Investigating brand loyalty using Dirichlet benchmarks: The case of light dairy products

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1 University of Aarhus From the SelectedWorks of Polymeros Chrysochou 2009 Investigating brand loyalty using Dirichlet benchmarks: The case of light dairy products Athanasios Krystallis Polymeros Chrysochou Available at:

2 Investigating brand loyalty using Dirichlet benchmarks: the case of light dairy products Athanasios Krystallis 1 and Polymeros Chrysochou 2 1: MAPP, Aarhus School of Business, Haslegaardsvej 10, DK-8210, Aarhus V, Denmark, atkr@asb.dk 2: MAPP, Aarhus School of Business, Haslegaardsvej 10, DK-8210, Aarhus V, Denmark, polyc@asb.dk 1

3 Investigating brand loyalty using Dirichlet benchmarks: the case of light dairy products Abstract During the last years, a strong consumer interest appears for food products with low caloric content ( light products). Due to their popularity, the real success of these products in the marketplace is a worthinvestigating issue. The creation of buyers that are loyal to light food brands constitutes an indication of this success. The present work aims to investigate consumer loyalty to light dairy (milk and yoghurt) brands. First, basic Brand Performance Measures (BPMs) are empirically estimated to describe market structure of the dairy categories under investigation. Then, the Dirichlet model (Ehrenberg et al., 2004) was fitted to the empirical data, pointing out to theoretical category loyalty measures. Grouping of the dairy categories under investigation according to their purchase frequency and brand penetration then follows. The work concludes with the overall estimation of consumer loyalty to the light dairy sub-category compared to other sub-categories that exist within the wider dairy categories under investigation. The total market share of light brands is found to be directly comparable with that of full fat brands. The importance of the light sub-category is indicated by both the empirical BPMs and the theoretical Dirichlet-type loyalty measures in both milk and yoghurt categories, since consumers appeared to be more loyal to light brand names. However, while the marketing differentiation strategy of the light sub-category seems to be particularly successful in creating loyal buyers, it must be examined within the wider category that it belongs. This conclusion is justified by the fact that, in the grouping of categories that has been attempted, not a clear trend followed by all light brands has been observed. On the contrary, light brand exhibited diverse loyalty behaviour. Introduction The intensified relationship between diet and health changes contemporary food consumer behaviour at a fast pace. A strong demand for food products with health protecting or enhancing properties has recently emerged, among which low caloric content products or simply light foods play a substantial role. The need for a reduced fat consumption is not simply a well-grounded perception in consumers mind. Away from some controversial aspects of the issue (e.g. how much healthy are food products that have undergone chemical treatment to reduce their naturally occurring fat content), international bodies such as the WHO have moved on to recommendations, such as decrease of the fats daily intake by 30% of the total caloric intake, lowering of the saturated fatty acids intake bellow 10% of the total energy intake, etc. (Colmenero, 2000; Popkin et al., 2001; Roininen et al., 1999; Roininen & Tuorila, 1999; Solheim & Lawless, 1996). As a result, world low caloric food production has been nowadays expanded to such an extent that it is now considered a multi-billion market. Since the specific products are very popular, and are being introduced in the food supply chain with a growing speed that indicates an extensive investment on behalf of the food industry, their real success in the market is a worth-investigating issue. From a marketing communication perspective, it is particularly interesting to examine whether the provision of marketing information about the (reduced) caloric content of a food product constitutes an effective brand selection criterion (Solheim & Lawless, 1996). Moreover, it is useful to examine whether this type of information promotes the creation of buyers loyal to a food brand, especially in relation to other food brand loyalty creation strategies (e.g. information about food enrichment with functional ingredients or about organically produced food). Based on the above, the present work aims to investigate consumer loyalty towards light dairy (milk and yoghurt) brands. First, basic Brand Performance Measures (BPMs) are empirically estimated to describe market structure of the dairy categories under investigation. Then, the Dirichlet model s fit to the empirical data is examined (Ehrenberg et al., 2004), pointing out to the theoretical estimation of category loyalty measures. Grouping of the dairy categories under investigation according to their purchase frequency and brand penetration then follows. The work concludes with the overall estimation of consumer loyalty to the light dairy sub-category compared to other sub-categories that exist within the wider dairy categories under investigation, using the polarization index estimated from the Dirichlet model (Fader & Schmittlein, 1993). Methodology Theoretical estimation of brand performance measures and the Dirichlet model The Dirichlet model constitutes one of the most widely adopted models related to empirical generalisations in marketing. The model and its empirical generalisations have been extensively tested and supported by numerous studies that cover very different types of markets (Ehrenberg et al., 2004; Goodhardt et al., 1984; Sharp et al., 2002; Uncles et al., 1995). The model has been proven to apply for more than 50 product categories and has particularly valuable repercussions, especially in relation to the theoretical analysis of BPMs 2

