Abstract
In the modern environment of service-based marketing techniques, maximizing customer lifetime value has evolved into a crucial objective of CRM, in order to obtain profits from creating and sustaining long-term relationships with their customers. This chapter makes a contribution by reviewing the various CLV techniques and modeling advances in this area and in addition highlights the direction for development. It specifically addresses the key challenges in the literature with regard to integrating dynamic, macroeconomic aspects into the CLV which has become imminent given the current economic and financial turmoil.
This chapter contains contributions from Avanti George, Madras School of Economics, Chennai, India.
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Notes
- 1.
The RFM model is a good benchmark when considering non-contractual settings where transaction can occur at any point in time. It is not an appropriate model for any contractual business settings. Nor is it an appropriate model for non-contractual settings where transactions can only occur at fixed (discrete) points in time, such as attendance at annual conferences, arts festivals, as in such settings, the assumption of Poisson purchasing is not relevant. Thus, models such as Fader et al. (2004) iso-value, beta-binominal/beta-geometric (BG/BB) model, or Morrison et al.’s (1982) brand loyal with exit model would be appropriate alternatives.
- 2.
The second and third assumptions result in the NBD, whereas the next two assumptions yield the Pareto distribution. This model requires only two pieces of information about each customer’s past purchasing history: his or her “recency” (when his or her last transaction occurred) and “frequency” (how many transactions he or she made in a specified time period).
- 3.
Various attempts have been made in the past to model CLV including Schmittlein and Peterson (1994) where they assume that the random purchasing around the individual’s mean is characterized by a normal distribution and that the average transactions values are distributed across the population according to a normal distribution. This implies that the overall distribution of transaction values can be characterized by a normal distribution. Fader (2004) adopts the gamma–gamma model originally proposed by Colombo and Jiang (1999).
- 4.
Specifically, studies that use hazard models to estimate customer retention are similar to the NBD/Pareto models except for the fact that the former may use more general hazard functions and typically incorporate covariates.
- 5.
Thomas et al. (2004a) found that whereas low price increased the probability of acquisition, it reduced the relationship duration. Therefore, customers who may be inclined to restart a relationship may not be the best customers in terms of retention. Thomas et al. (2004) empirically validated this across two industries. They also found that customers should be acquired based on their profitability rather than on the basis of the cost to acquire and retain them. Lewis (2003) showed how promotions that enhance customer acquisition may be detrimental in the long run. He found that if new customers for a new chapter subscription were offered regular price, their renewal probability was 70 %. However, this dropped to 35 % for customers who were acquired through a $1 weekly discount. Similar effects were found in the context of Internet grocery where renewal probabilities declined from 40 % for regular-priced acquisitions to 25 % for customers acquired through a $10 discount. On average, a 35 % acquisition discount resulted in customers with about half the CLV of regularly acquired customers. In other words, unless these acquisition discounts double the baseline acquisition rate of customers, they would be detrimental to the CE of a firm. These results are consistent with the long-term promotion effects found in the scanner data (Jedidi et al. 1999). In contrast, Anderson and Simester (2004) conducted three field studies and found that deep price discounts have a positive impact on the long-run profitability of first-time buyers but negative long-term impact on established customers.
- 6.
For example, eBay defines a customer to be active if she or he has bid, bought, or listed on its site during the past 12 months.
- 7.
The interest in customer retention and customer loyalty increased significantly with the work of Reichheld and Sasser (1990), who found that a 5 % increase in customer retention could increase firm profitability from 25 to 85 %. Reichheld (1996) also emphasized the importance of customer retention. However, Reinartz and Kumar (2000) argued against this result and suggested that “it is the revenue that drives the lifetime value of a customer and not the duration of a customer’s tenure” (p. 32). Reinartz and Kumar (2002) further contradicted Reichheld based on their research findings of weak to moderate correlation (0.2–45) between customer tenure and profitability across four data sets. However, a low correlation can occur if the relationship between loyalty and profitability is nonlinear (Bowman and Narayandas 2004).
- 8.
In survival analysis, an AFT model is a parametric model that provides an alternative to the commonly used PH models. Whereas a PH model assumes that the effect of a covariate is to multiply the hazard by some constant, an AFT model assumes that the effect of a covariate is to multiply the predicted event time by some constant. In both, the AFT parametric and the PH parametric approaches, the Weibull distribution is the most commonly used.
- 9.
Rust et al. (2004) argued that the “lost for good” approach understates CLV because it does not allow a defected customer to return. Others have argued that this is not a serious problem because customers can be treated as renewable resource (Dréze and Bonfrer 2005) and lapsed customers can be reacquired (Thomas et al. 2004). It is possible that the choice of the modeling approach depends on the context. For example, in many industries (e.g., cellular phone, cable, and banks), customers are usually monogamous and maintain their relationship with only one company. In other contexts (e.g., consumer goods, airlines, and business-to-business relationship), consumers simultaneously conduct business with multiple companies, and the “always a share” approach may be more suitable.
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Bhaduri, S.N., Fogarty, D. (2016). Customer Lifecycle Value—Past, Present, and Future. In: Advanced Business Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-0727-9_11
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