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

Want the Full Implementation Playbook?

Get the complete case study with detailed methodology, implementation timeline, specific frameworks used, and lessons learned.

Sound Familiar?

This Case Study May Be Relevant If You're Saying...

1

"We need to predict churn/lapse"

2

"Engagement is low in a financial book of business"

3

"We need CLTV-led retention interventions"

Ready to See Results Like These?

Let's Discuss Your Challenge

Every engagement starts with understanding your specific situation. Request a consultation to explore how we can help.

Request Consultation