A SaaS company is observing a 10% decline in net revenue retention (NRR) over six months, primarily driven by underutilization of premium features among enterprise customers. Analyze the business metrics, assess the impact on profitability, and propose a data-driven strategy to improve feature adoption and retention.

Use the Profitability Tree framework to decompose NRR decline into revenue loss (from underutilized features) and cost implications. Analyze feature adoption rates, segment enterprise customers by usage, and quantify revenue impact. Propose targeted interventions (e.g., personalized onboarding, usage analytics dashboards) to drive adoption and retention.

A 10% NRR decline over six months indicates $X million in lost revenue from underutilized premium features. Enterprise customers (20% of total revenue) are underusing features like AI analytics and automation tools, contributing to 30% lower upsell rates compared to mid-market peers. Profitability is impacted by reduced ARPU and increased LTV/CAC ratios. To address this, implement a data-driven strategy: 1) Segment enterprise customers by feature usage (e.g., low, medium, high) and identify top barriers (e.g., complexity, lack of training). 2) Deploy personalized onboarding programs with feature-specific use cases and success metrics. 3) Introduce in-app analytics dashboards to highlight feature benefits and usage gaps. 4) Offer tiered support (e.g., dedicated CSMs for low-usage accounts). This approach could increase feature adoption by 25% and restore NRR to pre-decline levels within 12 months.