Month: October 2025

Urgent Alert: Spot Fraudulent Job Offers Impersonating Pendo—and Protect Your Career

In my role leading product management, I take brand trust and cybersecurity seriously—especially when it affects people’s livelihoods. Over the past few weeks, I’ve seen a troubling uptick in brand impersonation and social engineering targeting candidates. It’s a reminder that protecting our community isn’t just a technical problem; it’s a product management leadership and stakeholder management responsibility.

We want to warn you about recent instances of fraudulent job offers purporting to be from Pendo and/or its affiliate companies.

If you receive an unexpected outreach claiming to be from Pendo with a fast-track offer, requests for payment, or a push to move conversations to informal channels, treat it as a red flag. Scammers often spoof logos, clone profiles, and use vague role descriptions to create urgency. Their goal is to extract personal data, money, or access—classic social engineering tactics that undermine data governance and privacy-by-design principles.

Here’s how I advise candidates to protect themselves while keeping their job search momentum. Validate every opportunity through the company’s official careers page and confirm the recruiter’s identity through corporate channels. Check that email addresses and domains match publicly listed corporate information, and be wary of communication conducted exclusively through messaging apps. Never pay fees, buy equipment up front, or share sensitive data like Social Security numbers or banking information before a formal, verified offer is in place.

If something feels off, pause and verify. Contact the company via the channels listed on its website, ask for a video meeting with the recruiter using an official corporate account, and request written details on the role and interview process. If it’s fraudulent, report it to the company, the platform where the outreach occurred, and—when appropriate—local authorities. Acting quickly helps with threat detection and response and protects other candidates from harm.

From a product and security perspective, this is a cross-functional issue that benefits from AI risk management discipline. Strong signals include clear public guidance on recruiting practices, a dedicated reporting mailbox for suspected scams, and hardened email authentication (SPF, DKIM, DMARC). Pair these with privacy-by-design reviews for hiring workflows, recruiter verification checklists, and ongoing education for talent teams. These measures reduce attack surface while reinforcing brand integrity.

If you believe you’ve shared information with a fraudulent recruiter, take immediate steps: change any reused passwords, enable two-factor authentication, place fraud alerts or freezes with credit bureaus as appropriate, and monitor accounts for suspicious activity. Document all communications; they can help security teams and platforms act faster.

Recruitment fraud is emotionally taxing and can erode confidence in the process. Don’t let scammers slow your momentum. Stay vigilant, verify before you trust, and share this warning so others can avoid similar traps. If you’re ever unsure about a message that appears to come from Pendo, pause, validate through official channels, and prioritize your safety first.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Pendo’s Summer Release: How I Reimagine Onboarding, Support, and Expansion in the SaaS + AI Era

I’ve been reflecting on How Pendo’s Summer Release reimagines onboarding, support, and expansion in the SaaS + AI era, and it resonates deeply with the product-led playbooks my team and I use every day. The core promise is simple and powerful: “These three best practices aren’t new, but how you achieve them is.” That framing captures the shift I see across high-performing product organizations—same outcomes, radically upgraded execution through AI, in-app experiences, and unified analytics.

For onboarding, I prioritize accelerating user activation with clear product tours, in-app guides, and great UX writing that removes cognitive load. The difference now is how precisely we personalize these moments: segmentation driven by product usage, CRM integration, and experiments (A/B testing with a disciplined minimum detectable effect) help us craft paths that meet users where they are. When onboarding is instrumented this way, it becomes a scalable engine for product-led growth rather than a one-time setup task.

Support is undergoing an equally meaningful transformation. Contextual, in-app help combined with agentic AI can diagnose issues, surface relevant knowledge, and guide users without forcing channel switches. I’m bullish on this, but only when it’s anchored in privacy-by-design, AI risk management, and strong data governance—trust is the prerequisite for any customer support AI strategy. When done right, support shifts from reactive ticket resolution to proactive value delivery.

Expansion, to me, is the earned outcome of consistent product value. In the SaaS + AI era, we can use unified analytics to identify readiness signals—feature adoption, outcomes achieved, and time-to-value—and trigger timely, ethical nudges in-app. The best motions align offers with real customer milestones, whether that’s consumption SaaS pricing upgrades, role-based add-ons, or advanced capabilities unlocked through demonstrated need. This is product-led growth at its most customer-centric.

