Fuzzy Target Groups in Analytic Customer Relationship Management

Management Summary

In this Chapter, the application of analytics (quantitative decision support) to customer relationship management (CRM) is discussed. It is shown how three analytic techniques, selection (finding relevant attributes), visualization (plotting relevant associations) and prediction (estimating relevant class membership) help increase success of CRM-based marketing activities. Furthermore, it is shown how inductive fuzzy logic techniques provide a technical means to support these three types of analyses, and a prototype implementation is presented.

The benefit of analytic CRM is its focusing on relevant information in order to increase efficiency. Selection, visualization and prediction can help optimize efficiency of CRM and Marketing resource investment by quantitative decision support. The fuzzy logic method presented in this chapter, inductive fuzzy classification (IFC), is a research approach which can be applied to support decisions in CRM-based marketing either in a human oriented or automated approach. Its benefit is (1) a selection and relevance filtering algorithm which works for both numeric and symbolic data; (2) a standardized semantically intuitive visualization technique for human decision makers; and (3) a method to increase predictive accuracy of existing models by transforming enterprise information into the fuzzy domain.

The basic idea is to compute fuzzy membership degrees to desirable targets from existing data (likelihood-based inductive approach). The resulting models, called membership functions, can be used in targeted database marketing in order to identify relevant customer attributes regarding a target such as product affinity; to plot the corresponding data-based target associations of relevant customer attributes in two dimensions; and to improve campaign response rates and maximizing efficacy of predictive models by transforming customer attributes into membership degrees to target likelihood. Specific visual examples can be found in Figures 2, 3, and 5. The section Case Study shows a real world instance.

Fuzziness

The world is not just black and white: Sometimes, statements are in-between true and false. This gradual concept of truth is the basis for fuzzy logic or approximate reasoning, as proposed by Zadeh (1975) and Bellman & Zadeh (1977). Fuzzy Logic, based on the concept of fuzzy sets introduced by Zadeh (1965) allows propositions with a gradual truth value and thus supports approximate reasoning, gradual and soft consolidation and non-exclusive classification. Fuzziness is an imprecision of boundaries, of concepts that are not clearly defined. Fuzzy logic provides a mathematically precise definition of fuzzy concepts, if those concepts are ordinal, by assigning a gradual membership degree to its elements. Fuzzy propositions lead to fuzzy classes, which allow gradual, fuzzy class boundaries. Fuzzy classification is a method for imprecise data consolidation, where the degree of membership in classes is gradual (Del Amo, Montero, & Cutello, 1999). The application of fuzzy classification to analytical customer relationship management (aCRM) has the advantage of precisiation of fuzzy concepts (Zadeh, 2008, p. 14) in the context of decision support for direct customer contact (Werro, 2008). This precisiation can be achieved by inducing membership functions to fuzzy target classes (Setnes, Kaymak, & van Nauta Lemke, 1998).