About the client
A leading product and solution provider for building safety and HVAC equipment.
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
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