Profitable Customer Management: A Study in Retail Banking

The increasing focus of relationship marketing and customer relationship management (CRM) studies on issues of customer profitability has led to the emergence of an area of research on profitable customer management. Nevertheless, there is a notable lack of empirical research examining the current practices of firms specifically with regard to the profitable management of customer relationships according to the approaches suggested in theory. This thesis fills this research gap by exploring profitable customer management in the retail banking sector. Several topics are covered, including marketing metrics and accountability; challenges in the implementation of profitable customer management approaches in practice; analytic versus heuristic (‘rule of thumb’) decision making; and the modification of costly customer behavior in order to increase customer profitability, customer lifetime value (CLV), and customer equity, i.e. the financial value of the customer base. The thesis critically reviews the concept of customer equity and proposes a Customer Equity Scorecard, providing a starting point for a constructive dialog between marketing and finance concerning the development of appropriate metrics to measure marketing outcomes. Since customer management and measurement issues go hand in hand, profitable customer management is contingent on both marketing management skills and financial measurement skills. A clear gap between marketing theory and practice regarding profitable customer management is also identified. The findings show that key customer management aspects that have been proposed within the literature on profitable customer management for many years, are not being actively applied by the banks included in the research. Instead, several areas of customer management decision making are found to be influenced by heuristics. This dilemma for marketing accountability is addressed by emphasizing that CLV and customer equity, which are aggregate metrics, only provide certain indications regarding the relative value of customers and the approximate value of the customer base (or groups of customers), respectively. The value created by marketing manifests itself in the effect of marketing actions on customer perceptions, behavior, and ultimately the components of CLV, namely revenues, costs, risk, and retention, as well as additional components of customer equity, such as customer acquisition. The thesis also points out that although costs are a crucial component of CLV, they have largely been neglected in prior CRM research. Cost-cutting has often been viewed negatively in customer-focused marketing literature on service quality and customer profitability, but the case studies in this thesis demonstrate that reduced costs do not necessarily have to lead to lower service quality, customer retention, and customer-related revenues. Consequently, this thesis provides an expanded foundation upon which marketers can stake their claim for accountability. By focusing on the range of drivers and all of the components of CLV and customer equity, marketing has the potential to provide specific evidence concerning how various activities have affected the drivers and components of CLV within different groups of customers, and the implications for customer equity on a customer base level.

Vision based indoor positioning in a retail environment

Modern smart phones often come with a significant amount of computational power and an integrated digital camera making them an ideal platform for intelligents assistants. This work is restricted to retail environments, where users could be provided with for example navigational in- structions to desired products or information about special offers within their close proximity. This kind of applications usually require information about the user's current location in the domain environment, which in our case corresponds to a retail store. We propose a vision based positioning approach that recognizes products the user's mobile phone's camera is currently pointing at. The products are related to locations within the store, which enables us to locate the user by pointing the mobile phone's camera to a group of products. The first step of our method is to extract meaningful features from digital images. We use the Scale- Invariant Feature Transform SIFT algorithm, which extracts features that are highly distinctive in the sense that they can be correctly matched against a large database of features from many images. We collect a comprehensive set of images from all meaningful locations within our domain and extract the SIFT features from each of these images. As the SIFT features are of high dimensionality and thus comparing individual features is infeasible, we apply the Bags of Keypoints method which creates a generic representation, visual category, from all features extracted from images taken from a specific location. A category for an unseen image can be deduced by extracting the corresponding SIFT features and by choosing the category that best fits the extracted features. We have applied the proposed method within a Finnish supermarket. We consider grocery shelves as categories which is a sufficient level of accuracy to help users navigate or to provide useful information about nearby products. We achieve a 40% accuracy which is quite low for commercial applications while significantly outperforming the random guess baseline. Our results suggest that the accuracy of the classification could be increased with a deeper analysis on the domain and by combining existing positioning methods with ours.