It is more costly to find and add a new customer than to maintain and grow an existing one.  In fact, Gartner recently reported that marketing budgets for customer retention will increase by more than 50 percent by 2015.  This is especially true in mobile telecommunications, where intense competition means consumers are bombarded with images, slogans and low-cost enrollments to entice them to jump from one carrier to the next.

By mining unstructured data to understand and track the variables that cause a customer to leave a service provider, we can predict the level and likelihood of customer churn – and give a carrier a roadmap for countering churn to help maintain profitability.

Alacer recently worked with a U.S.-based telecommunications firm to help it develop a plan to proactively reach out to vulnerable subscribers before they terminated their contracts.   To do this, we took a profit-centric approach to the task by developing a Profit-Churn score for each customer.  Over 70,000 subscribers with 75 relevant data points were examined, of which more than a fourth had already churned.

By considering the profitability of each customer, we could then prioritize which customers should receive immediate attention, and identify the high cost/low return customers where churn might actually be desirable.

And this is where it got interesting: we learned that 35 percent of the company’s subscribers were more profitable than the average customer, and that they generated a whopping 61 percent of the company’s cumulative expected profit per month (see chart).

Once the most profitable customers were identified, the company could then concentrate its retention efforts on those with the highest likelihood of termination.  It effectively moved the company from a reactive stance to a proactive one – and it’s just one example of how Alacer utilizes big data to provide its clients with a useful framework for developing a marketing plan of action.

 by Ed Sarausad