Customer Retention
Project - Customer Churn Prediction
Customer churn focuses on a predictive AI model designed to identify customers at risk of churn within one to three months. This allows for targeted actions to enhance retention. Benefits include improved customer loyalty, protected revenues, and optimised marketing spend.
Objective
The objective was to refine predictive models capable of identifying customers with a high likelihood of churning within the next month and extending to the next quarter. This foresight allows for the deployment of targeted retention strategies, thus improving customer loyalty and safeguarding the company's revenue trajectory.
Solution
The solution implemented is a sophisticated churn prediction model that utilises advanced analytics to scrutinise customer engagement patterns, transaction histories, and behavioural data. By applying machine learning techniques, the model effectively forecasts the probability of customers disengaging in the short-term (the next month) as well as the medium-term (the following three months). This predictive power enables the business to initiate timely and personalised retention campaigns, addressing at-risk customer segments before churn materialises.
Benefits
Increased Retention
Proactive identification and engagement of at-risk customers contribute to higher retention rates and customer loyalty.
Revenue Protection
By preventing churn, the model safeguards against revenue loss and secures the company’s financial health.
Resource Optimisation
Focused retention efforts on high-risk customers ensure efficient use of marketing resources and maximise ROI on customer retention initiatives.