Predicting high value customers

We applied advanced predictive models to customer behavioural data to enable our client to identify their most valuable customers early-on and grow their revenue and profitability.

The Challenge

Client acquisition in financial services can be extremely expensive. Our client needed to identify which customers were most likely to have high client lifetimes as early as possible in their customer journey. Sales and marketing resources could then be focused on those customers who were most likely to prove most profitable.

Our Approach

We conducted a “Data Health Assessment” across the client’s customer journey to understand how client profiles and behaviour was being tracked, identify any gaps or data quality issues. We stitched everything together into a data lake. Applying a range of predictive and machine learning models we rapidly prototyped a system that could predict key events on the customer journey.

Our Impact

Our models enabled our client to efficiently predict how likely a new lead is to convert, their propensity to become a high-value client and anticipated values for lifetime KPIs. These predictions have enabled them to efficiently target their sales, nurturing and retention activities resulting in a significant uplift in revenue and profitability.

Sector 
  • Financial services, insurance, legal
Study type 
  • Customer journey mapping
  • Data analytics
  • Data Science
Geography 
  • Africa
  • , Asia
  • , Europe

San Francisco

535 Mission Street, 15th Floor
San Francisco
CA 94105
USA
+1 855 503 0723

New York

8 Prospect Street
Brooklyn
NY 11201
USA
+1 855 503 0723

London

22 Upper Ground
London
SE1 9PD
United Kingdom
+44 (0)208 720 7269