Tag: customer success

Old-School Selling Beats PLG in the AI Era: My GTM Playbook for 8‑Day Enterprise Deals

Old-school, in-person selling is having a renaissance in the AI era, and I’ve seen why up close. From leading product and go-to-market teams through hypergrowth, I keep returning to one lesson: enterprise buyers still reward the teams who show up, orchestrate change management, and own outcomes end-to-end. The tech has changed; the human dynamics haven’t.
Has the sales playbook changed in the AI era? The tools are faster and the surface area is bigger, but the core motion remains the same: “showing up” beats letting the marketplace decide. That’s why in-person enterprise rollouts still beat product-led motions, especially when the stakes include security, governance, and cross-functional adoption. You win by reducing organizational risk, not by assuming free trials will do the heavy lifting.
Great enterprise sellers collapse silos. They sell to engineers and executives in one motion, pairing deeply technical validation with crisp business narratives. In my org, that means every high-velocity pilot has a dual thread: hands-on, eval-driven proof for the builders and a value architecture for the budget owners. When those motions run in parallel, time-to-value plummets and procurement friction fades.
Selling to AI-native buyers who grew up on ChatGPT changes tempo, not fundamentals. The same seller, different tempo: 8 weeks vs. 8 business days. These buyers evaluate fast, expect clear ROI, and push for automation-first workflows. How AI-native buyers handle build vs. buy decisions comes down to build for differentiation and buy for acceleration. If you make procurement feel like product—frictionless, instrumented, and transparent—you’ll meet their bar.
Process matters, but humanity wins. Building a robust sales process that still leaves room for unscripted moments is where trust is formed. I’ll never forget the story of the rep who taught a champion’s son guitar over Zoom—an unscripted moment that cemented a partnership. The lesson: raise the floor without capping the ceiling. Equip every rep with repeatable plays, then celebrate the creative instincts that make champions out of customers.
In early GTM, why the three highest-leverage early sales hires aren’t sellers at all resonates with my experience. I prioritize a solutions engineer who can de-risk integration, a forward-deployed operator who can run the first rollout like a product manager, and a customer success lead who designs adoption paths from day zero. Together, they compress the value journey from proof to production.
Compensation design shapes your talent market. The case for outsized commission accelerators for star sellers — and the kind of person they attract is real: magnets for competitors who close complex, multi-threaded deals and thrive with ownership. But beware: why too much process narrows the kind of seller you attract. Over-script it and you filter out the very people who can navigate ambiguity with customers.
Under the hood, instrumenting the funnel from stage zero to close keeps the system honest. I track intent signals before pipeline, conversion by persona and use case, proof milestones, and time-to-value in production. The three pillars of GTM excellence for me are repeatable discovery, referenceable outcomes, and relentless enablement. And inside the leadership team, building peers who are 80% aligned, not 100% preserves healthy tension while keeping execution fast.
AI is expanding the definition of enablement—whether AI is changing what good enablement looks like isn’t a theoretical question anymore. I see world-class teams arming reps with retrieval-first knowledge bases, sandbox environments, and objection libraries that evolve weekly. Meanwhile, selling against direct and implied competitors at once is the norm: your battlecard must cover “do nothing,” internal tools, adjacent categories, and new AI entrants—while you still remember why in-person enterprise rollouts still beat product-led motions for durable adoption.
Planning horizons tighten in AI markets. How far out should a GTM leader be planning? I work a dual cadence: a rolling 6-week operating plan that’s ruthlessly tactical and a 2–3 quarter roadmap for coverage, enablement, and category storytelling. What a normal week looks like in hypergrowth blends customer time, pipeline triage, onboarding and enablement, deal engineering, and process tuning—always with one or two high-conviction bets that could bend the curve.
References: Ahead: https://www.ahead.com; Amazon: https://www.amazon.com; Anthropic: https://www.anthropic.com; Attio: https://www.attio.com; Augment Code: https://www.augmentcode.com/; Cognition: https://cognition.ai; Cursor: https://cursor.com; Dani McCabe: https://www.linkedin.com/in/danielle-mccabe/; Datadog: https://www.datadoghq.com; GitHub Copilot: https://github.com/features/copilot; HubSpot: https://www.hubspot.com; Jeremy Powers: https://www.linkedin.com/in/jeremypowers/; JPMorgan: https://www.jpmorgan.com; Matt McClernan: https://www.linkedin.com/in/mattmcclernan/; MongoDB: https://www.mongodb.com; Nicole Rettinger: https://www.linkedin.com/in/nicole-rettinger-23b20465/; Notion: https://www.notion.com; OpenAI: https://openai.com; Parag Agrawal: https://www.linkedin.com/in/paragagr/; Parallel: https://parallel.ai; Snowflake: https://www.snowflake.com; University of Chicago: https://www.uchicago.edu; Windsurf: https://windsurf.com
If you’re scaling an AI product today, pair a disciplined sales-led growth engine with the best of product-led growth: fast paths to proof, hands-on validation for builders, executive-level value mapping, and human moments that turn customers into advocates. That’s how you compress an eight-week cycle into five business days—and keep the expansion flywheel spinning.

