Product Adoption & Engagement Strategy.
The Context The product had expanded to over 30 distinct features, creating a “feature bloat” paradox. While the platform was powerful, it was not one-size-fits-all. Some enterprise clients utilized the entire suite, while mid-market clients often found immense value in just 5 core tools.
The Challenge
- The “False Negative” Metric: Our adoption tracking was flawed. A client using only 5 out of 30 features was flagged as “At-Risk” (low adoption), even if they were getting 100% value from those 5 features.
- Generic Enablement: Because we couldn’t easily distinguish between “unaware” users and “uninterested” users, we were spamming clients with training for features they didn’t need.
- Complexity Churn: New clients were overwhelmed by the dashboard complexity, slowing down their initial momentum.
The Strategy
“Right-Sizing” the Value I led a strategic initiative to restructure how we packaged and monitored the product, moving from a monolithic model to a value-based one.
- Strategic Repackaging: We moved away from a single “all-access” license and introduced 3 distinct pricing tiers plus a specific “Pick Your Own” modular plan. This aligned the commercial offering with actual client needs.
- Contextual Health Scoring: We overhauled the health score algorithm to measure “Utilization vs. Entitlement” rather than “Utilization vs. Platform.”
- Result: If a client bought 5 features and used all 5, they were now correctly scored as “Healthy (100%),” not “At-Risk (16%).”
- Segmented Automation: With clients now properly categorized by plan, we launched targeted email campaigns. Users only received “Feature Adoption” nudges for tools actually included in their specific package .
- Power User Ecosystem: Launched monthly webinar workshops tailored to specific tiers, creating internal champions without overwhelming smaller clients.
The Results
By ensuring clients were only measured against the value they signed up for, we clarified our data and boosted engagement:
- Adoption Growth: Increased overall product adoption by 20% by removing friction and focusing users on relevant tools .
- Data Accuracy: Eliminated false “churn risk” alerts, allowing the CS team to focus their time on genuinely struggling accounts.
- Upsell Identification: The “Pick Your Own” model made it easier to identify expansion opportunities—if a client on a basic plan kept trying to access a locked feature, it triggered an automatic upsell lead.