Using the elaboration likelihood model to guide customer service-based segmentation.

Introduction

Microeconomic study describes how the firm seeks to maximize profits given resource constraints. It is not practical for a selling firm to expect all customers to have the same expectations of product offerings nor that all customers have the same proclivity to pay for better product offerings. Sellers must therefore rationalize decisions regarding their product offerings.

Logistic organizations regularly face the dilemma of determining appropriate levels of service, the output of logistics. In an effort to set themselves apart from rivals, firms are increasingly looking to logistics-driven customer service as their competitive weapon. Research has shown that customer service can be a significant source of differentiation and competitive advantage for firms (Lambert and Harrington, 1989; Sterling and Lambert, 1987). Higher levels of customer service generate customer loyalty that boosts long-term sales and profitability (Tucker, 1980). However, firms should only pursue customer service improvements if they expect adequate return from these endeavours (Bowersox and Closs, 1996). Therefore, there is a need for a practical tool that selling firms can use to separate those customers willing to pay for improved logistics services from those who are not.

Market segmentation is a practice that has long been used by consumer marketers and in the last two decades segmentation has received substantial attention in industrial market settings. More recently, research has demonstrated how industrial market segmentation finds application with logistics service. Popular methods of segmentation often experience barriers to implementation, however. As the first stage of a research agenda, this paper discusses the current state of logistical segmentation and introduces a popular consumer behaviour theory, the elaboration likelihood model (ELM), that can enhance a firm's ability to identify feasible, profitable customer segments willing to consider deeply the improved service offering and demonstrate increased commitment and loyalty on purchase. To that end, a review of the ELM and transformation of its constructs will suggest propositions that aim to answer the following general question:

What are the ELM-suggested buying firm characteristics that can be used to identify those firms most likely to respond favourably to customer service improvements with increased commitment and loyalty?

To address the above research question the paper first discusses the market segmentation literature. Following this review and synthesis, the authors address the benefits that theories from other disciplines offer logistics research and practice. The elaboration likelihood model is then introduced and the wealth of research on this model is summarized. Thereafter, the ELM's consumer-level constructs are transformed to a firm level. Finally, using the relationships specified by the ELM, the paper identifies the characteristics a selling firm can use to segment potential buyers according to their likely response to an offer of improved customer service. The paper concludes with a discussion of managerial implications and directions for future research.

A review of market segmentation

Most logistics decisions and operations are industrial in nature. Whether performed by the business itself or outsourced to third parties, the immediate customer for most logistics services is another business rather than a consumer. Obvious exceptions to this generalization include the decisions of retail location and the delivery of direct marketing products. In many cases, however, logistics can rely on the industrial marketing literature for theoretical contributions (Murphy and Daley, 1994). One such theoretical contribution arises from the industrial market segmentation literature.

The concept of industrial market segmentation goes back more than 60 years. Plank (1985), in his often cited review of the industrial market segmentation literature, notes that the term was first coined by Smith (1956) but actually the concept was developed more than two decades earlier by Frederick (1934). In the early 1970s, Frank et al. (1972) developed an extensive framework taken largely from the literature of consumer market segmentation. Building on this work, Wind and Cardozo (1974) apply a hierarchical approach to segmenting organizational markets. They suggest that firms, given a generic product or service, first identify "macro" segments according to the following buyer characteristics: firm size, product usage rate, application of the product, SIC category, organization structure, location and the purchase situation (new versus rebuy).

These macro variables are easy to identify yet sometimes offer incomplete insight and differentiation for sufficient segmentation. Therefore, Wind and Cardozo (1974) suggest that, when possible, further segmentation be enacted across "microsegments", or characteristics of the buyer's decision-making unit. These "up close and personal" characteristics include: identification of authority figures, an assessment of the target firm's communications network, personal characteristics of purchasing personnel, the perceived importance of the purchase, the relative importance of specific determinants in the buying decision, attitudes towards vendors and decision rules. It is believed that a combination of macro- and micro-segmentation is usually sufficient to distinguish worthwhile targets from the pool of potential buyers.

Almost a decade later Bonoma and Shapiro (1983) developed the foremost model of industrial market segmentation. In their effort to bridge the gap between what is practical for sellers to use (identifiable/accessible segmentation) and most relevant theoretically (needs/benefits-oriented segmentation), Bonorna and Shapiro extend the earlier work of Wind and Cardozo to suggest a nested approach to industrial market segmentation. Figure I illustrates the basic model. The nested approach suggests that the buyer start with factors that are company-oriented, general or easily identifiable and continue to seek customer knowledge that becomes increasingly more specific and intimate until worthwhile targets become apparent.

The three outermost layers of the nested model parallel Wind and Cardozo's "macro" bases of segmentation. The outermost layer suggests that industrial marketers first look to demographics to segment customers. Demographic variables provide a broad description of the potential customer and include the company's industry, size and location. The next basis for segmentation includes operating variables. These variables include the customer's technology base, use/non-use of particular products and brands and operating, technical and financial capabilities. The next, more specific layer of the model identifies customers according to their purchasing approaches. Viewed as an often neglected segmentation basis, this nest includes identification of the potential customer's formal purchasing organization, power structure, nature of existing organizational relationships, purchasing policies and criteria (Bonoma and Shapiro, 1983)

The two innermost layers of the …