Positioning & Partitioning A Replication & Extension. Anne Sharp, Byron Sharp and Natalie Redford University of South Australia

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1 Positioning & Partitioning A Replication & Extension Anne Sharp, Byron Sharp and Natalie Redford University of South Australia Track 17 Continuation of the work of Ehrenberg and Bass Abstract The mainstream marketing literature accepts the existence of distinctive brand images which position brands closer to some competitors than others. The closer two brands are perceived to be positioned within a market, the more directly these brands are thought to compete, with each sharing the same customer base. Initial empirical investigation of this issue has been conducted in the context of department store shopping. The results indicated that perceptual differences are not mirrored in patterns of sharing customers between brands. In this instance, behaviours appeared to be driven more by physical location than image closeness. This paper further builds on the empirical investigation, this time through examining a context where location issues are controlled for. A chilled, non-alcoholic beverage market is examined, where the range of brands was available in the majority of stores, but some functional and perceptual differences did exist. Both customer perceptual data and duplication of purchase data for the key brands in the market were examined. Comparisons were made between a brand s image positioning and any market partitioning in terms of purchase patterns. Again, the results provide little evidence of the impact of brand image differences on purchase behaviour. Rather, market partitions appear to be based on functional differences between brands. This research provides further support for the initial findings in this area. The Purpose of this Study The concept of brand image is one that is closely tied to differentiation. It is commonly assumed that brands that share similar brand images are less differentiated from each other than they are from other brands. The creation and maintenance of brand image is thus of strategic importance, being a crucial aspect of a firm s competitive strategy. Company failures and great financial losses have been ascribed as consequences of getting a company s image positioning wrong (see Ries and Trout, 1986). This idea of image-based differentiation states that, since brands differ in their positioning, so too will consumers differ in their individual brand choice (Bass, Givon et al. 1984). However, the idea of image differentiation leading to differences in a brand s customer base does not appear to be founded on a body of supporting empirical evidence. Andrew Ehrenberg (1988) has been one of the few critics of the brand image concept arguing that, since the observed differences between brands behavioural loyalty levels are systematically linked to market share differentials, there is little evidence of the impact of distinctive brand images (i.e. there are only smaller brands and bigger brands). In addition, an extensive analysis of purchase data across varied consumer goods categories and multiple countries, found that similar and dissimilar competitive brands are bought by much the same kinds of customers (Hammond, Ehrenberg et al. 1996; Kennedy, Ehrenberg et al. 2000). That is, there is no strong evidence of market segmentation between competitive brands, at least in the consumer goods area. This does not, however, address the issue of market partitioning. 2546

2 While two brands may have customers that have common demographic characteristics (i.e. belong to the same demographic segment) the brands may still be in different sub-markets of the category. Work looking at market structure has indeed found that such market partitioning exists (Rubinson, Vanhonacker et al. 1980; Grover and Srinivasan 1987) though it tends to be due to major functional differences in product features/function rather than image positioning (Uncles, Ehrenberg et al. 1995). This paper sets out to further investigate this issue of brand image differences being mirrored in brand usage patterns. Methodology The chosen category was iced coffee flavoured milk a very popular category in the market under study. There were five major brands produced by two companies. The brands (and companies)- were: Farmers Union (Farmers Union), Dairy Vale (Dairy Vale), Max (Dairy Vale), Feel Good (Farmers Union), and Take Care (Dairy Vale). These constituted the major iced coffee brands in the market, with each having enough market share to be considered a serious player. It was also widely thought that there was a fair deal of differentiation between the 'brands' in this category, as reflected in their differing advertising. The perceptual and behavioural data was collected from the same population of respondents (people who drank iced coffee in the last month). This allowed for direct comparison of perceptual data with behavioural data and removed sampling error. The behavioural data was derived from 18 weeks of repeat-purchase data, collected through a panel of 247 respondents. Data was collected on the purchasing behaviour of the different brands and purchase frequency. This data included every purchase occasion and record of brands purchased. Respondents were posted diaries in which to record their purchase behaviour. This data was collected via weekly telephone interviews conducted by Interviewer Quality Control of Australia (IQCA) accredited interviewers. The panel data was used to look for any partitioning in behaviour, through examining duplication of purchase between brands. That is, what proportion of buyers of brand X are also buyers of brand Y. Duplication of purchase statistics for each brand were compared to the average level of duplication in the category and to the known empirical generalisation derived from the Duplication of Purchase Law. Deviations from these benchmarks indicate market partitioning. Perceptual mapping and the construction of a brand image maps using Correspondence Analysis were used to examine the level of image-differentiation. Free association was used with consumers to identify relevant brand image attributes for the category and brands. The particular association technique adopted was successive word association, where the respondent is asked to list all words or thoughts that occur after being exposed to the given cue. This was done with a convenience sample of 30 people. Respondents were asked which attributes they associated with each brand using the "pick any" method that has been shown to produce image scores in line with other scale-type measures (Barnard and Ehrenberg 1990; Driesener and Romaniuk 2002). 2547

