The Unintended Consequences of Predicting Customer Churn

Customer churn, also known as customer turnover or attrition, is the loss of customers. Some consider customer churn rate to be a key indicator of current and future business health because the cost to retain customers is far less than recruiting new. Recovered long-term customers may also be more valuable than new customers due to brand loyalty. 

A distinction can be made between gross attrition and net attrition when modeling customer churn. Gross attrition is the loss of customers and their associated revenue for a particular period whereas net attrition is gross attrition plus the cost to recruit similar customers to replace lost revenue for the same period.

Churn itself can be categorized as either voluntary or involuntary. Voluntary churn occurs due to a decision by the customer to stop buying from the company, whereas involuntary churn occurs due to circumstances outside the customers' control. Companies interested in better understanding their customers and markets delineate between voluntary churn and involuntary churn.

Predicting customer churn is the process of using statistical modeling to compare the attributes of churn customers to that of non-churn customers. The process assigns a propensity to churn score for each customer, which correlates to how closely they match a churn profile. The resulting output quantifies the risk of losing any given customer.

The obvious objectives for predicting customer churn are to increase revenue by reducing turnover. However, what are some of the unintended consequences? The following by-products are a direct result of predicting customer churn:

1.     Quickly uncover data quality issues and/or information gaps very early in the process, enabling you to improve systems and/or processes.

2.     Establish an unbiased knowledge base allowing current and future stakeholders to work from a common truth as opposed to tribal knowledge.

3.     Discover your true gross and net attrition costs to better evaluate, prioritize and allocate resources.

4.    Enrich customer data by introducing a propensity to churn or loyalty score and gain a greater sense of what highly loyal customers look.

5.    Improve the results of other data models such as propensity to buy, cross & up-sell modeling, customer & market segmentation, marketing mix & marketing attribution.

The benefits associated with data modeling extend far beyond the output of model(s). The very process of developing and interconnecting models will ask questions you did not know needed answers, increase knowledge and drive competitive advantage.

Driving growth in a competitive market is about doing more with less, while translating value to the business. Why wait? Contact Us to gain Tomorrow's Knowledge Today!