Objective
A top retailer wanted to improve churn rates by leveraging data science driven insights. The goal was to understand each customer by creating behavioural cohorts and drive personalized campaign strategies to reduce churn.
Our Solution
- Our churn propensity computation module used a multi-model ensemble approach that included Logistic Regression, SVM, Boosted Decision Trees, Averaged Perceptron.
- Our customer segmentation module created customer micro-segments (cohorts) based on customer behavior and attributes, taking into consideration demographic data, shopping mission, average monthly spend, sentiments and feedback, basket preference, average time between visits, bill value as well as baskets.
- The customer micro-segments were used to devise intervention strategies(such as personalized churn reduction campaigns) for “high risk” customers in each target cohort.
- The solution leveraged a self-learning approach to refine recommendations and strengthen propensity rules.
The solution was built leveraging tcg mcube, our proprietary analytics platform that can analyse large volumes of data to drive accelerated insights. The built-in data models and data science algorithms drive velocity to value for our customers.
Project Impact and Outcomes
Rollout of churn prevention campaigns based on data science driven insights helped reduce churn rate by over 20% within 3 months.