3 Table 1: Image attributes for brands a fattening brand appeals to men a working class brand a rich/sweet brand a XXX (local area) brand a traditional/old fashioned brand an unpopular brand a brand for fat/ugly people a premium quality brand a healthy brand very fresh a brand for yuppies a high caffeine brand a new brand a nutritious brand a brand for women a brand for beautiful people a tough brand a low fat/calorie brand a brand for children a popular brand a good hangover cure a minor brand The perceptual data was then compared with behavioural data on duplication of purchase between brands. The greater duplication of purchase levels should occur between brands that have similar positions in the consumers minds, if brand image and purchase behaviour are linked. Results: The Mapping of Brand Images The correspondence map displayed excellent association between capturing the totality of the data and a two dimensional plot (goodnesss of fit = 0.83). A CGS plot (see Carroll, Green et al. 1987) rather than a French plot was also used in this analysis for ease of visual interpretation. Also informed commentators felt the map validly captured the brands image positioning which was underpinned by some functional differences. tough men Max working class hangover cure hi caffeine new unpopular FG TC nutritious minor brand women beautiful people low fat healthy FU popular ugly people fresh fattening yuppie brand Sth Australian DV childrens brand traditional premium brand sweet Figure 1: Perceptual map of iced coffee brands The perceptual map suggests three partitions in the market; Take Care and Feel Good plot close together near the dimension of healthy, low fat and female oriented. Indeed these two brands are the two low-fat milk iced coffees on the market. On this functional aspect, they are highly differentiated from the other brands. 2548

4 The second partition sees Max clearly positioned as a brand for working class males and being a high caffeine coffee. Farmers Union is closest to Max, and is seen as popular and is indeed the brand leader. However, it really stands on its own, not positioned on any definitive image aspect apart from being the most popular. And when the popular/unpopular variables are removed from the analysis there is no change in the Farmers Union position relative to other brands. Again we would draw the conclusion that Farmers Union is seen as the epitome of iced coffee rather than a brand positioned for any distinctive part of the market. Dairy Vale is positioned near the premium/traditional axis and near sweeter. Results: Duplication of Purchase Patterns In order to see if partitioning is occurring in the industry, it is necessary to know what levels of brand duplication should be normally expected for each brand in each industry, given its level of market share. These benchmarks are provided by the Duplication of Purchase Law and are captured within the Dirichlet model of repeat-purchase patterns (Goodhardt, Ehrenberg et al. 1984; Ehrenberg 1988; Uncles, Ehrenberg et al. 1995). The Dirichlet is widely supported, having been tested for over 30 years and across European, US, Asian and Australasian markets (Uncles, Ehrenberg et al. 1995) The dominant factor in such purchase duplications is simply each brand s penetration. This relationship with the overall brand penetrations is called the Duplication of Purchase Law. This law says that the proportion of customers of brand X who also buy brand Y is proportional to brand Y s penetration in the population as a whole. Calculating the duplication coefficient D as Av.Duplication/Av.Penetration is a simplification but one which works well in practice, at least for brands with penetration less than 50%, which is true for all brands in this market. b Db x y x Where b is the brand s penetration and D is the Duplication of Purchase coefficient Some clusters of brands or submarkets with higher duplications can also arise (Ehrenberg, Uncles et al (forthcoming)), these are revealed as deviations from the Duplication of Purchase Law. This phenomenon is called market partitioning. Table 2 summarises the duplication patterns seen between the brands. The way the data is presented makes it easy to see particularly high or low duplications between pairs of brands (Goodhardt 1972). The diagonal element of the table should constitute 100% in each cell - buyers of Brand A also buying Brand A. The table is best interpreted by reading down each column. 2549

