Most product teams drown in analytics but starve for insight. They track everything, yet still watch customers churn without understanding why. Yombwe Kotati, a Customer Service Manager and Product Operations Leader with over 20 years of experience, has seen this pattern repeatedly. The problem is not a lack of data. It is a lack of action. Over two decades leading more than 70 teams, Kotati has delivered a 96% NPS and secured more than $2.5 million in retained revenue by turning user analytics into strategies that change customer behavior. The difference between companies that retain customers and those that lose them often comes down to whether data is used to intervene before it is too late.
Map the Path to Success, Then Guide Everyone Down It
Retention strategies fail when they are reactive. By the time a customer stops logging in or cancels a subscription, the opportunity to influence behavior has already passed. Rather than focusing on why customers leave, Kotati starts by identifying what successful customers consistently do and then designs systems to replicate those behaviors across the broader user base. The first step is behavioral cohort analysis. Kotati’s teams examine what engaged users do in their first week, which features retained customers return to consistently, and which milestones correlate with long-term retention. The results are often unexpected.
“We built user cohorts based on behavioral patterns, logins, feature usage, and support interactions, and found clear trends,” Kotati explains after analyzing onboarding data for a SaaS product. “Users who completed onboarding within 48 hours and used three core features in the first week were 60% more likely to stay past 90 days.” That insight reshaped the entire onboarding experience. Instead of sending generic welcome emails, the team introduced personalized nudges and in-app guidance designed to steer users toward proven success milestones. The objective was simple: reinforce behaviors that data had already shown to drive retention.
Intervene Before Churn Happens, Not After
The second lever is speed. Waiting until a customer cancels to ask why they are leaving is ineffective. Kotati’s teams rely on real-time dashboards that monitor inactivity, declining usage, and skipped onboarding steps as they occur. When engagement drops below defined benchmarks, automated workflows activate immediately. “When a user fell below engagement benchmarks, automated workflows triggered personalized messages such as helpful resources, reminders, or check-in calls from our support team,” Kotati notes after implementing this system for a subscription-based platform. “This proactive approach helped re-engage at-risk users and directly contributed to securing more than $2.5 million in revenue.”
The most effective interventions blend automation with human connection. Automated messages address many scenarios efficiently. When a high-value customer disengages, however, Kotati’s teams escalate to direct outreach from support or customer success. Data informs the response, but human interaction often determines the outcome.
For these efforts to work long term, the product itself must evolve. Kotati closes the feedback loop by pairing usage data with targeted surveys and NPS responses, uncovering not only what users do but why they do it.
Data Tells a Story. Your Job Is to Act on It.
Engagement and retention are not accidental. They result from identifying what successful users do, intervening early when behavior shifts, and continuously refining the product based on real usage patterns and feedback. Kotati’s results did not come from collecting more data. They came from acting on the data already in hand. Your data is telling a story. The real question is whether you are listening, and whether you are willing to act on what it is telling you.
Connect with Yombwe Kotati on LinkedIn for more insights.