May 21, 2026
Stop Chasing Churn: How Behavioral Analytics Powers Proactive Retention in SaaS

Churn is a lagging indicator—and by the time I see it in a dashboard, the moment to change a customer’s mind has usually passed. At HighLevel, I’ve learned that durable retention starts long before a cancellation ticket, with product-led growth habits, customer success partnerships, and a clear view of user behavior that flags risk early and often.

Stop chasing SaaS churn after it happens. Learn how proactive product and service experiences, powered by behavioral analytics, help reduce churn before users leave.

My operating model is simple: treat retention as a design problem, not a rescue mission. I anchor our strategy in behavioral analytics and retention analysis, translating leading indicators—activation milestones, time-to-first-value, depth of feature adoption, and expansion intent—into outcomes like Net Recurring Revenue (NRR) and cohort-based retention. When these inputs move in the right direction, churn becomes the exception, not the trend.

To get there, I start with rigorous journey mapping and continuous discovery. We define the exact “aha” moments that signal value realization, instrument events across the funnel, and segment cohorts by persona, plan, and use case. Tools in a unified analytics platform (e.g., Amplitude analytics or Pendo) help us pinpoint where engagement decays, which features predict stickiness, and which friction points block activation. This evidence replaces hunches and lets us prioritize the highest-leverage work.

From those signals, I build a transparent risk score that anyone can use. It blends usage momentum (DAU/WAU), core feature frequency, anomaly detection on key behaviors, billing and payment health, and support sentiment. When the score crosses a threshold, we trigger plays—inside the product and through customer success—so we’re helping users before they drift, not pleading after they’ve left.

On the product side, I favor lightweight, contextual interventions: in-app guides tailored to stalled tasks, checklists that shorten time-to-value, adaptive product tours, and tooltip design that clarifies the next best action. We A/B test these experiences with a clear minimum detectable effect (MDE), watching both local metrics (feature completion, error rate) and global metrics (activation, retention). The goal is precision—right nudge, right user, right moment—without adding cognitive load.

On the service side, we run consultative support and customer success plays keyed to the same behavioral triggers. A sudden drop in core usage may prompt a quick diagnostic call; repeated failed integrations can route to solutions engineering; stalled accounts get value reviews or QBRs focused on outcomes, not feature checklists. Because product and service draw from the same data, customers experience a single, coherent journey.

Proactive retention also depends on smart packaging and pricing. When value metrics mirror how customers win, plan boundaries reinforce the right behaviors and reduce “silent churn” caused by misaligned tiers. Outcome-based pricing and clear upgrade paths can turn potential risk into expansion rather than attrition.

Operationally, I keep a weekly retention review with product trios and customer success leaders. We walk driver trees from inputs (activation, engagement depth, support friction) to outputs (NRR, churn), review session replay where confusion spikes, and commit to small, measurable experiments. This cadence compounds learning and keeps us honest about what’s moving the needle.

If you’re starting fresh, begin with four moves: define an activation milestone tied to value; instrument the few events that prove users are on track; build a basic risk score from those events; and craft three plays—one in-product, one lifecycle message, one success outreach—triggered by that score. You’ll create a flywheel where insights power interventions, and interventions feed better insights.

Churn will always exist, but it doesn’t have to be a cliff. With behavioral analytics guiding both product and service experiences, we can make retention the natural outcome of how we build, communicate, and support—long before a customer ever thinks about leaving.

Inspired by this post on Amplitude – Perspectives.

May 18, 2026
Scaling AI-Powered Customer Experience: Cross-Org Playbooks to Drive Product Impact

Customer experience is now a core product strategy lever, not a downstream support function. In my work leading product teams, I’ve seen that the fastest path to durable growth is aligning CX strategy with product, data, and go-to-market—especially when we’re building AI-powered solutions that must scale responsibly.

Amanda Sime is the Customer Experience Strategy Lead at Amplitude. She shapes CX strategy and partners across orgs to design and scale AI-powered solutions.

That mandate captures what high-performing organizations are doing well: connecting behavioral analytics, product discovery, and customer success into a unified operating system. When CX leaders partner tightly with product and data teams, we turn insights into action—using Amplitude analytics to identify friction, journey mapping to prioritize moments that matter, and a unified analytics platform to close the loop from hypothesis to measurable outcomes.

Practically, the playbook looks like this in my teams: start with rigorous journey mapping and retention analysis to pinpoint where value realization lags; run targeted A/B testing to validate interventions; and deploy in-app guides and product tours to accelerate user activation. Layer in session replay and behavioral analytics to understand intent, then operationalize learnings into repeatable workflows that improve time-to-value and customer success. This is how we make product-led growth concrete rather than aspirational.

AI Strategy adds both leverage and responsibility. We design AI-powered experiences with privacy-by-design, clear value propositions, and eval-driven development so we can measure lift, not just ship features. Cross-functional partners—from support to solutions engineering—become critical here, ensuring we scale responsibly while improving the signal-to-noise ratio of feedback flowing back to product roadmapping.

The outcome I aim for is simple: faster cycles from insight to impact. With tight cross-org alignment, a shared metrics framework, and disciplined experimentation, we can transform CX from reactive problem-solving into a proactive growth engine. If your team is ready to operationalize this approach, start with one high-friction journey, build a sharp driver tree, and let data, not opinions, guide the next iteration.

