Customer Retention & Churn Analytics

The objective was to move retention management from broad, reactive outreach to a more focused, evidence-led approach. The analysis was designed to identify the characteristics and behaviours associated with customer dormancy, estimate where revenue was at risk, and help management prioritise interventions while customers were still recoverable. In practical terms, the work connected customer profiling, product behaviour and service adoption to a clearer retention decision framework.

The firm was facing a material customer-retention issue: 45% of individual investors were inactive. Management therefore needed more than a headline dormancy ratio. The key questions were which customer groups were most likely to disengage, what commercial value was exposed, which behaviours appeared before discontinuation, and where targeted retention activity could have the greatest effect. The BI assignment converted those questions into a structured analytical problem using the firm’s available customer, product and transaction data.

• Compiled data across core-system tables using a data warehouse (Oracle/SQL Server) with Tableau for analysis, on a daily refresh.
• Profiled churned investors across investment size, first product, product breadth and value-added service usage.
• Modelled likelihood of discontinuation and expected revenue impact per investor segment.

• Most churned customers had lifetime investments between Rs. 5,000 and Rs. 500,000.
• They typically bought a single fund (Asset Allocation, Income or Money Market) and never explored others.
• Most never activated value-added services (digital, debit card, e-transactions).
• They stayed active for less than a year before redeeming.

• Personalised service to deepen investor experience and brand connection.
• Proactive support to resolve issues early, plus a formal feedback loop.
• Trusted-advisor positioning, referral and awareness programmes, and social channels for informal engagement.