4 (Ehrenberg et al., 2004; Uncles et al., 1995) and the resulting market structure analysis of various product (sub)categories. The main finding stemming out of the Dirichlet model about buyer behaviour is that, irrespectively of the way market data are gathered, markets follow certain systematic loyalty patterns (benchmarks). These patterns remain stationary across many product categories, in many countries and in various points in time (Ehrenberg et al., 2004). For instance, a usual market pattern is that the smaller brands (i.e. brands with smaller market share) stimulate smaller loyalty among their buyers than the larger brands do among theirs (Double Jeopardy effect, McPhee, 1963). Another market trend frequently observed is that large brands tend to monopolise the light buyers (i.e. those that buy less frequently from the category) (Natural Monopoly effect, McPhee, 1963). The usefulness of the Dirichlet model mainly lies in the fact that it can be used as a basis for the estimation of theoretical BPMs and the identification of loyalty patterns within a product (sub)category. From a statistical perspective, the Dirichlet model is constituted by a combination of two probability density functions, the Negative Binomial Distribution (NBD) and the Dirichlet Multinomial Distribution (DMD), which are considered to be independent and their parameters non-related. Calculating theoretical estimations from the Dirichlet model parameters is considerably complicated (Ehrenberg, 1988; Ehrenberg et al., 2004). However, there are software packages that facilitate this task (Kearns, 2001; Uncles, 1989). Empirical estimation of Brand Performance Measures (BPMs) and Juster scale BPMs, such as penetration, average purchasing frequency, brand market share, etc. describe the structure of a market (e.g. a product category) in relation to the loyalty exhibited by the buyers of the market (category) and are usually the outcome of supermarket scanner data analysis. Scanner data are available for many product categories and have been used for the analysis of real purchasing behaviour of consumers for quite some time now; yet they remain expensive in terms of money to acquire and time to analyse. An approach alternative to the collection of scanner data is through the completion of a questionnaire that includes the Juster Probability Scale (JPS), according to which it is required by consumers to estimate the possibility to purchase specific brands of a product category within a pre-specified future time period. Answers are provided in an 11-point probability scale, with end-points from 0 to 10, where 0 corresponds to the absolutely improbable and 10 to the absolutely probable possibility choices (Wright et al., 2002). The JPS estimates consumers possibility to engage in a future behaviour towards specific brands, but does not constitute a means to investigate general consumer attitudes (Wright et al., 2002). This scale is preferred over other purchasing intention measures, since it has been proven to be more reliable and precise, constituting a direct means to estimate real purchasing behaviour (Wright & MacRae, 2007). Purchasing behaviour data by means of the JPS can be collected at a relatively lower cost and in shorter time; they concern a wide variety of products; and substitute scanner data when the latter are impossible or unprofitable to collect. The specific scale can be used for the estimation of BPMs that are necessary inputs for the estimation of the Dirichlet model (Wright et al., 2002) see following paragraphs. Wright et al. (2002) concluded that Juster estimates are very close to those estimated from actual purchase data (e.g. scanner data). The present survey follows the above-described approach. The answers to a JPS are transformed into purchasing probabilities and are then used for the estimation of BPMs, such as penetration and purchase frequency at the brand level, as well as at the product category level, and market shares at the category level. Using the specific BPMs, it is possible to empirically estimate the Dirichlet model, from which additional BPMs can be estimated, such as the share of category requirements and the percentage of solelyloyal buyers. Statistically-wise, estimations of BPMs are relatively easy and are analytically described by Wright et al. (2002). The JPS answers are transformed into purchasing probabilities (p ij ) by being divided by 10. The following equations are used for the estimation of BPMs that are necessary to the Dirichlet model: Brand penetration (b j ) is the average of purchase probabilities and it is expressed as a percentage for a brand j: Brand penetration: b j = pij n 100 (1) i where p ij is the probability consumer i to buy brand j during a specific time period, and n is the number of responding consumers. Product category penetration (Β) is estimated as 1 - (probability of non purchase of any brand during the specific time period) for each respondent separately; then, the average category penetration value is calculated. Category penetration is thus estimated as follows: Product category penetration: B = 1 ( 1 ) 100 p ij n (2) i j 3

