Extended conditional trend analysis: Predicting triple period buyer flows with a tri‐variate NBD model

Abstract

Marketers are interested in the loyalty of their customer base. Increasingly this includes examining behavioural loyalty inferred from the frequency or weight of purchase. A typical approach is to divide the customer base into arbitrary segments based on weight of purchase and then attempt to move customers from lighter to heavier segments, rather than have them reduce purchasing or cease buying altogether. Effects can be monitored by examining how purchasing by groups of individuals evolves over successive periods. However, much of the flow between segments represents random fluctuations in period-to-period purchasing rather than true change to underlying loyalty. Accurate analysis requires true change to be separated from these stochastic changes, for example through benchmarks derived from conditional trend analysis (CTA). While CTA considers the two-period case, it provides no guidance for changes seen across three-periods. The three-period case is nonetheless regularly reported by panel companies and relied on by managers. We therefore develop the three-period CTA, using a tri-variate NBD, to allow the analysis of buyer flow across three successive periods. We provide an empirical illustration and demonstrate fresh insights into the evolution of consumer loyalty. The findings allay oftraised concerns about supposedly 'lost' buyers, as perceived customer loss is often simply regression to the mean of the buying rates. Accordingly, the three-period CTA shows predictable proportions of buyers who move between different buying-weight segments, including first-year buyers who were apparently 'lost' in the second year but return to buy the brand in the third year. | INTRODUCTIONMarketing managers are increasingly concerned to examine and grow the loyalty of their customer base. One such analytical approach involves commissioning consumer panel companies to conduct 'buyer-flow analysis' (InfoScout, 2016) that describes the movement over time of cohorts of buyers based on their weights of purchase.The managerial goal is typically to increase the loyalty of the customer base by moving customers from lighter to heavier brand purchasing weights, rather than have customers reduce the frequency of their purchasing or cease to buy the brand altogether. Buyer-flow analysis begins by arbitrarily dividing the customer base into several purchasing segments. A typical classification would involve behavioural segmentation of customers into light-, mediumand heavy-buying groups, along with the group of non-buyers. There are no fixed criteria for segment classification, with weights of purchasing used for segment cut-offs being somewhat idiosyncratic. The analysis relies on determining the extent to which individuals change their weight of purchasing, allowing a manager or analyst to track customer migration between different purchasing segments over consecutive periods; for example, first identifying buyers classified as medium-weight in an initial period and then assessing what