Managing Dynamics in a Customer Portfolio

Abstract

Although highly relevant for marketing practice, few studies provide conceptual and empirical insights into customer portfolio management. Furthermore, most approaches to analyzing customer portfolios are static. This article discusses three neglected key issues relevant for a dynamic customer portfolio analysis: (1) Does a static versus a dynamic valuation lead to a different prioritization of customer segments in a portfolio? (2) How does offensive or defensive management of segment dynamics affect portfolio value? and (3) Do reliable predictors for dynamics of a customer's position in the portfolio exist? As a tool for customer portfolio analysis, the authors develop a segmentbased customer-lifetime-value model. They capture customer dynamics by analyzing how customers switch between segments of different values across time. The authors apply their tool with longitudinal data from four firms with up to 300,000 customers. The results from the empirical analysis and a simulation study provide answers to the three key issues raised. First, compared with a dynamic analysis, a static approach overestimates the value of some customer segments but underestimates others. Second, a defensive versus offensive management of value dynamics is relatively more appropriate for middle-tier segments, whereas the opposite holds true for bottom-tier segments. Third, general customer characteristics and aggregated transaction characteristics indicate future segment dynamics, whereas specific product usage data differentiate customers according to current value.

Extending customer relationship management: from empowering firms to empowering customers

“…In the mid1990s the rise of analytical CRM allowed firms to analyse large quantities of customer data and identify relevant behavioural data and relationship development stages (Peacock, 1998). It was argued that there was a natural fit between data mining and CRM (Shankar and Winer, 2006; see also Homburg et al, 2009). This was true especially in contexts where the amount of data from a single customer was substantial and provided opportunities to identify different customer segments such as those with the most potential growth and most profitable customers and also to identify emerging customer trends.…”

Dynamics in the international market segmentation of new product growth

“…To accommodate dynamics, we develop a semiparametric hidden Markov model (HMM) that allows country segment membership to vary flexibly over time. The paper extends recent advances in time-varying household segmentation (e.g., Du & Kamakura, 2006;Paas, Vermunt, Bijmolt, & Journal of the Royal Statistical Society, 2007), dynamic customer value segmentation (Brangule-Vlagsma, Pieters, & Wedel, 2002;Homburg, Steiner, & Totzek, 2009) and customer relationship dynamics (Netzer, Lattin, & Srinivasan, 2008) to an international scope and combines them with recent advances in semiparametric modeling of new product growth patterns with penalized splines (Stremersch & Lemmens, 2009).…”

The appropriateness of different modes of strategy from a product-market perspective

“…Thus, environment-oriented activities refer to activities the organization engages in order to cope with the external and internal environments, namely, monitor markets and technology developments (e.g., Kaul, 2012;Sood & Tellis, 2(05), SWOT analysis (e.g., Helms & Nixon, 2010), competitor analysis (e.g., Chen, 1996), portfolio analyses (e.g., Day, 1977;Henderson, 1979, Homburg, Steiner, & Totzek, 2009, market segmentation studies (e.g., Asllani & Halstead, 2011;O'Regan, Kalidas, Maksimova, & Reshetin, 201 I), venture capital activities (e.g., Park & Steensma, 2012), and mergers and acquisitions (e.g., Homburg & Bucerius, 2005;Valentini, 2012).…”