Customer Portfolio Analysis: Applying Financial Risk and Volatility Measures to Customer Segmentation and Risk-Adjusted Lifetime Value Determination

Wangenheim, Florian von; Lentz, Patrick

doi:10.2139/ssrn.782064

Cited by 10 publications

(12 citation statements)

References 25 publications

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“…Nevertheless, this figure tells us little about the applicability of individuals' transaction forecasts in a managerial context. Past best customers may not necessarily belong to the group of future best customers (Wangenheim and Lentz 2005). For example, an airline might want to prioritize high-value customers in an overbooking occasion and deny boarding to lower-value customers.…”

Section: Instant Customer Base Analysis / 89mentioning

confidence: 99%

“…For example, Wangenheim and Lentz (2005) show that trend in revenues (i.e., the slope of revenue regressed on time) is an important predictor of a customer's life-cycle pattern and improves the accuracy of CLV predictions. An appealing characteristic of the stochastic models described initially is that they work only on recency and frequency purchase information.…”

Section: Limitations and Further Researchmentioning

confidence: 99%

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Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

Wübben

Wangenheim

2008

Journal of Marketing

137

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Recently, academics have shown interest and enthusiasm in the development and implementation of stochastic customer base analysis models, such as the Pareto/NBD model and the BG/NBD model. Using the information these models provide, customer managers should be able to (1) distinguish active customers from inactive customers, (2) generate transaction forecasts for individual customers and determine future best customers, and (3) predict the purchase volume of the entire customer base. However, there is also a growing frustration among academics insofar as these models have not found their way into wide managerial application. To present arguments in favor of or against the use of these models in practice, the authors compare the quality of these models when applied to managerial decision making with the simple heuristics that firms typically use. The authors find that the simple heuristics perform at least as well as the stochastic models with regard to all managerially relevant areas, except for predictions regarding future purchases at the overall customer base level. The authors conclude that in their current state, stochastic customer base analysis models should be implemented in managerial practice with much care. Furthermore, they identify areas for improvement to make these models managerially more useful.

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Section: Instant Customer Base Analysis / 89mentioning

confidence: 99%

Section: Limitations and Further Researchmentioning

confidence: 99%

Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

Wübben

Wangenheim

2008

Journal of Marketing

137

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“…Researchers have worked to integrate customer portfolio ideas within the well-developed stream of research focused on measuring CLV and CE (e.g., Von Wangenheim and Lentz, 2005). Buhl and Heinrich (2008) proposed a quantitative model based on financial portfolio theory that (1) considers CLV as well as the associated risks of customer segments and (2) provides a method for adding or subtracting market segments.…”

Section: Customer Equity and The Evolving Understanding Of Riskmentioning

confidence: 99%

Risk considerations in the management of customer equity

Bolton¹,

Tarasi²

2015

Handbook of Research on Customer Equity in Marketing

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“…Different researches in CRM literature have proposed and used Capital Asset Pricing Model to incorporate customer risk in CLV models. (Buhl & Heinrich, 2008;Dhar & Glazer 2003;Gupta et al, 2004;Hogan et al, 2002;Hopkinson & Lum, 2002;Ryals, 2003;Tarasi et al, 2011;Wangenheim & Lentz, 2005). The CAPM model is based on the assumption that investors are rational and risk averse.…”

Section: Research Backgroundmentioning

confidence: 99%

“…This uncertainty is characterized as "risk". Despite the fact that CLV based models are generally formed from financial valuation viewpoint, the aspect of risk, which is central to financial valuation models, has been widely neglected here (Wangenheim & Lentz, 2005;Ryals & Knox, 2005;Sackmann et al, 2010;Stahl et al, 2003;Kundisch et al, 2008;Tarasi, et al 2011). The aspect of risk is usually incorporated in CLV models by means of risk-adjusted discount rate.…”

Section: Introductionmentioning

confidence: 99%

Using downside CAPM theory to improve customer lifetime value prediction in non-contractual setting

Albadvi¹,

Norouzi²

2013

10.5267/j.msl

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Identifying and selecting the most profitable customers from a shareholder’s perspective is of great interest to marketing managers. One promising line in this regard is to explore the customer lifetime value and its profitable management over time. There is a significant body of marketing literature about CLV evaluation in terms of various perspectives. However, much less attention has been paid to the risk associated with customer relationships. Previous researches in this area considered risk as “variability of cash flows generated by customers”, regardless of the trend of variability. Whereas the upside and downside variability from the customer’s expected profitability are extremely different in CRM context. This paper provides a quantitative model based on downside Capital Asset Pricing Model (D-CAPM) to evaluate risk-adjusted CLV and compares the results by employment of traditional CAPM. This paper contributes to this field by extending the discussion on customer risk measurement and provides an approach that enables marketing managers to evaluate the risk of decline from average profitability for different customer segments

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Customer Portfolio Analysis: Applying Financial Risk and Volatility Measures to Customer Segmentation and Risk-Adjusted Lifetime Value Determination

Cited by 10 publications

References 25 publications

Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

Risk considerations in the management of customer equity

Using downside CAPM theory to improve customer lifetime value prediction in non-contractual setting

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