Underpinning all three motions is measurement discipline. I push for a unified analytics platform that ties together behavioral data, retention analysis, funnels, and cohorts with downstream CRM integration. That allows product trios to make fast, informed decisions and connect activation, support efficiency, and expansion to business outcomes. Whether your stack includes Pendo, Amplitude analytics, or custom pipelines, the principle is the same—one source of truth that informs action.

Execution matters as much as strategy. Empowered product teams working in tight product trios can ship small, valuable increments, run clean experiments, and learn faster than the market shifts. Strong stakeholder management and clear product roadmapping keep leadership aligned on outcomes vs output OKRs, so we’re funding what works and pruning what doesn’t. In my experience, this operational rigor is what turns promising ideas into durable competitive differentiation.

If you’re looking to operationalize these ideas, start by defining activation and expansion milestones that map to your value proposition. Instrument your in-app guides and product tours to support those milestones, and commit to an experimentation cadence with well-defined MDE. Layer in agentic AI carefully—pilot in the support surface where context is rich and stakes are clear—and enforce privacy and governance from day one. Finally, close the loop with unified analytics so every improvement compounds.

Pendo’s Summer Release highlights a broader reality: our industry isn’t inventing new destinations, we’re modernizing the routes. Onboarding, support, and expansion remain the pillars—but AI, in-app experiences, and integrated data make them smarter, faster, and more human. That’s the shift I’m leaning into—and the one customers feel immediately.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
The Only 3 Dashboards Product Executives Actually Use to Drive Outcomes, Alignment, and Growth

I’ve learned the hard way that more charts don’t equal more clarity. One challenge that comes with this is knowing what matters at the right level of leadership. Executives everywhere are busy, and they don’t need the nitty-gritty details to do their jobs well. When I’m operating at the VP level, I rely on just three dashboards that give me fast signal, reduce noise, and keep teams aligned to outcomes—not output.
These dashboards sit on top of a unified analytics platform that connects product analytics (Amplitude analytics or Pendo), CRM and revenue data (e.g., HubSpot), billing, and support signals. Consistent definitions, data governance, and outcomes vs output OKRs ensure we’re making decisions with confidence, not gut feel. The goal is simple: a shared, executive-ready view that ties product strategy to business impact.
Dashboard 1: Outcomes and Strategy Alignment. This is the north star view I use to orient the company. It highlights ARR, NRR, and GRR trends; progress against our outcomes vs output OKRs; our product-led growth funnel; and our primary value proposition metric (e.g., activation-to-time-to-value). I include a 12-month view with quarter-over-quarter deltas, a short written narrative, and the top three strategic bets we’re funding. In board management and QBRs vs OKRs discussions, this keeps focus on what we achieved, what moved, and what we’re changing next.
Dashboard 2: Customer Value, Adoption, and Retention. This is where retention analysis meets product discovery. I track activation rate, time-to-value, feature adoption cohorts (from Amplitude analytics or Pendo), retention curves by segment, and expansion vs contraction signals. Leading indicators include NPS and CES alongside qualitative themes from support and sales. I also monitor funnel drop-offs and in-app guides or product tours performance to see where users get stuck. The intent is to connect behavior to revenue so we can prioritize changes that actually improve customer outcomes.
Dashboard 3: Execution Health and Quality. This helps me assess whether our operating system is working. I look at delivery predictability against product roadmapping and sprint planning, cycle time and throughput, escaped defects, incident volume, and MTTR. I also review experiment velocity and A/B testing readiness (including minimum detectable effect) to ensure we’re learning at pace. Resource allocation across strategic initiatives and a clear risk register support proactive stakeholder management.
I review these dashboards weekly with my product trios and monthly with cross-functional leaders, then synthesize a concise narrative for the executive team and the board. Each dashboard is a decision engine: it has an owner, a single source of truth, clear thresholds, and a list of next actions. By grounding conversations in the same views, we reduce back-and-forth and keep momentum high.
A few implementation rules have served me well: keep the signal dense and the visuals simple; lock metric definitions and ownership; avoid vanity metrics; and instrument privacy-by-design from the start. When data is trustworthy and the story is tight, teams focus on the right problems and progress compounds.
If you find yourself wading through dozens of reports, try consolidating to these three executive dashboards. You’ll spend less time arguing about the data and more time driving product-led growth, accelerating alignment, and delivering customer value at scale.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Build the Cake, Then the Frosting: 3 Elements of a High‑Performing AI Strategy That Wins

Over the past few years leading product at HighLevel, I’ve watched too many teams rush to demo flashy agents before they’ve built a reliable foundation. The metaphor I use in every AI roadmap review still hits home: “Think of AI readiness as a three-layer cake. Most companies are trying to build the fancy frosting (the agent interface) without bothering to bake the actual cake underneath.” If we want durable impact, we have to bake first, frost later.

