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.
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.