1997), and their degrees of loyalty may vary or even change. We can model the second (more realistic) scenario using a Markov switching-matrix approach.2
Acquisition and retention. Note that the brand-switching matrix models both the acquisition and the retention of cus- tomers. Acquisition is modeled by the flows from other firms to the focal firm, and retention is modeled by the diag- onal element associated with the focal firm. The retention probability for a particular customer is the focal firm’s diag- onal element, as a proportion of the sum of the probabilities in the focal firm’s row of the switching matrix. Note that this implies a different retention rate for each customer × firm combination (we show the details of this in a subsequent section). This describes the acquisition of customers who are already in the market. In growing markets, it is also important to model the acquisition of customers who are new to the market.
The switching matrix and lifetime value. We propose a general approach that uses a Markov switching matrix to model customer retention, defection, and possible return. Markov matrices have been widely used for many years to model brand-switching behavior (e.g., Kalwani and Morri- son 1977) and have recently been proposed for modeling customer relationships (Pfeifer and Carraway 2000; Rust, Zeithaml, and Lemon 2000). In such a model, the customer has a probability of being retained by the brand in the sub- sequent period or purchase occasion. This probability is the retention probability, as is already widely used in CLV mod- els. The Markov matrix includes retention probabilities for all brands and models the customer’s probability of switch- ing from any brand to any other brand.3 This is the feature that permits customers to leave and then return, perhaps repeatedly. In general, this “returning” is confused with ini- tial “acquisition” in other customer equity and CLV approaches. The Markov matrix is a generalization of the migration model and is expanded to include the perspective of multiple brands.
To understand how the switching matrix relates to CLV, consider a simplified example. Suppose that a particular customer (whom we call “George”) buys once per month, on average, and purchases an average of $20 per purchase in the product category (with a contribution of $10). Suppose that George most recently bought from Brand A. Suppose that George’s switching matrix is such that 70% of the time he will rebuy Brand A, given that he bought Brand A last time, and 30% of the time he will buy Brand B. Suppose that whenever George last bought Brand B he has a 50% chance of buying Brand A the next time and a 50% chance of buy-
1997), and their degrees of loyalty may vary or even change. We can model the second (more realistic) scenario using a Markov switching-matrix approach.2
Acquisition and retention. Note that the brand-switching matrix models both the acquisition and the retention of cus- tomers. Acquisition is modeled by the flows from other firms to the focal firm, and retention is modeled by the diag- onal element associated with the focal firm. The retention probability for a particular customer is the focal firm’s diag- onal element, as a proportion of the sum of the probabilities in the focal firm’s row of the switching matrix. Note that this implies a different retention rate for each customer × firm combination (we show the details of this in a subsequent section). This describes the acquisition of customers who are already in the market. In growing markets, it is also important to model the acquisition of customers who are new to the market.
The switching matrix and lifetime value. We propose a general approach that uses a Markov switching matrix to model customer retention, defection, and possible return. Markov matrices have been widely used for many years to model brand-switching behavior (e.g., Kalwani and Morri- son 1977) and have recently been proposed for modeling customer relationships (Pfeifer and Carraway 2000; Rust, Zeithaml, and Lemon 2000). In such a model, the customer has a probability of being retained by the brand in the sub- sequent period or purchase occasion. This probability is the retention probability, as is already widely used in CLV mod- els. The Markov matrix includes retention probabilities for all brands and models the customer’s probability of switch- ing from any brand to any other brand.3 This is the feature that permits customers to leave and then return, perhaps repeatedly. In general, this “returning” is confused with ini- tial “acquisition” in other customer equity and CLV approaches. The Markov matrix is a generalization of the migration model and is expanded to include the perspective of multiple brands.
To understand how the switching matrix relates to CLV, consider a simplified example. Suppose that a particular customer (whom we call “George”) buys once per month, on average, and purchases an average of $20 per purchase in the product category (with a contribution of $10). Suppose that George most recently bought from Brand A. Suppose that George’s switching matrix is such that 70% of the time he will rebuy Brand A, given that he bought Brand A last time, and 30% of the time he will buy Brand B. Suppose that whenever George last bought Brand B he has a 50% chance of buying Brand A the next time and a 50% chance of buy-
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