Methodology Playbook

Summary

Kevin's view is that churn is never a single-team problem — it is a diagnostic signal that traces back to ICP misalignment, onboarding failures, product stickiness gaps, and organizational accountability structures. Retention cannot be owned by CS alone; it requires coordinated action across product, engineering, sales, and leadership. The work starts with categorizing churn correctly, measuring the right things, and assigning ownership to the teams that actually have the influence to change outcomes.

Methodology

Kevin begins any churn engagement by establishing LTV as the north star metric, broken into its two components — revenue value and time duration — so each can be improved independently. He then runs a segmentation exercise to tag churn as avoidable versus unavoidable, which resets organizational expectations and focuses effort on the right problems. From there, he sequences the retention deep dive by aligning stakeholders on ICP first, because without a shared definition of the ideal customer, all subsequent churn analysis will be interpreted differently by different teams. On the structural side, Kevin pushes hard on ownership: PLG and onboarding quality are engineering problems, not CS problems, and placing accountability in teams without the authority to change the product creates the illusion of ownership without the capacity for impact. For CS teams specifically, he recommends a bell curve coverage model — white-glove for high-value and high-risk tails, scalable programmatic support for the broad middle — and insists that health scoring, workflows, and escalation triggers be coupled directly to the product roadmap and in-product behavior signals rather than lagging satisfaction metrics. Finally, Kevin distinguishes between leading and lagging indicators: when retention improvements take months to materialize in NRR, teams must define intermediate signals like activation rates and feature adoption milestones to validate that initiatives are working before the final metric confirms it.

Summary

Low product adoption is almost always a vendor problem, not a customer problem — it signals a failure in onboarding, workflow integration, content, or incentive design. Kevin's view is that founders and CS teams must diagnose the exact point of breakdown in the adoption chain rather than assuming customers lack motivation or sophistication. The fix requires simultaneous intervention across multiple levers: hands-on enablement, workflow automation, educational content, consulting support, and friction reduction.

Methodology

Start by walking the full adoption funnel end-to-end to isolate the weak link — distinguish between willingness (does the user want to do this?) and capacity (do they have the bandwidth and clarity to do it?). Treat low adoption as a vendor failure signal and respond with direct intervention: schedule calls using the customer's actual data, complete tasks alongside them first, then guide them through repetition until they build proficiency. Attack the adoption problem from multiple angles simultaneously — improve practitioner and patient incentives, automate or integrate the action into existing workflows (e.g., EHR systems), and add a human consulting layer when customers can't translate product insights into decisions. Proactively surface unreported usability issues, since customers who've given up rarely complain — build mechanisms to catch silent friction before it kills retention. Close educational content gaps by creating use-case-specific videos and resources (not just how-to guides) that show customers how to achieve their desired outcome, ideally featuring existing power users. Finally, mine happy customers through structured discovery interviews to understand exactly how their day-to-day workflows changed — this evidence becomes the most compelling sales and onboarding asset for the next prospect.