Case Study · Predictive Analytics

Customer Lifetime Value (CLV) Model for the Automotive Industry

A forward-looking CLV model that predicts each customer's value across vehicle sales, service, parts and finance, so marketing, retention and trade-in decisions are made on future value.

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Client

Automotive retailer with sales, service and F&I operations

Industry

Automotive

Delivers

Predicted 3-year value for every customer

The problem

Most dealers value customers on what they spent last year. A customer who just bought a car may never return for service, while a quiet service regular could be ready for their next vehicle. Marketing spend, service offers and trade-in terms were not based on what a customer is likely to be worth in future.

Modelling approach

One customer view that brings together vehicle purchases, repair orders, parts, accessories, finance and insurance (F&I) products and warranty contracts.
Probabilistic repeat-purchase models (BG/NBD with Gamma-Gamma) for service and parts spend.
A separate replacement-cycle model estimates when each customer is likely to buy their next vehicle and its expected value.
Components are combined into a 3-year CLV with confidence ranges, validated on past cohorts.

How the business uses it

Marketing budgets are set by predicted value rather than spread evenly.
High-value customers get priority service, loyalty benefits and proactive upgrade offers.
Trade-in and retention offers are sized to the relationship's future value.
Acquisition channels are judged on the long-term value of the customers they bring in, not cost per lead.