5 Average purchasing frequency for brand j (wj) during the specific time period (w j ) can be estimated from the total brand volume purchased by dividing this with the estimated number of brand buyers: Average purchasing frequency for brand j w = p v nb (3) where v ij is the most probable number of purchases by the respondent i for the brand j that is estimated through the question How many times it is possible that you buy brand <j> in the following time period <w>. Equation (4) explains the category purchasing frequency (W), which is estimated as the sum of unitary brand purchasing volumes by the expected number of category buyers: Category purchasing frequency W = p v nb (4) j i Finally, brand market share (ms j ) can be estimated as follows: Brand market share ms = ( b w BW ) 100 i j j j j (5) Brand Loyalty Measures Certain BPMs can be used as measures to estimate buyers loyalty to the brands of a (sub)category, with the percentage index Share of Category Requirements (SCR) being one of the most important loyalty measures (Stern & Hammond, 2004). SCR indicates how many times a brand will be bought out of 100 category purchases. Other loyalty measures are the percentage of buyers who purchase only one brand ( solely loyal ), as well as the index of Repeated Purchases, that expresses the possibility of a brand to be also bought in the future. An alternative loyalty measure constitutes the polarization index φ, which shows the degree of homogeneity in the way consumers choose the brands of a category. Polarization index φ is related to the S statistic of the Dirichlet model, as follows: 1 φ = (6) 1 + S Both indices (φ and S) capture changes in the homogeneity in consumer choices. But as the S index varies from 0 to infinity, the φ index can vary from 0 to 1 thus being easier to explain. As regards to the φ index, values closer to 0 point out to full homogeneity in consumers choice meaning that there are high levels of brand switching within the category, since all buyers show the same propensity to buy brands of the category. Values closer to 1 indicate the presence of heterogeneity meaning that there are high levels of brand loyalty, since each buyer just buys his or hers favourite brand from the category (Fader & Schmittlein, 1993). Determining loyalty to a certain product category does not indicate whether consumers show loyalty towards certain brands or certain attributes of a product. The φ index, however, allows this investigation. In the present work, the φ index is used as a means to estimate consumer loyalty towards the low fat ( light ) claim on the food label of the products under examination, since attributes such as label information about caloric content (i.e. light or full fat milk), enrichment (i.e. calcium-enriched milk) or even way of processing (i.e. fresh or high pasteurised milk) can have an impact upon consumer loyalty to different brand characteristics. The research For the needs of the present research, a survey has been undertaken with the use of a structured questionnaire in 200 consumers in the area of Athens, Greece, during the months October and November Each respondent completed a questionnaire that included the JPS in relation to the main brands in the two food categories under examination, milk and yoghurt, as explained above, for a 4-week period ( How many times it is possible that you buy brand <...> in the following 4 weeks? ). Twenty brands have been included in the milk category and 16 brands in the yoghurt category. From these brands, 8 have been light in each category respectively. According to the research aim, the brands included in the questionnaire have been selected based on certain additional attributes, such as their enrichment (functional vs. conventional) and for milk only the way of processing (fresh vs. high pasteurised). The sampling method was convenience and data collection took place through personal interviews. After controlling for purchasing or not of milk or yoghurt, the final number of consumers included in the analysis was N milk = 177 persons και Ν yoghurt = 167 persons. ij ij ij ij j 4

