Client is a leading private life insurance firm in India selling a gamut of insurance products like term insurance, savings plan, ULIP’s, pensions and many more, helping them to meet their long-term financial goals and plan their lives in a better way.
Client wanted to improve cross-sell penetration within existing customer base. It was already running direct marketing campaigns targeting customer segments based on current product held, life stage rules, time of the year and other business rules. Incremental gains from such campaigns were marginal and it was desired to deep dive into data to:
1. Proactively identify the policyholders who have a high likelihood to purchase more than one policy based on all available data fields.
2. Use agent characteristics as the main lever to predict cross-sell propensity.
Propensity Algorithm to score customers using Logistic Regression. Datasets used were Customer Demographics, Product Details, Financial Behavior, Customer Interaction and Agent Details.
1. Cross Sell propensity scores at product category level for each customer.
2. Scores normalized to recommend top 2 products customer is likely to purchase.
3. Recommendations used to power email and call center campaigns.
1. Tailored marketing campaigns across modes of marketing.
2. Incremental Revenue of USD 100,000 in 3 months.
3. Lower cost of Marketing Campaigns.