Customer attrition

Customer attrition, also known as customer churn, customer turnover, or customer defection, is a business term used to describe loss of clients or customers.

Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the "...cost of retaining an existing customer is far less than acquiring a new one." Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients.

Companies usually make a distinction between voluntary churn and involuntary churn. Voluntary churn occurs due to a decision by the customer to switch to another company or service provider, involuntary churn occurs due to circumstances such as a customer's relocation to a long-term care facility, death, or the relocation to a distant location. In most applications, involuntary reasons for churn are excluded from the analytical models. Analysts tend to concentrate on voluntary churn, because it typically occurs due to factors of the company-customer relationship which companies control, such as how billing interactions are handled or how after-sales help is provided.

When companies are measuring their customer turnover, they typically make the distinction between gross attrition and net attrition. Gross attrition is the loss of existing customers and their associated recurring revenue for contracted goods or services during a particular period. Net attrition is gross attrition plus the addition or recruitment of similar customers at the original location. Financial institutions often track and measure attrition using a weighted calculation called Recurring Monthly Revenue (or RMR). In the 2000s, there are also a number of business intelligence software programs which can mine databases of customer information and analyze the factors that are associated with customer attrition, such as dissatisfaction with service or technical support, billing disputes, or a disagreement over company policies.

Industry applications
Financial services such as banking and insurance use applications of predictive analytics for churn modeling [5, 4, 3], because customer retention is an essential part of most financial services' business models. Other sectors have also discovered the power of predictive analytics, including retailing,[1] telecommunications and pay-TV operators (see [2]). One of the main objectives of modeling customer churn is to determine the causal factors, so that the company can try to prevent the attrition from happening in the future (see [2] for a case study). Some companies want to prevent their good customers from deteriorating (e.g., by falling behind in their payments) and becoming less profitable customers, so they introduced the notion of partial customer churn (see [1]).

Customer attrition is a major concern for US and Canadian banks, because they have much higher churn rates than banks in Western Europe. US and Canadian banks with the lowest churn rates have acieved customer turnover rates as low as 12%, by using tactics such as free checking accounts, online banking and bill payment, and improved customer service. However, once banks can improve their churn rates by improving customer service, they can reach a point beyond which further customer service will not improve retention; other tactics or approaches need to be explored.

Research on customer attrition
Scholars such as Gent University's Dirk Van den Poel have studied customer attrition at European financial services companies, and investigated the predictors of churn and how the use of customer relationship management (CRM) approaches can impact churn rates. Van den Poel's studies combine several different types of predictors to develop a churn model. This model can take demographic characteristics, environmental changes, and other factors into account.

Research on customer attrition data modeling done by Tom Au may provide businesses with several tools for enhancing customer retention. Using data mining and software, Au applies statistical methods to develop nonlinear attrition causation models. Au notes that "...retaining existing customers is more profitable than acquiring new customers due primarily to savings on acquisition costs, the higher volume of service consumption, and customer referrals." Au argues that to build an "...effective customer retention program," managers have to come to an understanding of "...why customers leave" and "...identify the customers with high risk of leaving" by accurately predicting customer attrition.

References and External sources

 * [1] Buckinx Wouter, Dirk Van den Poel (2005), “Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting”, European Journal of Operational Research, 164 (1), 252-268.
 * [2] Burez Jonathan, Dirk Van den Poel (2006), "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Expert Systems with Applications, 32 (2), 277-288.
 * [3] Prinzie Anita, Dirk Van den Poel (2005), “Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM”, Decision Support Systems, 42 (2), 508-526.
 * [4] Van den Poel Dirk, Bart Larivière (2004), “Customer Attrition Analysis for Financial Services Using Proportional Hazard Models,” European Journal of Operational Research, 157 (1), 196-217.
 * [5] Pohl Stefan (2007), “Zeitraumbezogene Stornomodellierung in der Kraftfahrtversicherung,” Zeitschrift für Versicherungswesen (ZfV), 59 (11), 361-368.