About the client
A leading product and solution provider for building safety and HVAC equipment.
Problems:
Our client was struggling with high customer churn rate and reached out to Innover with following business objectives:
- Development of an analytical model to predict the propensity of churn for B2B customers
- Creation of a customer churn risk score and identification of necessary steps to mitigate the churn of high value customers
Solution:
Innover developed an Analytics and Machine Learning powered customer churn prediction model which included:
- Unification of structured and unstructured historical data from various heterogeneous data sources including - customer information, churn data, products, contracts, service desk etc.
- Analysis of this data to create new features and define dependent variables. Then, categorize and cluster this data and deploy different ensemble models for each cluster.
- Next step was to perform multivariate analysis to identify key causal factors that are driving customer churn.
- Finally, generation of customer churn risk score as an output of the ensemble models to create risk bins for each customer