A Conceptual Model for Evaluating Segments - Research Paper Example

Market segment evaluation is maybe the most critical process in the entire segmentation approach because it deals with the assessment of the different segments identified in terms of attractiveness, substantiality and measurability. In this paper there are three models reviewed; the richness curves proposed by Novak et al. (1992), the evaluation matrix proposed by Sarabia (1996) and the strategy-aligned fuzzy MADM model proposed by Ou et al. (2009). All three models have departed from the traditional notion that statistical measures can be used to evaluate segments and have embarked on a more flexible and subjective (to the firm) or firm-specific approach to the segment evaluation. It is suggested by the analysis that a synthesis of these three models can potentially produce a holistic tool for evaluating segments; a tool which considers both the internal and external forces and which incorporates the firm’s specific needs. Introduction The evaluation of market segments is one of the most critical aspects in the entire segmentation analysis, given that the segments that are eventually selected by firms need to be attractive, measurable and substantial (Beane and Ennis, 1987; Sarabia, 1996). Segmentation is crucial because it provides a direction for marketing strategies to be developed; on the basis of segmentation firms design specific marketing activities targeting clusters of the market which are share some degree of homogeneity and which are likely to respond in a similar manner to marketing incentives (Jonker et al., 2004). According to Wind (1978) and Ou et al. (2009) segmentation should be not only approached through statistical measures which only provide a picture of the differences between various segments, but more consistently should be approached through subjective measures as well, which denote the essence of the judgements of the decision makers and as Sarabia (1996) stresses, the distinctiveness of each firm (and each firm’s needs). Thus evaluation of segments becomes of crucial importance because it shows on the one hand the attractiveness of each segment identified but this attractiveness is measured by incorporating the specific-firm aspects as well. In this paper, three models for evaluating segments are presented and assessed in terms of their strengths in an attempt to synthesize a theoretical framework for segment evaluation. Different Approaches to Market Segmentation Market segmentation is treated differently by many academic researchers and scholars. According to Beane and Ennis (1987) the existence of various approaches towards market segmentation reveals the different viewpoints on the subject. Market segmentation is often viewed as clustering the markets in an attempt to identify segments which can be profitable for business, but is also often viewed as simple categorization of the consumers in groups with no essential meaning attached to them. Segmentation can be treated on the basis of geographical data, psychographics, demographics and behavioural data. Other treatment of segmentation may be based on purchase occasion or even benefit clusters (Beane and Ennis, 1987). The different approaches to market segmentation eventually affect the marketing practices of firms in that they illuminate different dimensions of the customer base or the target segment. For example, a demographic segmentation may be useful in products for which consumption is largely dependent on the demographic data of the market, but unequally neglects other elements of the target market which may also have a close relevance to the consumption. According to Allenby et al. (2002) there are two primary differences in the treatment of market segmentation: the ‘ex ante’ and the ‘ex post’; the ex ante approach indicates an exploration of the drivers behind the consumers’ behaviour while the ex post approach indicates an investigation of the responses of the consumers’ behaviour in the various marketing offerings. In general the different market segmentation treatments provide results which are not holistic but focus on specific criteria (drawn from the approach of the segmentation) (Assael and Roscoe, 1976). As Allenby et al. (2002) point out these differences should be resolved by aligning properly the marketing objectives of the firms with the goals of segmentation itself; for example if the objective of the firm is to create a brand positioning strategy then an ex post approach can provide more important results because it looks at the responses of consumers and not the drivers of their behaviour. Assael and Roscoe (1976) appear to have claimed this argument way before, by stating that the differences in the market segmentation treatments need to be resolved simply by integration and coordination of the goals of marketing strategies/practices and the goals of the mere practice of segmentation. Models for Evaluating Market Segments Apart from the fact that the different approaches to segmentation affect marketing practices, the evaluation of the segments becomes an even more multi-dimensional framework. Morisson (1973) has argued that the traditionally used R2 for the evaluation of market segments was no longer a sufficient tool, because it failed to address