When I design an AI Strategy, I anchor on three elements that map directly to that cake: a data and instrumentation foundation, a governance and risk layer, and finally the agent experience itself. This sequence isn’t theory—it’s how we de-risk delivery, accelerate product-market fit, and create competitive differentiation without compromising trust.

Layer 1 — Data and instrumentation: The base of the cake is clean, well-instrumented data flowing through a unified analytics platform. I start with a clear event schema, rigorous data quality checks, and tight CRM integration so we can connect outcomes to users, accounts, and journeys. Privacy-by-design is nonnegotiable: we minimize PII, define retention, and ensure consent flows are explicit. With this in place, gen ai features have the context they need—retrieval works, grounding holds, and feedback loops from production inform continuous improvement.

On top of that, I build measurement in from day one: activation, retention, task success, latency, and satisfaction. Every AI interaction is observable. We run A/B testing with a well-defined minimum detectable effect, pair quant with qualitative review, and feed human-in-the-loop judgments back into ranking and prompt libraries. This is how we avoid “demo-ware” and deliver real, repeatable value.

Layer 2 — Governance and risk: Before scaling, I formalize AI risk management and data governance. That includes model evaluation against safety and quality thresholds, red-teaming for jailbreaks, and threat detection and response for prompt injection and data exfiltration. We establish policy for model and provider selection, versioning, and rollback; we log prompts, responses, and decisions for auditability; and we define escalation paths when the system is unsure. These controls don’t slow us down—they create the confidence needed for faster iteration and board management alignment.

I also align legal, security, and product early on a taxonomy of risks—bias, hallucinations, privacy, IP leakage—so we can write tests and guardrails once and reuse them across features. The result is fewer surprises in customer pilots and a far smoother path through enterprise procurement.

Layer 3 — The agent experience: Only now do we invest in the frosting—the agent interface and workflows. Here I focus on clear jobs-to-be-done, crisp UX writing, and transparent system behavior. We design agentic AI flows that show reasoning steps when helpful, ask for clarification when confidence is low, and gracefully hand off to humans in customer support scenarios. Product tours, in-app guides, and tooltips reduce the learning curve and accelerate user activation.

Crucially, we measure the interface, not just the model. Agent Analytics tracks intents, tool use, fallbacks, and user corrections so we can tune prompts, tools, and policies. This closes the loop from experience back to data and governance, and it directly informs product roadmapping and sprint planning. When the cake is baked this way, go-to-market becomes easier: we can prove ROI with hard numbers, fine-tune pricing, and scale adoption with product-led growth tactics.

If your AI roadmap feels stuck, start with an honest readiness audit against these three elements. Shore up instrumentation and data pipelines, codify governance, then refine the agent interface with real user telemetry. Bake first. Frost last. That’s how we ship AI that customers trust—and keep winning after the first demo high fades.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Urgent Alert: Spot Fraudulent Job Offers Impersonating Pendo—and Protect Your Career

In my role leading product management, I take brand trust and cybersecurity seriously—especially when it affects people’s livelihoods. Over the past few weeks, I’ve seen a troubling uptick in brand impersonation and social engineering targeting candidates. It’s a reminder that protecting our community isn’t just a technical problem; it’s a product management leadership and stakeholder management responsibility.

We want to warn you about recent instances of fraudulent job offers purporting to be from Pendo and/or its affiliate companies.

If you receive an unexpected outreach claiming to be from Pendo with a fast-track offer, requests for payment, or a push to move conversations to informal channels, treat it as a red flag. Scammers often spoof logos, clone profiles, and use vague role descriptions to create urgency. Their goal is to extract personal data, money, or access—classic social engineering tactics that undermine data governance and privacy-by-design principles.