Inspired by this post on Amplitude – Best Practices.

May 15, 2026
Our Operating Model Is the Product—Why We Built Product Partners to Accelerate Outcomes

I’ve learned that customers don’t just buy features—they buy the way we discover, decide, build, ship, and support. In other words, the operating model is the product. That realization has shaped how my team and I at HighLevel translate product strategy into tangible, repeatable outcomes that show up in quality, reliability, onboarding, and consultative support every single day.

We created Product Partners to codify that operating model and scale it with discipline. It’s a blueprint and operating rhythm that unifies product strategy with go-to-market strategy, customer success, and solutions engineering—so empowered product teams can move faster without sacrificing clarity, governance, or customer trust.

First, we anchored on continuous discovery. Product trios work shoulder-to-shoulder with customer-facing teams to run customer interviews, journey mapping, and A/B testing, then validate insights with session replay and behavioral analytics. We use driver trees and opportunity solution trees to connect problems to outcomes, ensuring prioritization is evidence-based and aligned to product-market fit—not just output.

Second, we elevated delivery excellence. Our practices emphasize CI/CD, feature flags, observability, SRE-informed incident management, and DORA metrics to shorten feedback loops while raising the bar on stability. Privacy-by-design, data governance, and regulatory compliance are built into our workflows, and we make deliberate build vs buy decisions to protect platform scalability and long-term velocity.

Third, we integrated go-to-market alignment from day one. Solutions engineering and customer success shape requirements early, so launches include in-app guides, product tours, onboarding paths, and consultative support that accelerate user activation. We tie outcomes vs output OKRs to stakeholder management rituals, ensuring sales-led and product-led growth motions reinforce each other instead of competing for focus.

Finally, we closed the loop with a unified analytics platform. Activation, retention analysis, and Net Recurring Revenue (NRR) sit alongside qualitative signals from customer interviews and support. This single source of truth helps us refine product positioning, sharpen value propositions, and improve roadmapping and sprint planning with clear, testable hypotheses.

What does this mean for our partners and customers? Faster time-to-value, fewer handoffs, clearer expectations, and a shared lens on the metrics that matter. Product Partners isn’t a side program; it’s how we operationalize trust—through transparency, consistent rituals, and a bias toward learning that compounds.

If this resonates, you’ll feel it in how we discover, build, and support together. I’ll continue to share our playbooks—covering continuous discovery, onboarding, and outcome-based planning—so we can keep raising the standard for product management leadership and product-led growth, one operating rhythm at a time.

Inspired by this post on Product School.

May 14, 2026
From Tickets to Topline: How We Turned Support into a Consultative, AI-Powered Growth Engine

By the end of 2024, we were already all-in on Fin, and our customer support organization was deep in its own transformation. Resolution rates were strong, efficiency was improving, and for the first time, something new was emerging: capacity.

That newfound capacity wasn’t just a relief; it was a strategic opening. As we became less reactive day to day, I saw how support’s unique vantage point—rooted in customer needs and aligned with company goals—could evolve into a consultative function that actively drives value for customers and the business.

This is the story of how we built consultative support. I’ll walk you through how we got started, the results we achieved, and the lessons I’d carry forward if I were doing it again from scratch.

We didn’t begin from zero. A few years earlier, we partnered closely with research and data science to drive product adoption. In a project we called “next best step,” we tested offering proactive guidance inside already-established conversations. It worked well, and as Fin accelerated how we worked, we realized we were ready to push into broader, more ambitious opportunities.

Instead of dictating a solution from the top, I opened the floor. We hosted a support town hall and asked the team to share concrete ways support could directly drive company outcomes. The conversation was electric—practical, creative, and grounded in real customer moments.

Right there, we spun up campaign concepts. One idea was an always-on in-product banner offering a call with a member of our team to help customers set Fin up to the best of its ability. Another was the “Fin upsell campaign,” where, once a customer had a positive interaction with Fin and clicked the “that helped” button, a tailored message would share details about our own success with Fin and invite the customer to book a call to learn more and ask questions.

The energy from that session made one thing obvious: the team already knew how to help customers extract more value from the product. They just needed focus, permission, and a clear path to act.

We started small on purpose. I recruited a group of volunteers who dedicated part of their week to exploring new, proactive ways to support customers. We kept the group tight for two reasons: first, even with Fin freeing up significant capacity, we still had to deliver excellent day-to-day support; second, this was an experiment, and we weren’t going to overhaul a 100+ person organization without proof.

One of our first campaigns focused on proactive engagement with self-serve customers—those without a dedicated sales or success touchpoint. Our goal was to give this group direct access to teammates with first-hand experience in AI transformation and help them see the value they could get from Fin.

Early use cases included guiding customers through Fin trials, working with mature customers on optimization to get more out of Fin, and proactively identifying high-potential accounts that looked ready for Fin. None of this required a new team or a big budget—just attention and intention.

To make consultative support stick, we trained for a mindset shift. I encouraged the team to move beyond solving the immediate issue and instead probe deeper to understand each customer’s unique context. We leaned on our sales and success peers to refine our outreach—learning how to time our messages, frame value succinctly, and meet customers at the right moment rather than waiting for them to come to us.

