MARKETING CAMPAIGN    OPTIMIZATION

USE CASE

THE CHALLENGE

A retail bank was looking to automate and optimize allocation of marketing campaigns that were typically run in parallel. The prioritisation of the campaigns was previously done manually and without considering the overall business impact. In addition, contact rules were rather rigid, not allowing to follow a business opportunity when it occured.

OUR SOLUTION

Our solution is centered around two key input parameters: cost of each marketing channel and potential revenue of each product or service offered. It further allows to set marketing campaigns flexibly so that it reflects current business goal (e.g., to maximize profit, sales, volume of loans, etc.). We developed a bespoke combination of machine learning models and optimization and simulation techniques that decides who to contact with what offer and when so that the long-term business benefits are maximized.

RESULTS

Overall marketing profitability estimated to have improved by c.20%. In addition, automation of the whole process now allows marketing and segment experts to move from the manual prioritization of campaigns to more value-adding tasks.