Here’s how I advise candidates to protect themselves while keeping their job search momentum. Validate every opportunity through the company’s official careers page and confirm the recruiter’s identity through corporate channels. Check that email addresses and domains match publicly listed corporate information, and be wary of communication conducted exclusively through messaging apps. Never pay fees, buy equipment up front, or share sensitive data like Social Security numbers or banking information before a formal, verified offer is in place.

If something feels off, pause and verify. Contact the company via the channels listed on its website, ask for a video meeting with the recruiter using an official corporate account, and request written details on the role and interview process. If it’s fraudulent, report it to the company, the platform where the outreach occurred, and—when appropriate—local authorities. Acting quickly helps with threat detection and response and protects other candidates from harm.

From a product and security perspective, this is a cross-functional issue that benefits from AI risk management discipline. Strong signals include clear public guidance on recruiting practices, a dedicated reporting mailbox for suspected scams, and hardened email authentication (SPF, DKIM, DMARC). Pair these with privacy-by-design reviews for hiring workflows, recruiter verification checklists, and ongoing education for talent teams. These measures reduce attack surface while reinforcing brand integrity.

If you believe you’ve shared information with a fraudulent recruiter, take immediate steps: change any reused passwords, enable two-factor authentication, place fraud alerts or freezes with credit bureaus as appropriate, and monitor accounts for suspicious activity. Document all communications; they can help security teams and platforms act faster.

Recruitment fraud is emotionally taxing and can erode confidence in the process. Don’t let scammers slow your momentum. Stay vigilant, verify before you trust, and share this warning so others can avoid similar traps. If you’re ever unsure about a message that appears to come from Pendo, pause, validate through official channels, and prioritize your safety first.

Inspired by this post on Pendo – Perspectives.

October 25, 2025
4 Costly Misconceptions About AI Agents—and What Product Leaders Must Do Instead

Building AI agents looks deceptively simple right now. After leading multiple agentic AI initiatives, I’ve learned that the difference between a demo and a dependable product comes down to disciplined product discovery, ruthless scoping, and a clear AI Strategy that aligns with business outcomes. Here are four common misconceptions I correct early with stakeholders—and the practices I use to avoid expensive detours.

Misconception 1: “An LLM plus a few prompts is a production-ready agent.” In reality, production-grade agents require orchestration and rigor: tool-use and retrieval, memory design, state management, deterministic fallbacks, and continuous evaluation. I instrument Agent Analytics from day one to trace tool calls, latency, error codes, and cost per task; then I use A/B testing with a clear minimum detectable effect (MDE) to validate improvements before broad rollout. This is where product roadmapping and sprint planning matter—sequencing capabilities so we avoid building speculative features that don’t move outcomes.

Misconception 2: “More autonomy is always better.” The right autonomy level is contextual and risk-adjusted. For high-stakes workflows, I design for human-in-the-loop and role-based guardrails, grounded in privacy-by-design and data governance. Policies like least-privilege access, audit logs, and reversible actions reduce operational risk while still delivering leverage. In practice, this hybrid approach also controls cost: narrower scopes, clearer prompts, and bounded tool access reduce hallucination surface area and improve reliability—key to AI risk management.

Misconception 3: “If we build it, users will adopt it.” Adoption is earned with thoughtful onboarding and in-app guidance, not promised by a feature launch. I pair agent launches with targeted product tours, contextual tooltips, and progressive disclosure to drive user activation and product-led growth. 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. Whether you use Pendo or a comparable solution, the principle stands: instrument the experience, run experiments, and iterate quickly based on evidence, not intuition.

Misconception 4: “Security, compliance, and governance can wait.” Deferring controls is a false economy. I embed AI risk management from day zero: prompt injection defenses, PII redaction, DLP, grounding and citation strategies, and threat detection and response. Clear data retention policies, vendor diligence, and model evaluation standards keep leadership, security, and legal aligned. This is the crux of building trust—and it’s far easier to design up front than to retrofit under pressure.

How I execute in practice: start with a tightly framed use case tied to a measurable outcome; define outcomes vs output OKRs; build a slim vertical slice to validate feasibility; instrument Agent Analytics from the first commit; ship behind feature flags; and operationalize learning loops across support, success, and GTM. The result is a durable path to product-market fit for agentic AI—one that compounds learning while minimizing blast radius.