To validate our approach, we needed data—not vibes. We built a simple but rigorous comparison: accounts we engaged with versus accounts we reached out to but didn’t hear back from. Over a six month period, we tracked feature adoption, Fin usage, and expansion revenue across both groups.

The result was clear: engaged accounts grew roughly twice as fast in both usage and expansion.

To further prove the value of proactive support, we also tracked direct Fin resolutions generated after consultative interactions, resolution and automation rate improvements across engaged accounts, and influenced expansion ARR across everything we worked on over the year.

Seeing those numbers was a turning point. This wasn’t a side project anymore—it was a repeatable motion with measurable business impact.

As results became visible, partnerships multiplied. Self-serve engineering teams saw the value of well-timed human touchpoints. Customer lifecycle marketing tapped us to handle responses to their campaigns. Product teams began partnering with us to identify high-impact engagement opportunities. We also deepened our collaboration with digital, scale, and high-touch success teams—stepping in where they lacked capacity and offering deep technical guidance to help customers get the best from the platform.

What began as simple outreach matured into targeted, strategic initiatives tied directly to company goals.

Within a year, our volunteer crew grew to ~16 teammates across regions—curious, motivated, and eager to try new things. We continued expanding the consultative support function and took on new projects end to end. Most recently, we assumed ownership of the new “sales assist” team to drive self-serve trial conversions and help new customers get the most from their first experience.

Here are the practices that mattered most in making consultative support real and durable:

Start with your team, not a strategy doc. The best ideas came from the people closest to customers. That town hall shaped our initial direction more than any top-down plan could have.

Don’t scale before you’ve proved it. A small, motivated group moved faster, learned quicker, and produced clearer results than a broad rollout. When you need organizational buy-in, a rigorous proof point beats a promising concept.

Train for a different mindset. Consultative work requires curiosity, commercial awareness, and the ability to hold broader context—not just product knowledge. Invest deliberately in coaching and frameworks that strengthen these muscles.

Measure against a control group. Without a control, you have a story. With it, you have a business case—and that’s what unlocks resources, headcount, and prioritization.

Lean into being different. It’s helpful to take cues from sales and success, but you don’t have to operate exactly like them. There’s real power in support’s distinct perspective and tone.

Building this consultative support function fundamentally changed how we think about our remit. Support is no longer just there to respond; it now drives adoption, influences retention, generates expansion revenue, and, for many self-serve customers, serves as the primary human touchpoint.

In an AI-first world, where Fin handles all of the transactional work, this kind of work becomes even more important. Because the question for support leaders is no longer “how do we handle more tickets?” but rather, “how do we use support to grow the business?”

Inspired by this post on The Intercom Blog.

May 8, 2026
Stop Silent Churn: The 8 Best SaaS Prediction Tools for 2026 (Features + Use Cases)

Churn isn’t just a retention problem—it’s a product, go-to-market, and strategy signal that shows up everywhere in the customer journey. Over the past few years, I’ve evaluated and implemented churn prediction tools across high-growth SaaS environments, and the difference between reactive firefighting and proactive, data-driven retention is night and day.

Compare the top 8 churn prediction tools for SaaS teams. Features, use cases, and how each stacks up, so you can act before customers quietly leave.

When I assess churn prediction tools for product-led growth, I start with a simple question: will this help my team see risk early enough—and clearly enough—to intervene with precision? The best platforms combine behavioral analytics, retention analysis, and anomaly detection to surface leading indicators before Net Recurring Revenue (NRR) takes a hit.

First, signal coverage matters. Strong churn models draw from product usage events, CRM integration, support tickets, billing health, and even session replay to capture real-world behavior. I look for native connectors to systems like Intercom, Pendo, and Amplitude analytics, plus flexible ingestion for custom events. Without comprehensive signals, even the smartest models will miss critical moments such as stalled onboarding, shrinking active seats, or feature disengagement.

Second, I require transparent risk scoring and clear drivers. Black-box scores erode trust with Customer Success and Product teams; explainability builds alignment. Tools that expose driver trees, cohort-based retention analysis, and segment lift help me translate insights into prioritized experiments. When possible, I tie predicted churn segments to A/B testing with a thoughtful minimum detectable effect (MDE) so we can quantify impact quickly and avoid overfitting to noise.

Third, actionability is non-negotiable. Predictions must trigger targeted AI workflows, in-app guides, and product tours—not just dashboards. My ideal setup routes high-risk cohorts to tailored journeys (e.g., an onboarding rescue path) while notifying the right owner in CRM and Customer Success. Playbooks should be easy to operationalize, measurable, and reversible if the signals change.

Fourth, I evaluate platform scalability, data governance, and privacy-by-design. Enterprise readiness means clear role-based access, auditability, robust SLAs, and an architecture that can evolve into a unified analytics platform as the product and data footprint grows. I also weigh total cost of ownership, implementation time, and maintenance burden against expected gains in NRR and expansion.

In my experience, the winning tools are the ones that make it simple to connect predictions to outcomes: reduce onboarding drop-off, increase user activation, prevent seat contraction, and accelerate expansion. They align Product, Customer Success, and Growth around shared metrics, shorten time-to-value, and make proactive retention part of the operating rhythm—not a last-ditch effort at renewal.

