Financial ServicesClient Engagement & Retention Analytics
Behavioral Analytics for Client Engagement & Retention
Large UK Wealth Management Firm|Large Enterprise
8%
Reduction in policy lapse rate
5%
Acceleration in retention activities
Improved
Cross-sell/up-sell targeting
Real-time
Risk-based engagement
The Challenge
Needed real-time analytics to predict lapse risk, personalize engagement, reduce inactivity, and improve client retention and profitability.
wealth managementengagementretentionlapse riskCLTVpropensity modelinginactivityportfolio profitability
The Solution
Behavior-based Segmentation + CLTV Modeling + Retention Intervention Design
- Unified data audit
- Behavior-based client segmentation
- CLTV methodology
- Retention propensity modeling
- Upsell modeling
- Intervention design via Dynamo
The Results
Improved engagement and retention with targeted interventions and risk-based portfolio management.
8%
Reduction in policy lapse rate
5%
Acceleration in retention activities
Improved
Cross-sell/up-sell targeting
Real-time
Risk-based engagement
Functional Areas
Customer AnalyticsRetention MarketingPortfolio Management
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"We need to predict churn/lapse"
2
"Engagement is low in a financial book of business"
3
"We need CLTV-led retention interventions"
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