fundamentals of the actual purchase behaviour of consumers. Wildt (1976) has contradicted these arguments and has shown that the R2 statistic is capable (with modifications and adjustments of errors) to address the gaps identified by Morisson. Since then, much of the literature on the evaluation of market segments has departed from the statistical orientation and has embarked upon a more subject and marketing – oriented analysis. “The difficulty in using R2 for evaluating market segmentation schemes is that it does not reflect the marketing context” (Novak et al., 1992, p. 255). Novak et al. (1992) explain that the commonly used R2 variance measures, can give an overview of the major differences between the various market segments, but fail to provide information about the differences within the market segments (and consequently they fail to increase the knowledge of marketers on which particular consumers within the segments they should direct the marketing activities). According to Novak et al. (1992) this failure occurs because R2 measures variance between all segments identified (even those which are not directly involved with the purchase behaviour- that is non users) and thus ‘symmetric’ evaluation provides explanation of variance between all segments. A more ‘asymmetric’ evaluation is necessary; one which measures the differences between those segments which are directly involved with the purchase behaviour – that is the users for example. The authors propose a model for evaluating segments on the basis of ‘richness curves’. Richness curves are viewed by Novak et al. (1992) as significant in addressing the gaps or the failures of the variance measures; richness curves actually show the concentration of ‘users’ within each segment and therefore provide useful information to marketers in regards to the target market. A richness curve as a model to evaluate segment offers two kinds of information which are crucial in marketing: on the one hand it shows how much marketers would expect to reach consumers in each of the rich segments and on the other hand it shows the concentration of consumers in each of the segments, which is critical in marketing decisions (for example if marketers want to increase the market size then it is more wise to select target segments with lower ‘richness’ (see Appendix A). According to Novak et al. (1992) the strength of the richness curves lies in the fact that they provide information for evaluation in comprehensive and marketing-oriented manner (as opposed to variance measures such as R2). Sarabia (1996) argues that, the evaluation of segments is a non-sufficiently studied and analyzed area due to the fact that the majority of researchers focuses on the segmentation in general but neglect the most important aspect of the process which is the selection of the segments based on the evaluation of the segments. The author, furthermore, suggests that quantitative evaluation of the segments is critical but management considerations (qualitative variables) become more significant because they emphasize the distinctiveness of each firm and consequently the distinctiveness of each firm’s needs. In that respect, Sarabia (1996) proposes a model for evaluating and selecting segments on the basis of an evaluation matrix (see Appendix B). The model consists of four fundamental steps: first of all the firm needs to identify the criteria (variables and indicators) against which the different segments will be measured. Secondly, the firm needs to classify them as ‘short term’, ‘medium term’ or ‘long term’ variables and then assess each of the variables/indicators to the value they have on the firm. When the assessments of each of these are made and the relative weights of the indicators are measured, then the firm constructs an evaluation matrix which consists of four quadrants (based on the values of short term assessments and indicators and long term assessment and indicators). In each quadrant different segments are placed according to their scores obtained from the relative weights of the indicators and the assessment of the importance on the firm; the marketers then need only to pick those segments which are in line with their objectives. For example, a segment which has the highest score in the index (developed by weights and assessment, taking into consideration the short term or the long term orientation) is actually the target segment of the firm. The strength of Sarabia’s (1996) model for segments’ evaluation lies in the fact that it encompasses qualitative factors which influence the selection of the segments. The needs of the firm itself are assessed against the indicators and this adds to the distinctiveness of the model, as it takes into consideration the difference of each firm and each firm’s needs in terms of marketing strategies. This occurs because the identification of the variables/indicators as well as the relative weights and assessments of these are made on the basis of the managerial decision (considering the firm’s position, the firm’s objectives and the firm’s strategic orientation). According to Sarabia (1996) a simple mathematical model fails to reveal the dependencies of the evaluation of the segments on the firms’ differences, and this model is sought to fill this gap. This view