The leaders who win with AI agents won’t be the ones who move fastest in a demo. They’ll be the ones who manage risk transparently, learn in public with their users, and turn continuous insight into competitive differentiation. If you’re planning your next agent milestone, align the roadmap to outcomes, treat governance as a feature, and make adoption your North Star.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Design Four High-Impact Lifecycle Journeys with Pendo Orchestrate to Drive Retention

I’ve spent my career building product-led growth motions that deliver value fast and build durable retention. The most consistent pattern I’ve seen is simple: When we orchestrate timely, contextual guidance inside the product, customers discover value sooner, adopt core workflows more completely, and return more often. That’s exactly where Pendo Orchestrate shines for my team.

From first click to lifelong retention, you’ll deliver the right message at the exact right time, every step of the way. With Pendo Orchestrate, you can design those kinds of moments with intention. And in this blog, we’ll show you how.

At a high level, I map the customer lifecycle into four journeys—onboarding, activation, retention, and expansion—and align each to clear outcomes. Using targeted in-app guides and product tours, behavioral triggers, and segment-specific messaging, I can optimize each stage without overwhelming users. What follows is how I approach each journey to maximize time-to-value and retention.

Onboarding: I design progressive onboarding that adapts to a user’s role and first-run actions. Instead of a single, long product tour, I use short, contextual nudges that appear exactly when a user reaches a relevant screen or performs a key event. This reduces cognitive load, shortens time-to-value, and sets up a reliable path to initial success. When needed, I A/B test different sequences and measure impact on activation rate to ensure we’re improving the real user experience, not just adding more guidance.

Activation and habit-building: After first value, I focus on reinforcing the behaviors that correlate with long-term retention. Here, lightweight tooltips, celebratory moments when users reach the “aha” action, and just-in-time prompts for adjacent features help form habits. I track cohort-level activation metrics and use retention analysis to see whether these nudges translate into sustained product usage. If a segment stalls, I adjust copy, timing, or the sequence to better match user intent.

Retention and re-engagement: Not every customer stays on a steady path. For at-risk cohorts—users who haven’t completed a critical workflow or whose usage is declining—I trigger helpful, empathetic in-app guides that remove friction and offer a direct path back to value. I also solicit lightweight feedback to understand obstacles. The goal isn’t to interrupt; it’s to make it effortless to recover momentum.

Expansion and upsell: When users demonstrate readiness—mastery of core features, frequent usage, or role-based signals—I introduce advanced capabilities with targeted product tours and clear value propositions. Timing is everything; I prefer unobtrusive prompts that appear at the exact moment their workflow benefits from an upgrade. By matching message to milestone, expansion feels like a service, not a sell.

Operationalizing these journeys starts with crisp definitions of success (activation, adoption depth, and retention), thoughtful segmentation, and a cadence of experimentation. I keep the loop tight: instrument key events, launch small, measure outcomes, and iterate. Over time, the orchestration becomes a durable system—consistently delivering the right guidance to the right user at the right moment, and continuously compounding product impact.

If you’re looking to scale product-led growth, these four journeys provide a pragmatic blueprint. Start with the stage that’s hurting most (often onboarding), prove the lift, then expand. As outcomes improve, your users feel supported, your product experience feels intuitive, and your business earns the retention and expansion it deserves.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
WTF is MCP? The powerful protocol giving enterprise AI agents real-world autonomy

I get asked this constantly by boards, CIOs, and product teams: WTF is MCP, and why does it matter for enterprise AI? Here’s my straightforward take from the trenches of rolling out agentic AI across complex, regulated environments—and why it changes how we design, govern, and scale autonomous capabilities.

“Model Control Protocol gives your AI agents arms and legs to go do stuff with your data.” That framing resonates because it’s both simple and accurate. MCP turns passive “chatbots” into active agents that can safely take action within defined guardrails.

In practice, MCP is the connective tissue between models and the tools, systems, and workflows we trust. It standardizes how agents request permissions, execute tasks, and report outcomes—so enterprises can move from demos to durable operations. The benefit isn’t just autonomy; it’s autonomy with accountability, aligned to our AI Strategy and data governance obligations.

When I pilot agentic AI in production, I start with a narrow scope: which systems the agent touches (for example, CRM integration via HubSpot), what actions it can take (read, write, or propose), and what evidence it must log (inputs, outputs, and approvals). That discipline keeps us compliant with privacy-by-design while unlocking real business impact.

