Purchasing propensity in the automotive sector.

Use case

challenge.

In this scenario, the client is an automotive brand with a large number of followers, making it challenging to distinguish between those who are merely fans and those who are genuinely going to purchase a vehicle.

Within its customer-centric philosophy, it pays great attention to anyone interested in its vehicles and the brand., but there is a need to analyze the propensity of its current customers to acquire new vehicles, both for those customers who already own a vehicle from the brand and for potential leads.

solution.

This purchasing propensity initiative enables the identification of those customers and leads who have a higher probability of buying each specific model of the brand.

On one hand, a discovery of variables will be conducted, characterized over time and by location, that have a greater predictive capacity based on the historical purchase data, lifestyle, behavioral segments, proximity of products, or socio-demographic information concerning the studied vehicle.

Customer clustering based on similarity and study objectives will be performed by analyzing their interactions with the entity over a historical period of 10 years.

Next, there will be a training and validation process for algorithmic models to obtain the probability of the customer purchasing the target vehicle. This involves generating a probability scoring for each customer based on the target model, finishes, and the acquisition model.

The initiative was approached in two phases based on the collected information: one focused on existing customers of the brand, and another that incorporates information from leads added to the CRM.

benefits.

Scoring of entity’s customers on acquisition propensity for 19 target models/finishes is performed based on over 350 variables for each customer.
Improvement in results is achieved over the traditional model used by the brand for calculating propensity to contract in the majority of the set objectives.