from diagnostics to model deployment and first results
of upsell success rate after the first 6 months
of customers likely to buy more
Are precisely identified due to the high accuracy of the model
The Business Problem
A major British insurance company was facing a less than satisfactory upsell rate for their automotive insurance customers.
We were tasked to come up with a model to identify which customers are likely to accept and upsell offer and suggest what offers would be most relevant to customers.
We created a model that predicts how likely is each customer to accept an offer for complementary products and services – each of them is assigned a “propensity to buy” score from 0% to 100%. The model is based on customer, transactional, service and NPS survey data. After initial validation the model was implemented into the customer service processes and started generated regular recommendations, optimisation suggestions, more accurate prioritisation of customer segments.
After the first 6 months of targeted marketing initiatives for customers with a high likelihood to buy, upsell success rate increased with 28%.
- Industry: Insurance
- Company Size: 4500+
- Location: Switzerland
- GemSeek Capabilities: Predictive Customer Analytics