5 Table 2: Duplication of Purchase % ever bought % who then bought Buyers of Farmers Union Dairy Vale Take Care Max Feel Good Farmers Union Dairy Vale Take Care Max Feel Good Average Expected. Dupe MAD From Table 2, we can see that the top three brands share their customers the most, as would be expected. As an example of how to read the table, 21% of people who purchased Farmers Union, also purchased Dairy Vale and 43% of Dairy Vale purchasers also purchased Farmers Union. This is the expected pattern, that big brands (in this case Farmers Union) share their customers less (ie have lower DoPs) than smaller brands (in this case Dairy Vale). The table reveals that most deviations from the average DoP are minor, as shown by the Mean Average Deviation, with the exception of Feel Good and Take Care. These two brands have deviations from the expected duplications of +15 and +25 respectively. Clearly, these two brands are sharing their customers to a far greater degree than predicted by the Duplication of Purchase law. This partition can be best ascribed to not just perceptual similarities between the brands, but also their real functional differences. Both these brands are the only low fat (diet) iced coffee brands with no sugar on the market. It appears to be this functional difference that is driving the partition, rather than just the brands perceptual similarities. Interestingly, brands that are positioned perceptually further apart, such as Max and Dairy Vale, do not share their customers to a markedly less extent than expected. This would again support the idea that functional differences, rather than perceptual ones, drive the deviations from expected DoP patterns. Overall then, the major pattern shown by the DoP table reveals that repeat-buying is largely due to how big the brand is (market share), rather than a particular positioning of the brand. Any partitions are due to major functional differences between brands as found by Uncles Ehrenberg et al (1995). 2550

6 Conclusion The results of this study provide further empirical evidence that perceptual differences between brands do not automatically translate into differences in buying behaviour. However, brands that are positioned closely together and that have functional differences from the other brands in the market, do appear to have higher sharing of customers than predicted by the Duplication of Purchase law. More replication is needed to see if instances can be found where perceptual differences alone are enough to cause such deviations. References Barnard, N. R. and A. S. C. Ehrenberg (1990). "Robust Measures of Consumer Brand Beliefs." Journal of Marketing Research 27(November): Bass, F. M., M. M. Givon, et al. (1984). "An Investigation Into the Order of the Brand Choice Process." Marketing Science 3(4, Fall): Carroll, J. D., P. E. Green, et al. (1987). "Comparing Interpoint Distances in Correspondence Analysis: A Clarification." Journal of Marketing Research 24(November): Driesener, C. and J. Romaniuk (2002). A Comparison of Perceptual Mapping using Ranking, Rating and Pick any Technique. ANZMAC, Mebourne. Ehrenberg, A. S. C. (1988). Repeat-buying: facts, theory and applications. London, Oxford University Press. Ehrenberg, A. S. C., M. D. Uncles, et al. (2003 (forthcoming)). "Understanding Brand Performance Measures: Using Dirichlet Benchmarks." Journal of Business Research. Goodhardt, G. J. (1972). Loyalty Reports - A Coded Example. ESOMAR Seminar "Panels", Amsterdam, The Netherlands, European Society for Opinion and Marketing Research. Goodhardt, G. J., A. S. C. Ehrenberg, et al. (1984). "The Dirichlet: A Comprehensive Model of Buying Behaviour." Journal of the Royal Statistical Society 147(part 5): Grover, R. and V. Srinivasan (1987). "A Simultaneous Approach to Market Segmentation and Market Structuring." Journal of Marketing Research 24(May): Hammond, K., A. S. C. Ehrenberg, et al. (1996). "Market Segmentation for Competitive Brands." European Journal of Marketing 30(No. 12): Kennedy, R., A. Ehrenberg, et al. (2000). Competitive Brands' User-Profiles Hardly Differ. Market Research Society Conference (UK), Brighton, England, Market Research Society (UK). Ries, A. and J. Trout (1986). Positioning: The Battle for Your Mind. New York, McGraw Hill Inc. 2551

7 Rubinson, J. R., W. R. Vanhonacker, et al. (1980). "On 'A Parsimonious Description of the Hendry System'." Management Science 26(2, February): Uncles, M., A. Ehrenberg, et al. (1995). "Patterns of Buyer Behavior: Regularities, Models, and Extensions." Marketing Science 14(No. 3, Part 2 of 2): G61-G