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C5i > 实例探究 > Leveraging an insurance customer retrieval solution to reactivate lapsed accounts
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Leveraging an insurance customer retrieval solution to reactivate lapsed accounts

技术
  • 分析与建模 - 预测分析
适用行业
  • 金融与保险
适用功能
  • 销售与市场营销
用例
  • 补货预测
服务
  • 数据科学服务
挑战
The client, a leading insurance company, was facing a challenge of accessing lapsed insurance policies with a potential of repayment within a specific time bracket. The company was witnessing revenue loss and wanted to reactivate these lapsed policies. However, they wanted to ensure that the strategy resulted in minimum wastage of money and effort.
关于客户
The customer in this case study is a leading insurance company operating in the financial services industry. The company was facing a challenge with lapsed insurance policies and was seeking a solution to reactivate these accounts. The company was experiencing revenue loss due to these lapsed policies and was looking for a strategy that would result in minimum wastage of money and effort. The company's goal was to identify these lapsed accounts and focus on their reactivation, which would result in an additional flow of revenue.
解决方案
Blueocean Market Intelligence customized their insurance customer retrieval solution to address this problem. They accessed the lapsed policies that had the potential of repayment by invoking inforce attributes on the defaulted policies. A binary logistic regression was utilized on lapsed and inforce datasets and a KS cutoff was decided based on the model results. A confusion matrix was built consisting of all the four wells. Factors like premium to be paid, income of the policy holder, occupation and the total sum assured at the end of maturity were found to be greatly affecting the model results.
运营影响
  • Comprehensive analysis of lapsed policies
  • Identification of factors affecting the predictive model such as premium to be paid, income of the policy holder, occupation and the total sum assured at the end of maturity
  • Development of a strategy for reactivating lapsed policies
数量效益
  • Approximately 11,000 policies targeted from a portfolio of 50,000
  • 8,000 policies successfully repossessed

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