2.3 On customer value
In recent years, customer portfolio management (CPM) has focused on optimizing clients’ value to the firm. The company’s interest lies in knowing how much net benefit it can expect from a customer today. These expectations are then used to implement efficient marketing strategies to get the highest return on investment. To that end, two key metrics are estimated by firms: customer lifetime value (CLV) and customer equity (CE) (see the definitions in part 1.3).
According to Blattberg and Deighton (1996), CLV is a temporal variable defined as the revenue derived from a customer minus the cost to the firm for maintaining the relationship with this very customer. As shown by Reinartz and Kumar. (2003), CLV modelling depends on the type of relationship a firm has with its clients. In a contractual relationship, customer defections are observed which means that longer lifetime means higher customer value. Conversely, when the relationship is non-contractual, uncertainty arises between the customer’s purchase behavior and lifetime.
With the development of data collection tools, companies have lots of customer-level data (or customer transaction data) at their disposal to measure CLV (Fader and al. 2005). Consequently, different modelling approaches can be adopted in order to estimate customer value.
2.3.1 Recency Frequency Monetary models
Recency Frequency Monetary (RFM) models are considered the simplest strategy to measure CLV and customer loyalty (Gupta, Hanssens, and Hardie 2006). They aim at targeting specific marketing campaigns at specific groups of customers to improve response rates. RFM models consist in creating clusters of clients based on three variables:
- Recency which is the time that has elapsed since customers’ last activity with the firm.
- Frequency that is the number of times customers transacted with the brand in a particular period of time.
- Monetary that is to say the value of customers’ prior purchases.
However, RFM models have a limited predictive power since they only predict clients’ behavior for the next period.
2.3.2 NBP-Pareto model
In their article on CLV management, Borle and Singh (2008) draw the review of more advanced modelling techniques that can be implemented to estimate customers’ value. A popular method to estimate customer lifetime value is the negative binomial distribution (NBD) - Pareto (Fader and al. 2005) which helps solving the lifetime uncertainty issue. The model takes past customer purchase behavior as input such as the number of purchases in a specific time window and the date of last transaction. Then the model outputs a repurchase probability as well as a transaction forecast for each individual. In Borle and Singh’s research paper, a hierarchical bayesian model is implemented with a view to jointly predict customer’s churn risk and spending pattern. Here, the main advantage of using a bayesian approach is to give priors on CLV’s drivers. The study is based on data coming from a membership-based direct marketing company where firm/client relationships are non-contractual. In other words, the times of each customer joining the membership and terminating it are known once these events happen. Thus the implementation of a sophisticated estimation strategy is justified.
2.3.3 Econometric models
In our study, emphasize is placed on estimating the overall value of a customer portfolio. The methodology we develop is based on a research paper written by our Econometrics teacher Alain Bousquet, whose goal is to provide tools for an efficient management of patent portfolios (Bousquet 2021). The main idea is to consider each patent as an asset with a related value which can generate income if this very patent is exploited. The author emphasizes the importance to focus on the portfolio’s variance on top of its expected value. Specifically, he explains that the variability in the probability of success in the exploitation of patents leads to a decrease in the overall risk to which the portfolio is exposed. This modelling approach can be transposed to customer portfolio analysis with the customer’s value corresponding to the CLV and the probability of exploitation being the opposite of the risk of attrition. In this context, CLV can be estimated either with techniques mentioned above or regression methods. The customer’s risk of churn can be modelled with duration models or machine learning techniques as evoked in 2.2. With this econometric framework, it is expected that customer heterogeneity be a key factor in the total variance of the portfolio’s value.