, engagement, and activation), and target relevant segments. Then I layered the narrative on top of existing analytics using in‑app guides and product tours to deliver the experience where it matters most—inside the product. The reason it took minutes, not hours, is that Pendo MCP let me work with what we already had—segments, saved reports, and proven guide templates—so I could spend time on the story, not the scaffolding. No code, minimal configuration, and a crisp call to action made it feel polished without being heavy. Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement. If you want to replicate this quickly, start by selecting one user segment and three metrics that matter to them, write a two‑sentence narrative that connects those metrics to outcomes, and ship a short in‑app guide with a single, purposeful CTA. That’s enough to deliver a personalized year‑in‑review feel and spark immediate exploration—no new infrastructure required. What surprised me most was how a small, story‑driven touch created outsized alignment across customers and internal teams. It turned analytics into advocacy, reminded our users of the value they’re already getting, and opened the door to deeper adoption. If you’re pursuing product‑led growth, a fast “Pendo Wrapped” is one of the highest‑leverage experiments you can run this week.

    Inspired by this post on Pendo – Perspectives.


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  • 4 Proven Ways to Keep Employees Informed and Engaged—from Onboarding to Lasting Adoption

    4 Proven Ways to Keep Employees Informed and Engaged—from Onboarding to Lasting Adoption

    Keeping employees informed and engaged isn’t just a communications challenge—it’s a product challenge. When we treat internal tools like products with clear activation moments, measurable outcomes, and continuous discovery, adoption moves from hope to habit. Over the years, I’ve seen small changes in how we onboard, communicate, and measure compound into dramatically higher engagement, better compliance, and faster time-to-value.

    “How to improve onboarding, compliance, and internal communications within your employee tools.” That question guides my approach end to end—from the moment someone logs in for the first time to the day they become an expert, championing best practices across their team.

    First, I personalize onboarding to accelerate user activation. I map the critical first actions and design a lightweight sequence of product tours and in-app guides that surfaces only what matters right now. Progressive disclosure, clear UX writing, and thoughtful tooltip design reduce cognitive load. I measure time-to-first-value, A/B test checklist microcopy to remove friction, and use Intercom or Pendo to deliver contextual walkthroughs by role, location, and permission level. Amplitude analytics helps me validate that the guided path leads to the intended activation event and sustained usage.

    Second, I make compliance effortless and measurable. Instead of long trainings, I embed micro-learnings and policy nudges directly in the flow of work, with just-in-time prompts and short, scenario-based confirmations. I segment by role to avoid alert fatigue and localize where regulations require nuance. Completion rates, quiz accuracy, and time-to-complete are tracked alongside qualitative feedback. When compliance messaging underperforms, I run A/B testing on tone, timing, and format, then iterate until adherence is both higher and faster.

    Third, I orchestrate internal communications as lifecycle messaging—not announcements. Employees get targeted release notes, role-specific tips, and in-app reminders aligned to their stage: new, adopting, proficient, or champion. I avoid channel sprawl by making the primary source of truth available in the product, then reinforcing it via email or chat only when necessary. CRM integration and audience rules ensure relevance, while a champions network and office hours create human touchpoints that deepen trust and accelerate adoption.

    Fourth, I close the loop with analytics and continuous discovery. I instrument key events and run retention analysis to understand which behaviors predict long-term engagement. I look at cohorts before and after a new guide or product tour, and I compare lift in user activation and feature adoption over 14-, 28-, and 90-day windows. Amplitude analytics provides the behavioral picture; surveys, interviews, and passive feedback widgets explain the why. Together, these inputs power a product-led growth approach for internal tools—observable, repeatable, and improvable.

    When teams ask where to start, I pilot one persona, one workflow, and one high-value outcome. I define the activation event, instrument it, launch a single targeted in-app guide through Pendo or Intercom, and A/B test the onboarding microcopy. Two weeks later, I review retention cohorts and completion data, talk to users, and either scale the pattern or iterate. That cadence builds credibility quickly because it ties every communication to a measurable result.

    The payoff is tangible: faster onboarding, higher compliance, clearer internal communications, and employees who feel supported rather than overwhelmed. With disciplined messaging, smart instrumentation, and ongoing discovery, we can turn internal tools into catalysts for performance—and transform engagement from a campaign into a culture.


    Inspired by this post on Pendo – Best Practices.


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  • Unlock Travel & Hospitality Growth: Product Benchmarks and Metrics Top Teams Rely On

    Unlock Travel & Hospitality Growth: Product Benchmarks and Metrics Top Teams Rely On

    I lead product teams building travel and hospitality experiences, and one lesson keeps repeating: companies that measure what matters move faster. Benchmarks turn gut feel into grounded product strategy, making it clear where activation, conversion, and retention are underperforming—and where we can unlock outsized growth.

    Discover exclusive data and strategies from our Product Benchmark Report. Compare the travel and hospitality industry’s performance across key product metrics.

    When I evaluate a product line, I start with a simple model: attract, convert, delight, and retain. For travel and hospitality specifically, I focus on search-to-book conversion, onboarding completion, first-booking activation rate, time-to-book, average booking value, cancellation rate, support contact rate, DAU/MAU stickiness, repeat booking rate, and long-term retention. These key product metrics reveal friction in discovery and checkout flows, surface pricing and inventory gaps, and quantify loyalty.

    From there, I assemble a test-and-learn plan. Using Amplitude analytics to instrument the funnel and Pendo for in-app guides and product tours, my teams design A/B testing with a clear minimum detectable effect (MDE), prioritize hypotheses, and execute rapid, weekly iterations. This is classic product-led growth: reduce cognitive load in onboarding, streamline search and filter UX, clarify policies before payment, and personalize reactivation nudges to improve user activation and retention analysis.

    Benchmarks are only as trustworthy as the underlying data. I insist on strong data governance, privacy-by-design practices, and clear event taxonomies so that insights remain reliable across quarters and across markets. That foundation keeps our decisions defensible with stakeholders and regulators while accelerating delivery.

    Finally, we translate insights into action with crisp product roadmapping and sprint planning. Cross-functional product trios align OKRs to the biggest benchmark gaps, and we review progress in weekly performance rituals so every experiment ladders up to strategy. This cadence helps teams stay empowered and keeps leadership focused on outcomes, not output.

    If you’re building in travel and hospitality, use these benchmarks as your starting line and your ongoing scorecard. Calibrate targets against peers, double down on what moves the needle, and let the data guide bold, customer-centered bets. When teams rally around meaningful metrics, momentum compounds.


    Inspired by this post on Amplitude – Perspectives.


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