In this 2026 comparison, I’ll outline how each tool handles data breadth, model quality, explainability, and workflow automation. I’ll also share implementation checklists and decision criteria so you can choose the right fit for your stage, stack, and motion—whether you’re primarily product-led growth, sales-led, or hybrid.

If you’ve ever felt like customers “quietly leave” despite solid top-of-funnel metrics, this guide will help you turn churn signals into concrete actions—and convert at-risk accounts into durable advocates.

Inspired by this post on Pendo – Perspectives.

May 8, 2026
Making (Great) Data Flow Effortless in Amplitude to Unlock Faster Activation and Product-Led Growth

On the Amplitude growth team, the mission is clear: make it easier than ever to get (great) data flowing in Amplitude. That focus resonates deeply with me because, in my experience leading product organizations, nothing accelerates value creation faster than clean, trustworthy behavioral data reaching the right people at the right moment.

When Amplitude analytics is fueled by high-quality event streams, product teams can move from guesswork to precision. With consistent, enriched signals, behavioral analytics becomes a daily superpower—shortening time-to-first-insight, sharpening user activation strategies, and aligning everyone on outcomes. This is the foundation of a unified analytics platform that actually drives product-led growth.

“Great” data isn’t accidental; it’s designed. It starts with a clear tracking plan, human-readable event names, and strict schema validation. It continues with robust data governance, CI/CD-friendly instrumentation, and docs-as-code so analytics definitions don’t drift. When teams instrument once and trust forever, they reduce thrash, avoid rework, and build a durable decision-making muscle across product, engineering, and customer success.

The payoff shows up where it matters: onboarding becomes clearer, user activation improves, and experiments become more conclusive. With in-app guides and thoughtful product tours reinforced by reliable event data, I can see where users hesitate, why they drop, and which nudges actually help them succeed. That makes it easier to prioritize the highest-leverage changes and to communicate impact credibly to stakeholders.

I’ve repeatedly seen teams cut weeks of analysis down to days once they standardize event taxonomies, automate QA for instrumentation, and establish lightweight governance. The result is a smoother path to retention analysis, faster iteration on activation milestones, and a culture that treats data as a first-class product—not an afterthought.

Ultimately, making it effortless to get (great) data flowing in Amplitude is about dignity for the end user and leverage for the business. It’s how we turn curiosity into clarity, align teams around measurable outcomes, and scale product-led growth with confidence.

Inspired by this post on Amplitude – Best Practices.

April 30, 2026
Scale Support with Heart: How AI Makes Every Customer Interaction Faster and More Human

Every day at HighLevel, I talk with support leaders who are balancing two imperatives that can feel at odds: scaling service efficiently while deepening empathy in every interaction. My product lens is simple—use AI to clear the path for humans to do what only humans can do: listen, understand, and solve nuanced problems with care.

Discover how AI helps support teams deliver faster, more empathetic experiences. Automate the repetitive, so agents can focus on what matters: the customer.

That principle anchors our customer support AI strategy. We deploy AI workflows that handle the heavy lift—classification, intent detection, summarization, knowledge retrieval, and next-best-action—so agentic AI can triage, resolve routine issues, and hand off the right context when a human touch is needed. The result is a queue that moves faster, with more signal and less noise, and a team freed to bring empathy and judgment to the moments that matter most.

On the front line, a voice AI agent or chat interface deflects repetitive requests, while conversation design ensures the experience feels respectful, transparent, and helpful. Inside the console, Agent Analytics surface what leaders care about: which topics spike, where customers get stuck, how sentiment and CSAT shift, and which playbooks actually shorten time to resolution. When an agent steps in, AI-assisted replies, real-time summarization, and suggested macros reduce cognitive load—so attention goes to the customer, not the keyboard.

Shipping these capabilities responsibly requires rigor. My playbook pairs LLMs for product managers with a retrieval-first pipeline that grounds responses in audited knowledge, backed by privacy-by-design and data governance. We use eval-driven development to measure safety and quality, and A/B testing to quantify impact before broad rollout. This isn’t just about automation; it’s about trust, reliability, and continuous discovery with real customers.

Context is king, so CRM integration is non-negotiable. By unifying tickets, purchase history, prior conversations, and lifecycle stage, agents walk in with empathy already loaded. Whether the channel is Intercom, HubSpot, or native chat, a unified analytics platform connects signals across journeys, enabling proactive outreach, smarter product tours, and in-app guides that prevent avoidable tickets in the first place.

The outcome is a support organization that scales without sacrificing humanity. AI handles the repetitive; people handle the relational. Teams spend less time searching and more time solving. Leaders coach with data instead of guesswork. And customers feel heard—because they are. That’s how we make human support more human, at scale.

Inspired by this post on Amplitude – Perspectives.

April 28, 2026
Build vs. Buy for Churn Prediction: My Proven Playbook for Faster Retention and ROI

Churn is the silent tax on growth, and I treat churn prediction as a core product capability—not a side project. Over the years, I’ve led teams through multiple implementations across different data maturities and go-to-market motions, and the same question keeps returning at kickoff: what’s the smartest path to impact now and defensibility later?

“Should you build or buy your churn prediction model?” The right answer depends on time-to-value, data readiness, available talent, and whether churn prediction is a true differentiator for your product strategy or simply a must-have capability to power customer success and product-led growth.