6 All equations (1) (5) described above have been estimated through MATLAB. Following, the results have been used for the calculation of Dirichlet model s theoretical estimations through DIRICHLET software package (Kearns, 2001). Analysis and Results Based on the Juster estimates for the milk category (Table 1), the total market share of the 20 brands is 88.8%, total penetration (% of category buyers) is 88.3%, and the average purchasing frequency is 9.8 times during the 4-week period under examination. In other words, about 88% of consumers buy one of the 20 milk brands at least once a month, while a consumer of the sample buys milk at average 10 times a month. Milk brand number 4 (light) is the market (category) leader, with a market share of approximately 18% (over total sales) and average purchasing frequency 6.1 times. Table 1. Milk category market structure, BPMs from Juster estimates, N milk =177 (*) Sub-categories according to... Market Penetration Purchase Brand share Fat content Enrichment Processing (%) frequency (%) Milk 4 Light Conventional Fresh Milk 3 Full Conventional Fresh Milk 1 Full Conventional Fresh Milk 2 Light Conventional Fresh Milk 13 Light Conventional High pasteurised Milk 12 Full Conventional High pasteurised Milk 5 Full Conventional Fresh Milk 11 Full Functional Fresh Milk 16 Full Functional High pasteurised Milk 9 Full Conventional Fresh Milk 18 Light Conventional High pasteurised Milk 6 Light Conventional Fresh Milk 10 Light Conventional Fresh Milk 7 Full Conventional Fresh Milk 17 Full Conventional High pasteurised Milk 8 Light Conventional Fresh Milk 15 Light Conventional High pasteurised Milk 19 Full Functional High pasteurised Milk 14 Full Conventional High pasteurised Milk 20 Full Functional High pasteurised Category Total market share light sub-category 40.5 *Notre: T=4 weeks For the yoghurt category (Table 2), total market share is 76.6%, total penetration is 80.7% and average purchasing frequency 4.5 times during the 4-week period under examination. Yoghurt brand number 1 (light) is the market (category) leader, with market share of approximately 17% over total sales and average purchasing frequency of 2.9 times. In Tables 3 and 4, the Dirichlet model has been applied on the categories under examination to theoretically estimate BPMs. Regarding the category-related loyalty measures, the % SCR for the average milk brand is 18.4, the solely loyal buyers are 4% of the sample, the repeat rate is 63.4%, while the polarization index φ for the overall category is Regarding yoghurt, the relevant BPMs are, for the average brand SCR 5

