The marketing team of a leading MNC in garden supplies wanted to identify the reasons why customers were churning and predict the customers at high risk of churning. Read the case study to know how predictive ML models were created to determine customers likely to churn so that the company could create targeted campaigns for their retention, thereby increasing customer lifetime value.
The leading garden supplies company wanted to prevent churn by identifying reasons that stopped them from making subsequent purchases and predict customers who are at high risk of churning to take preventive actions and improve customer lifetime value. The client also wanted to improve their sales via e-commerce platforms, retail stores, and drive subscriptions while improving the demand for their products.
The case study explains how ML solution created by Sigmoid integrated 15 different data sources, segmented customers at a granular level. This increased the accuracy of predicting customers likely to churn by 2.5x times. This also resulted in 70% improvement in customer retention while enabling the client to design marketing campaigns to retain customers and increase the customer lifetime value.
Offered Free by: Sigmoid
See All Resources from: Sigmoid