When speed and coverage matter most, I start by evaluating category platforms that pair behavioral analytics with activation. As one example, vendors emphasize immediate business outcomes such as integrations, in-app guides, and workflow triggers that help you act on risk signals fast—without waiting months for model training or data engineering.

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.

Buying makes sense when you need rapid time-to-value, opinionated best practices, and a unified analytics platform to operationalize insights through product tours, in-app guides, and CRM integration. In these cases, I’m optimizing for coverage, consistent signal quality, and ease of activation for customer success—so the team can focus on interventions, not infrastructure.

Building is compelling when churn prediction is a source of competitive differentiation or you have proprietary signals others can’t access. If your product generates unique behavioral data, requires custom anomaly detection or explainability constraints, or must blend usage telemetry with domain-specific risk scoring, a tailored model can raise precision and unlock novel retention levers.

My hybrid approach has become a reliable playbook: buy first to establish a strong baseline and close the activation loop, then selectively build where proprietary data and context yield outsized gains. I use retention analysis to identify high-signal behaviors, then iterate with A/B testing and a clear minimum detectable effect (MDE) to validate uplift before committing engineering capacity.

Total cost of ownership is non-negotiable. I account for more than license or training costs: ongoing data engineering, feature pipeline maintenance, model monitoring for drift, and AI risk management all add up. Strong data governance, privacy-by-design, and regulatory compliance must be baked in—whether I build, buy, or blend both.

Activation determines real ROI. Predictions that don’t flow into customer success workflows, lifecycle messaging, or in-product nudges rarely move Net Recurring Revenue (NRR). I prioritize tight integrations that enable targeted experiments—journey mapping, contextual tooltips, and timely outreach—to reduce friction and increase user engagement at the moments that matter.

My quick decision test: buy if time-to-value and adoption are the immediate goals; build if proprietary signals and explainability are core strategic assets; blend if you want fast wins now with room to differentiate later. Answering the build vs. buy question through this lens consistently improves retention, accelerates product-led growth, and keeps teams focused on the customer experience rather than plumbing.

Inspired by this post on Pendo – Perspectives.

April 16, 2026
From High-Touch Swarms to Scalable Product: Turning Customer Signals into High-Impact Features

The best signal often comes from the least scalable work.

I’ve learned this the hard way—and the rewarding way. When I’m closest to customers, rolling up my sleeves with the team, I uncover nuanced, high-signal insights that no dashboard or aggregate report can reveal. Those insights, when treated with rigor and discipline, become the backbone of a durable product strategy and true product management leadership.

At Intercom, that is at the heart of how we operate on “swarms.” Swarms are cross-functional teams of Fin experts focused on ensuring customers succeed when trialing Fin. Each team consists of engineers, data scientists, and a product manager, all focused on optimizing Fin for our customers.

Working in these teams gives us deep insights into the needs of individual customers, but they can also form the foundation of new Fin features. Let me explain.

I frame the journey from insight to impact in three levels: “Level 1: Swarms – where the signal comes from,” “Level 2: Cockpit – where the signal starts to scale,” and “Level 3: Product – where the signal reaches maximum leverage.” This model blends continuous discovery with pragmatic solutions engineering and creates a clear path from hands-on learning to product-led growth.

Level 1: Swarms – where the signal comes from. The goal is simple: help Fin resolve more conversations and help customers understand and use the product. Swarms partner with customers to define their goals and how Fin fits into their workflows. We map out an automation roadmap by analyzing their conversations, determining the APIs and Procedures they need, and the level of automation they can achieve. We then support them in implementing it and reaching that outcome. This involves ongoing analysis to identify optimizations to their configuration and the next best actions for increasing automation levels, such as improving knowledge base content or deploying new APIs.

During a swarm, the feedback loop is fast. We test something, ship something, and quickly see whether the metric moves. That speed and depth is what makes swarms so valuable. It’s also what makes them hard to scale. I’ve felt the thrill of watching a key metric bend within hours—and the constraint of knowing that kind of attention doesn’t scale to every account.

For example, we developed an automation taxonomy to predict the level of automation a customer can achieve. Initially, this analysis was manual and took more than half a day to run, with time required to prep and visualize the data. But the effort was worthwhile. For one customer, we predicted an automation rate of 70% and they achieved exactly that.

By working closely with customers, we learn what drives success, but this work is inherently hands-on and doesn’t scale on its own. So the real challenge is figuring out how to turn what we learn in those high-touch engagements into systems, tools, and product changes that benefit far more customers. That’s the inflection point where AI workflows and product strategy meet.

Level 2: Cockpit – where the signal starts to scale. Not every customer should need swarm-level attention. The way we bridge that gap is by making the swarm analyses repeatable and shareable. Once we can run the same analysis across customers, we can start turning bespoke swarm learnings into reusable signals. This is where Cockpit comes in.

Transform customer signals into action: this dashboard tracks support conversation volume, taxonomy percentages by type, and topic demand across account settings, billing, integration, and more to guide scalable feature bets.

We take patterns learned in swarms and encode them into internal tooling inside our insights web app, Cockpit. Instead of analysis being a bespoke project, it becomes a workflow. For example, we scaled the automation taxonomy and this has enabled us to quickly understand automation potential for all customers.

