8
weeks
from diagnostics to model deployment and first results
28%
increase
of upsell success rate after the first 6 months
90%
of customers likely to buy moreare 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.
Our task
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.
Our Solution
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.
A look in the future
After the first 6 months of targeted marketing initiatives for customers with a high likelihood to buy, upsell success rate increased with 28%.
Project Trivia:
Industry: Insurance
Company Size: 4500+ employees
Location: Switzerland
GemSeek Capabilities: Predictive Customer Analytics
