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Published on Friday, April 27, 2018

The Unintended Consequences of Predicting Customer Churn

Customer churn, also known as customer turnover or attrition, is the loss of customers. Customer churn rate is considered by some to be a key metric because the cost of retaining customers is far less than recruiting. Recovered long-term customers may be more valuable than new customers because of their familiarity with the brand.

When modeling customer churn a distinction can be made between gross attrition and net attrition. Gross attrition is the loss of existing customers and their associated revenue for a particular period of time while net attrition is gross attrition plus the cost to recruit similar customers, to replace lost revenue.

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 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 the 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, but 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 enabling you to better evaluate, prioritize and allocate resources.

4. Segment your customer base by loyalty score to gain a greater sense of what your highly loyal customers look.

5. Improve data and enhance modeling such as: propensity to buy, cross & up-sell modeling, 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, helping you build a better business.

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!

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