Retention ViewTM
Many loyalty models rely solely on attitudinal data, such as likelihood to repurchase, or overall satisfaction survey responses. Unfortunately, such data rarely provides the precision required for managerial decisions. In contrast, Retention View models actual customer switching behavior to understand and predict customer retention. Retention View provides guidance for developing or modifying retention programs and allocating company resources in retention efforts. Specifically, Retention View delivers:
- A clear diagnosis of what factors are driving retention or switching based on responses to questions regarding: customers’ perceptions of product or service performance, customers’ knowledge and beliefs, and customers’ sentiments regarding a company and its offerings.
- Quantification of the relative importance of the ‘drivers’ of retention to maximize the impact of subsequent retention efforts.
- Measures of how well Retention View has explained customers’ choice behavior.
- A Retention Index that may be used with selected survey questions to estimate the current state of customers’ loyalty. The Retention Index may also be used to provide “what if ” loyalty scenarios based on projections of increasing or decreasing retention drivers.
Modeling Retention
Actual customer behavior is the key metric for understanding what drives customer retention and switching, and is the foundation of the Retention View process. Combined with feedback obtained through specialized customer surveys, models are formed and estimates made of the drivers of retention or switching.
Conceptually, Retention View models retention/switching as a binary choice process (0 or 1) that is a function of survey ratings gathered from a client’s customers regarding product, service, and company attributes.
This straightforward process is easily illustrated in the figures above. In Figure 1, switched customers and retained customers have the same distribution of an attribute’s ratings, yielding a straight line or 50/50 probability of retention. In Figure 2, we see a large difference in the pattern of the ratings with switched customers providing generally low ratings and retained customers providing generally high ratings. This pattern of responses yields an S-shaped curve such that higher ratings indicate a higher probability of retention while low ratings indicate the likelihood of switching.
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