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Case study

Targeting the most valuable customers for a financial services company

We predicted and identified the high-value customers for a financial services company, allowing the team to spend more marketing resources targeting and supporting them. 


Customer

A FTSE 250 financial services company.


Problem

The company, which is a financial derivatives dealer, has annual revenues of hundreds of millions of pounds and tens of thousands of customers worldwide. One-fifth of its customers account for more than 90% of revenue and are as a result regarded as high-value customers. Spotting the customers who are likely to become high-value customers early allows the marketing team to offer them additional support, with the goal of gaining more business from them.


Solution

Faculty built a pipeline of models based on the different stages in the lifetime of a customer: sign-up, first deposit, first trade, first week of trades, and 30 days of trade. With this information, we were able to determine the probability of customers becoming high-value customers. We used two supervised learning algorithms, K-nearest neighbours and random forest, to build the models.

 

 


Impact

With our models, we helped the marketing team to identify high-value customers more accurately. Even with information gained from people at sign-up, the marketing team was able to contact about a third of the customer base with the likelihood of reaching half the high-value customers.

After customers place their first deposit, only 20% of the customer base, and after one month of trading, just 10% of the whole customer base, needed to be contacted for the provider to reach the same proportion of the high-value customers. In short, the company can make better use of marketing resources to target those customers who generate the most revenue.

To find out more about what Faculty can do
for you and your organisation, get in touch.