Now, a customer success manager (CSM) can pick a customer, see their automation potential and current performance, understand the biggest issues, and propose next actions. This is how we scale the impact of swarm learnings through CSMs and Sales. It allows far more customers to benefit from the same patterns we see in high-touch work, without requiring direct data science involvement every time.

Cockpit also functions as a valuable proving ground. It gives us a way to test ideas across a much broader set of customers and see what generalizes before we consider taking anything further. In other words, we transform sharp, local signal into broadly useful guidance—an essential step in any AI Strategy that aims to balance precision with scale.

Level 3: Product – where the signal reaches maximum leverage. The real payoff comes when the patterns we have validated internally become part of the product itself. Instead of helping one customer directly, or helping many customers through internal teams, we deliver a feature directly to customers so they can improve Fin’s performance on their own. Today, the automation taxonomy is a part of Insights and accessible to customers who have this feature.

Another example is CX Score. It started with close work alongside Intercom’s Customer Support team to understand performance with Fin, initially through predicted CSAT and resolution. Over time, this work evolved into CX Score: a scalable way to measure conversation quality across all customers.

The product stage is fundamentally different from Cockpit because of the constraints. Cockpit provides a platform for our customer analyses/tools but it doesn’t need to scale as far as product. What moves into product has to work for every customer, without configuration, at scale, so it has to generalize. That bar is what protects long-term quality while unlocking product-led growth.

That’s why the move from Cockpit to product isn’t automatic. We’re not just asking whether something is useful, but whether it’s broadly useful, robust, and scalable enough to run across the entire customer base. As a product leader, I push for this discipline because it’s where customer success, engineering excellence, and business outcomes converge.

The loop. The model is simple. Swarms generate the best signal, grounded in real customer problems. Cockpit operationalizes that signal so CSMs and Sales can use it across many customers. Product takes the patterns that truly generalize and turn them into scalable features that enhance every customer’s experience.

This loop allows a small swarm data science function to have impact beyond a small set of high-touch accounts, resulting in a stream of continuous improvements across all three levels and an ever-increasing level of automation for our customers. Practically, it’s a repeatable playbook for product management leadership: start with high-signal discovery, prove repeatability, and only then scale through product. Done well, it compounds learning, accelerates time-to-value, and aligns the entire organization around measurable outcomes.

Inspired by this post on The Intercom Blog.

April 13, 2026
Net Recurring Revenue Mastery: How Elite CS Teams Drive Expansion, Retention, and Growth

Net Recurring Revenue (NRR) is the clearest signal of whether our product, pricing, and customer success motions are compounding value or quietly leaking it. When I review our dashboard, NRR tells me—in one number—how well we retain, expand, and engage customers. It’s the difference between linear progress and durable, compounding growth.

At its core, NRR answers a simple question: did revenue from our existing customers grow or shrink this period? The standard way I frame it is: NRR = (Starting MRR + Expansion – Contraction – Churn) / Starting MRR. Expansion reflects upsells, cross-sells, and increased usage; contraction and churn capture downgrades and departures. Great teams don’t just watch this number—they engineer it.

The teams that consistently outperform treat NRR as an outcome of intentional design across the entire customer journey. They align product-led growth with customer success, weaving onboarding, user activation, in-app guides, and lifecycle messaging into one coherent system. They make adoption the star of the show, not an afterthought tucked beneath quarterly targets.

To scale that system efficiently, I lean on platforms that streamline in-app guidance and rich behavioral analytics. The promise is crisp and concrete: “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.” When the experience is instrumented end to end, expansion opportunities show up as patterns, not surprises.

Retention analysis is where the signal gets sharp. I segment cohorts by plan, size, and use case; map their journey; and run driver trees that connect leading indicators (activation depth, feature breadth, time-to-value) to the lagging outcome (NRR). This turns hunches into hypotheses and gives customer success managers a prioritized playbook, not a long wish list.

Onboarding is the first and most powerful NRR lever. The faster a customer experiences their first win, the more likely they are to adopt core features, invite teammates, and expand. I use in-app guides, product tours, and contextual tooltips to pave the path to value—always grounded in clear jobs-to-be-done, not generic walkthroughs. The goal is simple: remove friction, celebrate progress, and make the next best action obvious.

Operating cadence matters as much as tooling. I separate the rhythms: QBRs for strategic alignment and expansion planning; OKRs for cross-functional execution and accountability. QBRs anchor the conversation in outcomes and value realized; OKRs ensure product, marketing, and CS move in lockstep to close the gaps those QBRs reveal.

Pricing and packaging complete the loop. When the value proposition is clear and plans are aligned to outcomes customers care about, expansion feels natural—more capability for more value. Usage insights guide which features to gate, which to bundle, and where to price to maximize retention while unlocking healthy upsell paths.

None of this works without tight product–CS collaboration. My teams practice continuous discovery—customer interviews, win/loss insights, and in-product feedback—so we improve the experience where it truly matters. Journey mapping turns those insights into experiments, and experiments turn into polished features once the data speaks.

