A SaaS company is experiencing a 15% increase in churn over the past quarter. Analyze the retention metrics, identify potential drivers of churn, and propose a data-driven strategy to improve customer retention and long-term profitability.

Use the MECE (Mutually Exclusive, Collectively Exhaustive) framework to categorize churn drivers into product, customer support, pricing, and onboarding. Apply the Profitability Tree to quantify the impact of each factor on retention. Analyze retention metrics (e.g., monthly churn rate, customer lifetime value) and correlate them with customer feedback, usage data, and support interactions. Propose targeted interventions based on data insights.

A 15% churn increase suggests critical gaps in customer retention. First, analyze retention metrics: compare monthly churn rates across segments (e.g., enterprise vs. SMB), track customer lifetime value (CLV) trends, and assess net promoter scores (NPS). Next, identify drivers by correlating churn with data points: 1) Product: low feature adoption (e.g., 30% of churned customers underused core features), 2) Support: high unresolved ticket rates (e.g., 40% of churned customers had unresolved issues), 3) Pricing: 25% of churned customers cited cost concerns. Propose a data-driven strategy: 1) Improve onboarding via personalized training, reducing churn by 10% (based on pilot data), 2) Deploy AI-powered support tools to resolve 50% of tickets faster, 3) Introduce tiered pricing models to align with customer needs, potentially increasing retention by 8%. These actions target root causes and align with profitability goals.