Calculation and visualisation of the customer lifetime value

The bank's previous method of calculating customer lifetime value was inadequate and a fixed monetary value was attributed to each customer. The activity of the customer was not taken into account, which led to an inaccurate calculation of the customer lifetime value.

We partnered with the bank to combine different data sources and integrate customer activity into the existing Customer Lifetime Value calculation. Customer types were identified and classified into five categories using clustering in R. The interactive visualisation of the customer journey was illustrated in a D3 Sankey diagram.

Our data analytics and AI expertise enabled us to increase the accuracy of the Customer Lifetime Value by including the customer's activity in the calculation. Through the Sankey diagram, customer activity could be presented in an interactive visualisation, which made it easier for the bank to gain insights into the customer journey.

Our collaboration with the bank led to a significant increase in the accuracy of the Customer Lifetime Value. By integrating customer activity into the calculation, the bank was able to gain sound insights into customer behaviour and make informed decisions for the future.

As a result of our work, the Bank has been able to improve its ability to make informed decisions regarding its client relationships. We have shown that our expertise in data analytics and artificial intelligence is invaluable in optimising business processes.

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