is also supported by Wind (1978) who claims that the evaluations of segments need to be flexible and responsive of the strategic position and strategic aims of firms and not be entirely based on statistical findings which reveal differences or similarities between segments. An extension of Sarabia’s (1996) model (though with a distinctive approach) is the model proposed by Ou et al. (2009). Their model originates from the assumption that the strategy of the firm is closely bound to the selection of the target markets on the one hand and that the attractiveness of markets (and the segments of the markets) are underpinned by the five forces developed by Porter. According to Ou et al. (2009) the evaluation of a market at the aggregate level is drawn by the five forces, and consequently the evaluation of the segment on the same market bares similar analysis. The model that the researchers propose is a multi-attribute decision making model (MADM) that evaluates fuzzy variables derived from the competitive analysis of the market. The model suggests that the variables need to be assessed through fuzzy rating scores (assigning weights); each of the five forces has several sub-criteria which distinguish different segments. The decision makers then measure the scores, combine their judgments (subjective judgments) and generate a matrix (MADM matrix) which is offered for the selection of the segment and which is in essence based on the attractiveness of the segment (notably considering the criteria of the five forces) but at the same time employs the subjective evaluation of the decision makers. The model proposed by Ou et al. (2009) is differentiated from the Sarabia’s (1996) model in that it includes external forces which often prove critical and substantial in the evaluation of a segment. In short, apart from the fact that this model considers highly the subjective evaluation of the segments (aligning thus with the claims of Sariabia and Wind that consideration of the firm’s difference needs to be present in the segment evaluation), it also considers equally the external factors (notably the five forces of Porter) as major influential variables to the attractiveness of the segment both in the short term and in the long term. Moreover, the model of Ou et al. (2009) incorporates in the overall evaluation of the segments, the need for aligning the strategy of the firm with the selection of the target segment. This is important because it reveals ‘fit’ between the strategy and the selected target segment. Conclusions All models presented in this study can actually synthesize a rather holistic model for evaluating segments. Novak’s et al. (1992) richness curves is important particularly for the selection of the segment, Sarabia’s (1996) model offers an insight on the ways that marketers can approach the combination of the firm’s needs with the evaluation of the segments and Ou’s et al. (2009) provides a useful framework for incorporating external forces to the evaluation of the segment. Therefore a synthesized model, consisting of a mixture of these three approaches probably yields the best results; decision making which is flexible, direct and considerate of other than the simple purchase behaviour of segments (for example the five forces). Segmentation is critical because it provides a direction to markets as to the resources allocated for the marketing developments targeting particular segments and the specific marketing strategies that can have a strong and positive outcome. Marketing strategies should be designed and implemented based on the identification, evaluation and selection of segments in order to increase profit potentials (Beane and Ennis, 1987; Jonker et al., 2004) References Allenby, G., Fennell, G., Bemmaor, A., Bhargava, V., Christen, F., Dawley, J. et al. (2002). Market segmentation research: Beyond within and across group differences Marketing Letters, 13(3), 233–243 Assael, H. and Roscoe, A.M. (1976). Approaches to market segmentation analysis. Journal of Marketing, 40(4), 67-76 Beane, P. and Ennis, D.M. (1987). Market segmentation: a review. European Journal of Marketing, 21(5), 20 – 42 Jonker, J.J., Piersma, N. and Poel, D. (2004). Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Systems with Applications, 27(2), 159–168 Morisson, D.G. (1973). Evaluating Market Segmentation Studies: The Properties of R2. Management Science, 19(11), 1213-1221 Novak, T.P., Leeuw, J. and MacEvoy, B. (1992). Richness curves for evaluating market segmentation. Journal of Marketing Research, 29(2), 254 - 267 Ou, C.W., Chou, S.Y. and Chang, Y.H. (2009). Using a strategy-aligned fuzzy competitive analysis approach for market segment evaluation and selection. Expert Systems with Applications, 36(1), 527–541 Sarabia, F.J. (1996). Model for market segments evaluation and selection. European Journal of Marketing, 30(4), 58-74 Wildt, A.R. (1976). On evaluating market segmentation studies and the properties of R2. Management Science, 22 (8), 904-908 Wind, Y. (1978). Issues and advances in segmentation research. Journal of Marketing Research, 15 (3), 317-337 Appendix A Figure1: Example of a richness curve (Source: Novak et al., 1992, p. 256) Appendix B Figure 2: Example of a Segment Evaluation Matrix (Source: Sarabia, 1996, p. 71) Read More