Challenge
Observations in markets revealed a need to focus on understanding the customer retention rate. Customer churn is a problem for any tech business. Its definition is simple – churn happens whenever a customer stops doing business with your company or stops buying your product. The impact of losing a customer does not result in just revenue loss. but also impacts the trust in your brand in the markets. The cost of customer acquisition can range from a few dollars to millions of dollars; hence losing a customer also implies a negative impact on the return on that investment. The challenge was to build a churn and potential customer add prediction model which can help marketers to drive the engagement and retain the customers.
It can costs 5 times more to attract a new customer, then it does to retain an existing one!
Idea and Solution
A new predictive model dashboard which predicts potential Customer Churns based on past consumption trend and time series forecasting. The algorithm used was multiple linear regression. It provides a holistic view of the marketing health of the accounts basis marketing interactions and open pipelines. Further, it links the propensity of accounts to determine the potential for being a new customer.
The Customer Churn Prediction Model
Results
Marketers and sellers can take advantage of the view to engage these accounts proactively before they churn. Further, it helps to identify potential customer churns or adds with a view of their recent engagements and open opportunities all within a couple of clicks. This has resulted in minimizing cost and maximizing revenue together with rock solid trust building in the markets because the customers know that we care for them.
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