This chapter provides a preliminary study of customer satisfaction and loyalty, as key elements of the churn problem, from a supervised perspective. The experiments concerned data from petrol station usage surveys.
The performed analyses focus on classification mostly from the point of view of the achievement of in- terpretability. This interpretability of the results is paramount for actionable marketing. Feature relevance determination for feature selection and rule extraction were the tools used for achieving such required inter- pretability. Hence, it’s noted how the application of ARD enables the selection of 7 features (v1.- “Personal attention from staf”; v7.- “Hygiene and maintenance of the installations”; v3.- “Additional services”, v5.- “Signs inside installations”; v6 “Modern and attractive installations”; v14.- “Attractive and stocked shop” and v16.- “Payment cards with discounts”) as those which are more relevant for the classification of overall satisfaction. In this regard, it must be emphasized the non appearance of two feature groups:
1. v15.- “Price”, v11.- “Exact and reliable pumps” and v13.- “Top quality fuel”. Their non appearance indicates a mature and regulated market, where old procedures of fraud and / or abuse have been surpassed. Currently, petrol station users in Spain have internalized the quality of the replenishment service at competitive prices.
2. v2.- “Speed and efficiency of staff”, v4.- “Ease access to installations, well indicated” and v9.- “Extended opening hours”. For its part, the non appearance of these features as relevant is highly related with the profile of costumers that used the petrol stations of the analyzed brand. Thus, it was noted that customers were mainly “seniors”, expecting an attended service, additional services and personal attention from staff.
The subsequent rule extraction using OSRE enabled to denote that only five of the previously selected features were relevant. Thus:
• v1.- “Personal attention from staff” appeared in all of the 9 obtained rules. • v14.- “Attractive and stocker shop” appeared in 8 of the 9 obtained rules.
• v7.- “Hygiene and maintenance of the installations” appeared in 6 of the 9 obtained rules. • v6.- “Modern and attractive installations” appeared in 3 of the 9 obtained rules.
• v3.- “Additional service” appeared in 2 of the 9 obtained rules.
The absence of features v5.- “Signs inside installations” and v16.- “Payment cards with discounts” should be understood, from an interpretability point of view, as the existence of remotely significant differ- ences regarding to these features between the different petrol station competitors.
The obtained results were consistent with recent theory on satisfaction, loyalty and switching barriers models. The attributes and rules obtained in the described experiment enabled the company to define a decalogue of actions from which, currently, they evaluate and reward the performance of the petrol stations (owned or managed) members of the company’s network.
Chapter 6
Unsupervised churn analysis in a
telecommunications company
One of the major challenges faced today by telecommunications service providers is how to retain their customer base. Immersed in an extremely competitive market, they must engage in strategies to limit cus- tomer defection to competitors -a phenomenon also known as churn-. Anticipating the customer’s intention to abandon facilitates the launching of retention-focused actions and it represents a clear element of com- petitive advantage. As we introduced in Chapter 3, Data Mining techniques can assist churn (customer attrition) management processes and may provide clues to explain and anticipate churn [104]; and one analytical tool to this purpose is data clustering for market segmentation.
Thus, whereas in Chapter 5 we focus on customer satisfaction as a key point for churn prevention (see Figure 5.1), in the present chapter we focus on proactive bonding (see Figure 6.1). In particular, we propose an indirect and explanatory approach to the prediction of customer abandonment, based on the visualization of customer data -consisting of their consumption patterns- on a two-dimensional representation map, to explore the existence of abandonment routes in the Brazilian telecommunications market.
Our approach is based on two basic hypotheses:
• Different patterns of service consumption, regarding the type of communications established, corre- spond to different levels of predisposition to abandon;
• Different migration routes between time periods are likely to exist and be identifiable in the repre- sentation map, both negative: towards lower customer value and, eventually, service abandonment; and positive: towards higher customer value areas.
Two probabilistic neural network-inspired models of the manifold learning family (GTM and FRD- GTM) are used for the simultaneous visualization and clustering of multivariate data corresponding to customers of a principal Brazilian telecommunications company. These models allow the estimation of the relative relevance of each data feature on the definition of the obtained cluster structure and, in doing so; it eases the interpretability of the segmentation results. From these results, typical customer churn routes are investigated. Several indices of cluster validity for this model are also defined.
Thus, in the present chapter we first introduce the marketing problem and the data features used in the experiments (Section 6.1 and Section 6.2). This is followed by a summary description of the theory behind our approach (Section 6.3). Finally, we describe the developed experiments and the obtained results (Section 6.4) and the conclusions of this chapter (Section 6.5).
Results of this research were presented at the 15thEuropean Symposium on Artificial Neural Networks (ESANN 2007) [85] and at the 2ndSymposium on Computational Intelligence (IEEE SICO 2007) [88].