7 19.7%, exclusive loyal buyers 7%, repeat rate 34.7%, while the polarization index φ for the overall category is Table 2. Yoghurt category market structure, BPMs from Juster estimates, N milk =167 (*) Brand Sub-categories according to... Fat content Enrichment Penetration (%) Purchase frequency Market share (%) Yoghurt 1 Light Conventional Yoghurt 2 Full Conventional Yoghurt 6 Full Conventional Yoghurt 9 Light Conventional Yoghurt 5 Light Conventional Yoghurt 4 Light Conventional Yoghurt 10 Full Conventional Yoghurt 11 Full Conventional Yoghurt 3 Full Conventional Yoghurt 13 Full Conventional Yoghurt 7 Light Conventional Yoghurt 8 Full Conventional Yoghurt 12 Light Conventional Yoghurt 15 Light Functional Yoghurt 14 Full Functional Yoghurt 16 Light Functional Category Total market share light sub-category *Note: T=4 weeks In Figure 1, four groups of the milk category brands are depicted based on the category penetration and the brands purchasing frequency. The first group of brands is comprised of the category leaders (milk brands 1-4), which exhibit high purchasing frequency and penetration. The second group of brands includes the followers, which show average purchasing frequency and penetration. The third group is constituted by one brand that is purchased by few category buyers (low penetration) but very frequently. Finally, the fourth group of brands is comprised of the remaining small milk brands that complete the category. Similar is the picture in the yoghurt category (Figure 2). The first group comprises the category leader (yoghurt brand 1). The second group (yoghurt brands 9 and 11) is purchased by few consumers but very frequently. The third group comprises the followers. Finally, the fourth group is constituted by other small yoghurt brands that complete the category. Table 5 includes the polarization indices φ for the two food categories under examination, as well as for the three sub-categories (light, enriched, fresh). The light milk and the fresh milk sub-categories exhibit higher loyalty levels compared to the enriched milk sub-category. Similarly, within the yoghurt category, the light yoghurt sub-category shows higher loyalty level compared to the enriched yoghurt sub-category. Discussion and Conclusions The above-described results lead to the conclusion that the selection of the milk and yoghurt brands included in the category was accurate in relation to the specific consumer sample, given the high accumulated market share they acquire (more than 75% over the total sales of the two categories). This outcome adds face validity to the outcome of the present research. Regarding the structure of the two food categories under examination, it is worth highlighting the fact that a light brand is leading in both categories, both in terms of penetration and average purchasing frequency, as well as in terms of market share. This phenomenon is not accidental - for instance something that should be attributed to the power of the specific brand as such and not to their light character, given that three out of first 6

8 Table 3. Observed Juster (T) and theoretical Dirichlet (T) estimations of BPMs, milk category, 20 brands, Ν milk =177 (*) % of buyers who Purchase frequency (times buy per buyer) for... Share of Solely loyal Μarket category Penetration share requirements Purchase once five + % brand category (%) frequency O O T T T O T T T T T T Light brands Milk Milk Milk Milk Milk Milk Milk Milk Sub-category light Full-fat brands Milk Milk Milk Milk Milk Milk Milk Milk Milk Milk Milk Milk Sub-category full fat Average brand * Note: Τ=4weeks Repeat purchase 7

9 Table 4. Observed Juster (T) and theoretical Dirichlet (T) estimations of BPMs, milk category, 16 brands, Ν yoghurt =167 (*) % of buyers who Purchase frequency (times buy per buyer) for... Share of Solely loyal category Penetration requirements Συχνότητα once five + % brand category (%) αγοράς O O T T T O T O O T T T Μarket share Light brands Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Sub-category light Full-fat brands Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Yoghurt Sub-category full fat Average brand * Note: Τ=4 weeks Repeat purchase once 8

10 3 rd 1 st 2 nd 4 th Figure 1. Double jeopardy line and grouping of brands in the milk category 2 nd 1 st 3 rd 4 th Figure 2. Double jeopardy line andgrouping of brands in the yoghurt category 9