Great MCP use cases emerge where read-write actions compress time-to-value. Think: pulling Amplitude analytics cohorts to personalize outreach, auto-generating Pendo in-app guides based on feature adoption, or triggering customer support workflows with predefined playbooks. Each action is observable, reversible, and measured—because in the enterprise, repeatability beats novelty.

From a product management leadership perspective, I treat MCP-enabled agents like any other product surface. We define clear outcomes, not outputs: success rate per task, mean time to resolution, quality score, and safety incidents. We validate uplift with A/B testing and a minimum detectable effect (MDE) before scaling. Then we feed results into an Agent Analytics dashboard, just as we would for product-led growth funnels.

Governance is where MCP earns trust. I enforce least privilege, time-boxed credentials, environment isolation, and tamper-evident audit logs. Every tool call is tied to a business purpose, owner, and SLA. We integrate with existing threat detection and response processes so cybersecurity teams see the same telemetry they’re used to—no shadow AI, no surprises.

There’s also an adoption playbook that works: start with a contained domain, ship a sandboxed agent, require human-in-the-loop approvals, then progressively relax controls as accuracy and alignment improve. Document the boundaries in plain language, and instrument everything from day one. This is how we de-risk AI risk management while accelerating impact.

The most exciting shift is cultural: teams move from asking “Can the model do this?” to “What outcomes should the agent own—and what guardrails make that safe?” That mindset unlocks empowered product teams, clearer ownership, and faster iteration. MCP is simply the operational backbone that lets those choices stick.

If you’re evaluating where to start, pick one workflow with high frequency, clear rules, and measurable outcomes. Wire it to MCP with tight scopes, ship it to a friendly cohort, and learn aggressively. Autonomy isn’t the end goal—reliable, governed value is. MCP just makes that scalable.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
4 Proven Ways GTM Teams Drive Explosive Growth with Pendo’s HubSpot Integration

In my role leading product management, I’ve learned that the most reliable path to product-led growth is aligning product signals with the systems our go-to-market teams use every day. That’s exactly where Pendo’s HubSpot integration shines—by merging behavioral insights with CRM context so sales, marketing, customer success, and product move in lockstep.

See how customer behavioral data can help sales, marketing, customer success, and product teams create a better, more engaging customer experience.

First, I use the integration to create a single source of truth that blends in-app behavior with account and contact data. When product usage, feature adoption, and intent signals flow into HubSpot, lead scoring becomes smarter, pipeline quality improves, and our go-to-market strategy gets more precise. Reps prioritize the right accounts, marketing tunes messaging to demonstrated needs, and we operate as a unified analytics platform instead of scattered tools.

Second, I activate lifecycle journeys directly from HubSpot using in-app guides and product tours. By targeting experiences based on CRM stage or persona, onboarding accelerates, trial conversion increases, and time-to-value drops. The ability to personalize onboarding without engineering work gives marketing and customer success a powerful lever to deliver exactly the right guidance at the right moment.

Third, I orchestrate customer success playbooks that reduce churn and expand revenue. Health scoring improves when retention analysis is informed by real product usage, not just survey sentiment. When usage dips below a threshold, HubSpot workflows trigger save-plays; when product engagement surges, we operationalize expansion motions across self-serve upgrades and account-based upsell. The result is a tighter feedback loop between product adoption and revenue outcomes.

Fourth, I close the loop between sales, product, and marketing to refine product positioning and roadmap priorities. Signals from Pendo in HubSpot highlight which features correlate with win rates and renewals, so we double down on the value proposition that actually converts. Those same insights inform targeted campaigns, sharper messaging, and a continuous learning cycle across GTM and product teams.

To make this work in practice, I start with clear event taxonomies, privacy-by-design data governance, and tightly scoped use cases that we can measure within a quarter. We iterate with small A/B tests, compare outcomes to baselines, and socialize wins across sales, marketing, and customer success to build momentum. The integration becomes more than a data pipe—it’s an operating system for coordinated growth.

When product signals meet CRM workflows, teams stop guessing and start executing with confidence. That’s the power of Pendo’s HubSpot integration: it operationalizes product-led growth across the entire customer journey, from first touch to expansion.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Pendo Admin Power Checklist: 4 Proven Practices to Drive Adoption, Clarity, and Trust

Overseeing complex platforms like Pendo is where product leadership comes to life. I rely on four disciplined practices to keep our instrumentation clean, our in-app experiences on-brand, and our analytics credible enough to guide high-stakes decisions. If you’re setting up or tuning your instance, this checklist will help you build trust with stakeholders and accelerate product-led growth.