I build an NRR driver tree into our weekly reviews. Each branch (activation, adoption, multi-seat expansion, downgrade prevention, reactivation) has a clear owner, a measurable hypothesis, and a time-bound experiment. A/B testing guides what we ship broadly, and we define success upfront to avoid moving goalposts after the fact.

I’ve seen NRR climb meaningfully in a single quarter when we pair rigorous retention analysis with targeted onboarding improvements and value-based packaging. The lift rarely comes from one big bet; it’s the compounding effect of many small, well-instrumented decisions.

Here’s the 90-day play I return to: first, baseline NRR by segment and identify the top three drivers of expansion and the top three causes of contraction. Next, streamline onboarding with in-app guides and product tours that accelerate time-to-value and drive user activation. Then, craft expansion plays aligned to real outcomes (additional seats, advanced workflows, new use cases), and operationalize them via QBRs. Finally, preempt downgrades with early-warning alerts, targeted education, and a clear path from “stuck” to “successful.”

NRR is a team sport. When product, customer success, and go-to-market align around adoption and outcomes, growth compounds, risk declines, and every customer interaction becomes a chance to create more value—today and in every renewal to come.

Inspired by this post on Pendo – Perspectives.

April 13, 2026
Commercial vs. Internal Products: Hard Truths, High Leverage, and How I Make the Call

Internal Products Are Hard; Commercial Products Are Harder. That line captures years of hard-won lessons from leading both internal platforms and market-facing SaaS at HighLevel. I’ve seen how the two demand different muscles—even when the tech stack, talent, and timelines look the same on paper.

When I talk about internal products, I mean services and solutions that our own employees use to take care of customers—customer-enabling tools and services, agent consoles, fulfillment and billing workflows, operations dashboards, and the underlying platforms that keep them fast, compliant, and resilient. These tools don’t generate revenue directly, but they quietly determine customer experience, gross margin, and how quickly we can ship, resolve issues, and scale.

Commercial products, by contrast, add a second challenge layer. Beyond discovery, usability, and reliability, we must conquer positioning, pricing and packaging, competitive differentiation, sales enablement, procurement hurdles, and ongoing customer success motion. The surface area for failure is bigger, and the time-to-signal on product-market fit is slower and noisier.

Here’s how I decide where to invest. First, I anchor on outcomes, not output. If the business priority is net revenue retention, faster onboarding, or reduced cost-to-serve, internal products often provide the highest-leverage path. If the priority is new revenue, new market entry, or a must-have differentiator, we lean commercial. I make the trade explicit in outcomes vs output OKRs so we can defend the decision when pressure mounts.

Second, I run a clear build vs buy calculus. For internal needs, the default is buy if a mature, configurable solution exists that meets our security, data governance, and integration requirements. I only build when the workflow is core to our differentiation, the TCO of customization is lower than vendor sprawl, or we can capture unique proprietary advantage. For commercial products, I avoid embedding third-party IP in a way that caps differentiation or compresses margins as we scale.

Third, I insist on continuous discovery. Internal audiences are not a captive market—they’re discerning experts with real jobs to do. I treat them like customers, with structured customer interviews, journey mapping, and opportunity solution trees. I rely on empowered product teams and product trios to validate problems and reduce solution risk before we commit engineering time.

Fourth, I frame commercial vs internal work with capacity guardrails. In most planning cycles, I reserve explicit allocation for platform scalability and internal tooling, separate from feature bets. Without this, internal products become backlog filler, which guarantees we’ll pay the interest later in churn, SLA breaches, and slower delivery.

Execution differs too. For internal products, change management is the make-or-break. I plan enablement as a first-class deliverable: clear rollouts, in-app guides, training, and feedback loops with frontline champions. I track adoption, time-to-resolution, error rate, and satisfaction for internal users with the same rigor we apply to external users.

For commercial products, I design the discovery-to-GTM handshake early. Pricing and packaging must reflect value drivers discovered in research, not what’s easiest to meter. Sales and solutions engineering need crisp narratives, objection handling, and proof points. Customer success needs activation plans and health signals tied directly to leading indicators of retention.

Across both, I instrument the product and process. I lean on feature flags and progressive delivery to manage risk, and I protect SLOs with error budgets so teams balance reliability with iteration speed. CI/CD isn’t a badge—it’s how we earn the right to ship continuously without eroding trust.

Common pitfalls recur. Teams skip UX for employee tools because “they have to use it”—which backfires as shadow workflows and rework. Leaders underfund internal platforms, then wonder why velocity stalls. On the commercial side, teams over-index on features and under-invest in positioning and onboarding, leading to poor activation and elongated sales cycles.

What’s the payoff? When we treat internal products as products, we unlock scale: shorter handling times, fewer escalations, clearer accountability, and higher customer satisfaction. When we approach commercial products with the same discovery rigor plus smart GTM, we compress time-to-value and amplify differentiation. The craft is knowing which lever to pull when—and having the discipline to measure what matters.

My rule of thumb is simple. If the goal is operational excellence that compounds across the entire customer journey, invest in internal products with the same intensity you reserve for revenue-generating features. If the goal is market expansion or category leadership, invest in commercial products with a tight discovery-to-GTM loop. In either case, clarity of outcomes, disciplined discovery, and empowered teams win the day.

Inspired by this post on SVPG.

April 9, 2026