Crowe’s client, an insurance company, needed to predict the behaviour of potential customers who were engaging through their online quote system. This, in turn, could enable them to maximise growth and reduce dropouts in the sales cycle.
We met with the client to understand their needs and agreed on key data sources, addressing ethical, governance, and security concerns.
Using Python, we built and trained a machine learning model on customer purchase history to predict buying propensity. After testing, the model predicted potential customer behaviour on the client’s online quote system, aiding sales and revenue growth. The model was designed for easy integration into existing sales processes, providing augmented intelligence. We also identified options for automated responses in the sales process, such as customer follow-ups.
The model helped the client identify why customers favoured specific products, understand whether products met their needs, anticipate requirements to improve onboarding and issue resolution, and gain clearer insight into compliance risks such as KYC, sanctions, money laundering and mis-selling.