Learn best practices that every Pendo admin should know.

1) Standardize tagging and taxonomy. I start by defining a clear naming convention for feature tags, page tags, and track events (for example, feat:[area]:[action]). This taxonomy lives in a shared document, aligns to our product roadmapping and sprint planning, and includes ownership, definitions, and “do/don’t” examples. In practice, this reduces duplicates, improves segment reliability, and makes funnels, paths, and retention analysis far more actionable. I also schedule quarterly hygiene to retire stale tags and revalidate critical measures tied to OKRs.

2) Segment deliberately and manage access with intention. Meaningful segments—role, lifecycle stage, plan tier, and account health—unlock precise targeting for in-app guides and stronger insights. On the admin side, I enforce least-privilege access with SSO/SCIM, audit changes to tags and guides, and keep visitor and account ID strategies consistent across environments. This combination strengthens data governance and privacy-by-design while reducing operational risk.

3) Operationalize a guide lifecycle. In-app guides are powerful, but only when they’re coherent and governed. I maintain a style system and reusable templates for tooltips, walkthroughs, onboarding checklists, and the Resource Center so the UX feels intentional, not noisy. Every guide goes through QA in staging, frequency capping, sunset dates, and an owner accountable for outcomes. I measure impact with clear success metrics—adoption lift, funnel completion, or onboarding time—to ensure guides serve the product strategy, not just add UI clutter.

4) Build an analytics cadence that leaders can trust. I treat Pendo as a decision system, not just a dashboard. That means SDK updates are part of our release checklist, known key events are smoke-tested after deployments, and weekly insight reviews turn funnels, paths, and retention analysis into clear actions. Where appropriate, I pair experiments with A/B testing guardrails and tie findings back to outcomes vs output OKRs. Finally, I publish a simple “what we learned” summary to keep stakeholders aligned and focused on the next best move.

Your 5‑minute checklist: confirm a shared tagging taxonomy; align segments to roles, lifecycle, and plans; apply least-privilege access and SSO/SCIM; standardize guide templates and QA; set metrics for every guide; and establish a recurring analytics review tied to OKRs. With these four practices in place, your Pendo instance becomes a flywheel for onboarding, product adoption, and continuous discovery—without sacrificing governance or customer trust.

If you’re scaling quickly, start small: pick one product area, instrument it cleanly, launch a targeted in-app guide, and run a focused funnel review the following week. Momentum builds when teams see crisp insights and customers feel helpful guidance at just the right moment.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
SXM vs. the Rest: My High-Impact Playbook for Today’s Software Experience Tools and PLG

I spend a lot of time reviewing how customers move through our product and where their momentum stalls or accelerates. The tools you use to build and optimize software experiences are evolving. That simple truth reshapes our strategy every quarter, from the analytics we trust to the in-app touchpoints we design and the experiments we run to improve product-led growth.

When I say SXM, I’m talking about a comprehensive software experience management approach that unifies analytics, experimentation, in-app guides, messaging, and feedback loops. SXM vs. the rest is the real-world choice between an integrated platform and a patchwork of point solutions. I’m not married to one path; I’m obsessed with outcomes—speed to learning, lower friction for teams, and compounding retention gains.

The foundation is a unified analytics platform and a clean, consistent event schema. From there, I pair behavior analytics with in-app orchestration: tools like Amplitude analytics for deep behavioral insights and Pendo for targeted in-app guides, product tours, and contextual nudges. I instrument rigorous A/B testing with a clearly defined minimum detectable effect (MDE) and follow through with retention analysis to validate whether an uplift sticks beyond vanity metrics. Great UX writing and thoughtful tooltip design often make the difference between a nudge that converts and a prompt that gets ignored.

I choose between best-of-breed and platform consolidation using first principles decision making. If a point solution unlocks a capability that meaningfully advances our product discovery or activation work, I adopt it. If multiple tools converge on the same points of parity, I consolidate to streamline governance, reduce integration overhead, and accelerate delivery. The goal is not more software; it’s faster, clearer learning that informs product positioning and drives customer value.

