Can the negative binomial distribution predict industrial purchases?

Abstract

Purpose This paper aims to extend the known boundary conditions of the negative binomial distribution (NBD) model, and to test the applicability of conditional trend analysis (CTA) – a key method to identify whether changes in overall sales are accounted for by previous non-buyers, light buyers or heavy buyers – in industrial purchasing situations. Design/methodology/approach The study tested the NBD model and CTA in an industrial marketing context using a 12-month data set of purchases from an Australian supplier of a range of industrial plastic resins. Findings The purchase data displayed a good NBD fit; the study therefore extends the known boundary conditions of the model. The application of CTA provided second-period purchasing frequency estimates showing no significant difference from actual data, indicating the applicability of this method to industrial purchasing. Research limitations/implications Data relate to just one supplier. Further research across several industries is required to confirm the generalizability and robustness of NBD and CTA to industrial markets. Practical implications Marketing decisions can be improved through appropriate analysis of customer purchasing data. However, without access to equivalent competitor data, industrial marketers are constrained in benchmarking the purchasing patterns of their own customers. The results indicate that use of the NBD model enables valid benchmarking for industrial products, while CTA would enable appropriate analysis of purchases by different classes of customer. Originality/value This paper extends the known boundary conditions of the NBD model and provides the first published results, indicating the appropriateness of CTA to predict purchasing frequencies of different industrial customer classes.

Digital Loyalty Programmes

“…In addition, its reverse-J shape reflects the fact that most buyers in B2B as in B2C are far lighter than the average purchase rate. Consistent with previous loyalty programme literature, the NBD fit proposes that industrial purchasing follows similar repeat buying patterns as B2C, contrary to some prior commentary, which has suggested that regular timings of orders might constitute a theoretical boundary condition (Sharp et al, 2002) This chapter extends the application of the NBD model, which has seldom been applied in manufacturing and industrial goods markets before (although one recent exception is Wilkinson et al, 2016). The findings extend knowledge of several important buying characteristics to the industrial customer base and imply that the brand buyers behave as expected from many similarly close fittings.…”

“…Penetration: The proportion of all buyers under investigation who buy brand x at least once in a period, measured in percentage (Wilkinson et al, 2016).…”

“…Purchase Frequency: The average number of purchasing made by those who purchase at least once in a period (Wilkinson et al, 2016).…”

“…However, important differences exist between buying behaviours in B2B and B2C (business to consumer) contexts. Industrial manufacturers and their distributors have a relatively smaller customer base with bigger and more regular purchasing, some fixed contractual binding, professional buyers and sellers, a close relationship between parties (Jackson & Cooper, 1988;Wilkinson et al, 2016), and an emphasis on customer retention strategies . These differences may affect the operation of a loyalty programme, or render it ineffective.…”

“…• Limited visibility of channel partner's transactional data decreases the opportunity for category growth. Manufacturers are unlikely to interpret and benchmark behaviour without such information (Wilkinson et al, 2016).…”