11 Table 5. Polarization index φ for various milk and yoghurt sub-categories Sub-categories according to Milk Yoghurt Fat content (light) Enrichment (functional) Way of processing (fresh) Total (all brands) five leading brands in each category are light products (see Tables 1 and 2), with a cumulative market share of 32.8% in milk and 32.3% in yoghurt. In other words, more than half the sales of the top-5 brands in each category correspond to light brands. The same conclusion can be derived at the category level, with total market share of the light sub-categories (40.5% in milk and 43.1% in yoghurt) being directly comparable or even higher than that of the full-fat sub-categories (48.3% in milk and 33.5% in yoghurt). The importance of the light subcategory and its comparability to the full-fat sub-category is also supported by the theoretical BPMs of the Dirichlet model (see Tables 3 and 4). Both the penetration of the light sub-category and the percentage of sample members who buy light brands either one or more than five times are absolutely comparable to the relevant BPMs of the full-fat sub-category in both milk and yoghurt. Moreover, the findings that concern loyalty measures in the light sub-category have special value for the present research. Similarly to the BPMs presented above, loyalty measures are directly comparable between the two sub-categories (light and full-fat), both in milk as well as in yoghurt. It is worth stressing that loyalty in light yoghurts appears to be higher than loyalty in full-fat yoghurts (e.g. a light yoghurt brand will be purchased 51.7 times in 100 times that a yoghurt will be purchased, compared to 44.8 times that a full-fat yoghurt will be purchased; furthermore, a light yoghurt brand will be purchased in the future again with a probability of 74.6% compared to the probability of 70.2% for a full-fat brand see Table 4). The grouping of the categories under examination is also useful for gaining valuable marketing insights. For instance, in the milk category the first group of the leading brands is located bellow the double jeopardy line. This means that the leading brands are purchased fewer times than it is ideal for their penetration. This could be due to the intensive competition from the followers (that appear to be better positioned in the category - above the diagonal). Moreover, niche milk brand number 11 is a functional brand that appeals to parents with small children; thus, it is faced with limited competition in this area of the market. This fact can explain the high loyalty of buyers towards this brand (very frequent purchasing by few buyers). Finally, the small brands in the fourth group do not seem to justify their existence, unless they increase their purchasing frequency. Similar results with substantial marketing implications can be drawn for the yoghurt category. It is moreover worth mentioning that loyalty in the milk category is overall higher than loyalty in the yoghurt category, as indicated by the polarization index φ (see Table 5). This fact indicates that sample members have more favourite brands in the milk category than in the yoghurt category. On the contrary, in the latter category all brands have high probability of duplication. Furthermore, of special value are the findings regarding loyalty in the light sub-category in relation to loyalty in other sub-categories (see also Table 5). It thus seems that within the milk category one can discriminate between two important sub-categories (low-fat and fresh), with much higher loyalty exhibited by sample members towards brands in these sub-categories than in the wider category. In other words, consumers loyalty is driven by product characteristics such as light, while even the enriched milk brands exhibit substantial loyalty shares. This finding justifies the need of differentiation within a wider product category (in the present case the milk category) as a marketing strategy. This conclusion is even more apparent in yoghurt, where the light claim as a marketing differentiation strategy creates substantial loyalty, something that cannot be achieved by means of the enrichment-related differentiation strategy. Nevertheless, one should keep in mind that the low-fat differentiation strategy cannot lead to the creation of an autonomous food category, where light versions of various food products could belong. On the contrary, while it can be a viable marketing strategy, as shown above, especially successful in creating loyal buyers, it should be embodied to an integrated marketing effort within the wider category each light product belongs. In other words, a light milk brand is not a member of the low-fat market but of the wider milk market. This conclusion is supported by the fact that the light milk brands in the present survey demonstrated a diversified behaviour compared to the light yoghurt brands. Furthermore, it is also supported by the finding that, in the brand grouping task attempted, not a unified trend by the light brands - either milk or yoghurt - was able to emerge. On the contrary, the light brands were classified in all four brand types identified, exhibiting diverse loyalty behaviour. The present research is not free from limitations. For instance, the lack of strong convergence between observed and theoretical brand performance measures is a typical example. This is possibly due to the rather small sample size that is used in the present study. It is certain that the use of a larger and more representative sample would lead to more valid and easier to generalise results. It is thus obvious that the findings of the 10

12 present - rather exploratory - study hold for the specific sample and the time frame, with no possibility for empirical generalisations. Finally, a substantial weakness of the above-described methodological approach is its inability to explain reasons that drive loyalty on a personality level (i.e. why buyers are loyal), apart from the attribute brand level analysed here. A suggested extension of the specific methodology that could provide explanatory insights for the loyalty behaviour of buyers is the use of the Repeated Binary Logit model (RBL, Rungie & Laurent, 2005). The RBL constitutes an extension of the logistic regression based on the Beta Binomial Distribution, introducing a generalised linear model that can estimate the impact of covariates such as consumers socio-demographic or psychographic characteristics on loyalty measures. It is postulated (Chrysochou et al., 2008) that covariates of such type are able to provide satisfactory explanations regarding buyers loyalty behaviour towards food categories and brands. 11

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