AI now sits at the center of this stack. I apply gen ai and agentic AI to accelerate hypothesis generation, automate cohort detection, draft UX microcopy, and suggest next-best actions inside the product. That said, AI risk management, privacy-by-design, and data governance are non-negotiable. I won’t trade trust for tempo; we can have both by putting guardrails around training data, access controls, and evaluation criteria.

Operating rhythm matters as much as tooling. Product trios set outcomes vs output OKRs, then test and iterate—starting with onboarding, activation, and the moments that trigger value realization. We build in measurable in-app guides, run A/B testing with tight feedback cycles, and keep our go-to-market strategy aligned so every nudge, message, and feature release supports product-led growth.

My playbook is simple: clarify the outcomes, instrument the journey end-to-end, choose the smallest toolset that can answer the biggest questions, and learn faster than the market. Map critical paths, standardize taxonomy, and make experimentation a habit—not a project. Then double down where signal is strongest and retire anything that adds minimal lift to retention or expansion.

SXM isn’t a buzzword; it’s a disciplined way to build software that feels intuitive, responsive, and valuable from the first click. With the right blend of analytics, in-app guidance, experimentation, and AI—grounded in strong product management leadership—we can turn insights into momentum and momentum into durable growth.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
The 5 Stages of Software Experience Maturity: What to Fix First to Unlock Growth

I’ve led product teams through chaotic launches, painful plateaus, and breakout growth, and one truth keeps showing up: software wins when the experience is intentionally designed, measured, and continuously improved. To make that work repeatable, I rely on a simple maturity framework that aligns our product strategy, analytics, and in-app experience work across the organization. Find out where you stand—and what to fix first—with this maturity framework. Why “software experience” and not just “features”? Because activation, adoption, and retention depend on how clearly users understand value in their first sessions, how seamlessly they complete key workflows, and how consistently they succeed over time. That’s where empowered product teams, product-led growth, and outcomes vs output OKRs come together to create durable results. Stage 1 — Ad Hoc: At this level, teams ship features without a clear sense of who benefits, how success is measured, or how UX writing and onboarding shape outcomes. If this is you, fix this first: define your activation events, instrument the core funnel, and write concise, in-product copy that reduces friction. Even a lightweight retention analysis will reveal where value drops off. Stage 2 — Instrumented Awareness: You’ve added basic analytics and can see signups, activations, and drop-offs, often via tools like Amplitude analytics or a unified analytics platform. What to fix first: translate raw metrics into hypotheses and prioritize a small set of A/B testing experiments. Use a minimum detectable effect (MDE) to size tests, and start tracking leading indicators tied to adoption—not vanity metrics. Stage 3 — Guided Journeys: Onboarding, in-app guides, product tours, and contextual tooltips now clarify value and reduce time-to-first-value. What to fix first: build a guided path to activation for your top two personas, then test microcopy and sequencing. Pair qualitative insights from user feedback with cohort-based retention analysis to ensure your guides create durable behavior change, not just clicks. Stage 4 — Outcome-Driven Execution: Teams set outcomes vs output OKRs, run disciplined experiments, and connect learnings to roadmap decisions. What to fix first: standardize an experimentation playbook with clear guardrails for MDE, sample sizing, and stop rules. Align quarterly bets with a value proposition narrative that ties product discovery to measurable, customer-centric outcomes. Stage 5 — Predictive and Proactive: You anticipate user needs with tailored experiences, automate nudges at the right moments, and systematize continuous discovery. What to fix first: unify data across product, support, and lifecycle channels to personalize experiences without eroding privacy-by-design. Invest in scalable governance so insights flow to product trios and forward deployed engineers quickly and safely. How to use this framework: honestly score your current stage across analytics, onboarding, guidance, experimentation, and decision-making. Then pick the single change that removes the biggest bottleneck to the next stage—often a measurement gap, not a feature gap. Make improvements visible through product roadmapping and sprint planning, and celebrate progress to reinforce empowered product teams. In practice, maturity is not a badge; it’s a habit. When we pair rigorous analytics with thoughtful in-app experiences and clear strategic outcomes, we compound learning and unlock growth. If you’re unsure where to begin, start small: instrument activation, improve one critical guide, and run one high-quality experiment. Momentum follows.

Inspired by this post on Pendo – Best Practices.

October 25, 2025