Light vs. Regular brands: Do they follow the same patterns?

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1 University of Aarhus From the SelectedWorks of Polymeros Chrysochou 2012 Light vs. Regular brands: Do they follow the same patterns? Therese Sjostrom, University of South Australia Polymeros Chrysochou Armando Maria Corsi, University of South Australia Carl Driesener, University of South Australia Available at:

2 Light vs. Regular brands: Do they follow the same patterns? Therese Sjostrom*, Ehrenberg-Bass Institute, UniSA, Polymeros Chrysochou, Aarhus University, Armando Maria Corsi, Ehrenberg-Bass Institute, UniSA, Carl Driesener, Ehrenberg-Bass Institute, UniSA, Abstract Little is known about the market performance of brands that carry light claims (i.e. light brands) in comparison to their regular counterparts. In order to fill this gap, we explore whether light brands a) perform similarly to regular brands in terms of brand performance measures, b) achieve similar loyalty levels to regular brands, and c) share the same customers (show similar partitions) as regular brands. The analysis is conducted on four product categories (Colas, Flavoured Carbonated Beverage, Soups and Margarine) using household panel data provided by TNS. The results show that when considering standard brand performance measures (i.e. market share, penetration and purchase frequency) regular brands perform better than light brands. With respect to loyalty, light brands achieve greater levels of loyalty than regular brands. Finally, light brands share their customers more than regular brands do, which suggests the existence of market partitions. Key words: light brands, brand performance, brand loyalty, polarisation index, duplication of purchase law Introduction and Literature Review The prevalence of dietary related diseases increases worldwide (World Health Organisation 2003). In response to this trend, and complying with the public discourse on healthier diets and lifestyles, strategies helping consumers to adopt healthier eating habits, such as the launch of healthier food product lines (i.e. low-calorie, low fat and low sugar), have been introduced. (Chrysochou 2010). Such products are often described in the extant literature as light products (Urala 2005). Light products fall under the classification of a nutrition claim or nutrition content claim, given that it is a statement that claims non-existence or reduction of a nutrient content (Food Standards Australia and New Zealand 2011). Similarly, the EU Regulation 1924/2006 has created specific regulations for these claims (Verhagen et al. 2010). According to the Mintel New Product Launch Database a global expansion of 80,000 low fat products was recorded between 2002 and 2007 (Mintel Global New Products Database 2011). This increase can be explained by the growing consumption of light products as a way to control weight (Miller et al. 1998). Standard dietary advice recommends that the total fat consumption should be reduced to 30%-35% of the total daily energy intake. However, past research shows that there is a big gap between what is being recommended and what is being achieved by consumers (Hill et al. 2002; Miller et al. 1998). Light products allow consumers to reduce the consumption of fat and sugar with consequential benefits in terms of physical wellbeing, health and weight management (Drewnowski et al. 2007; Kähkönen and Tuorila 1999; Monsivias and Drewnowski 2007). 1

3 There are contradicting messages on how well light products have been accepted by consumers. One stream of research postulates that reduced fat products have been very well accepted and that naturally modified products with a high initial acceptance can rely on their acceptance over time (Hill et al. 2002; Stubenitsky et al. 1999). In line with this, Viaene (2007) affirms that products with a positive health message (e.g. light, low fat and cholesterol free ) lead to increasing sales. On the other hand, consumers have been found to be suspicious towards health messages overall and reduced-fat products in particular (Hill et al. 2002). It is vital for marketing managers to increase their understanding of how brands carrying a light claim perform in the market. Evidence from the literature (Hill et al. 2002; Rapp et al. 2009) suggests that light brands are expected to perform equally as regular brands, irrespective of whether their light claim may influence the purchase behaviour of consumers. However, to the best of our knowledge, this belief has never been tested with actual purchase data. In order to fill this gap, this paper seeks to answer the following three research questions: 1. Do light brands perform similarly to regular brands (in terms of brand performance measures)? 2. Do light brands achieve similar loyalty levels as their regular counterparts? 3. Are there similar partitions (i.e. levels of sharing) between regular and light brands? Data and Methods This research is based on secondary panel data collected by Taylor Nelson Sofres (TNS) Super Panel data in the UK. Four product categories Colas, Flavoured Carbonated Beverages (FCB), Soups and Margarine have been selected according to whether a category included a sufficient number of brands that carried a light claim (i.e. low-fat, low-sugar and low-calories) (see Table 1). FCB consists of all carbonated soft drinks excluding Colas. Each category was divided into two sub-categories light (i.e. diet, low-calories or low-fat) and regular (i.e. the standard version of the category). A fifth aggregated category named Aggregated Soft Drinks has been created consisting of Cola and FCB, to establish differences in the overall soft drink category. Performance metrics over a 12-month period have been extracted, although the actual years of analysis differ between categories (see Table 1). The results for the Soup and Margarine category are not disclosed in the table due to space limitations. Table 1 Categories, year and sub-categories used in this study Cola 1998 & 2007 FCB 1998 & 2007 Soup 1998 Margarine 2008 Diet Diet Low calories Low fat Regular Regular Regular Regular The Dirichlet model is used as a benchmark tool in order to investigate the market performance of each category (Goodhardt et al. 1984). This model is one of the most widely used for studying brand performances, as it provides useful benchmarks and offers a natural baseline for behavioural brand loyalty (Ehrenberg et al. 2004). Penetration and purchase frequency, both at the category and brand level, are necessary to fit the model. These metrics are obtained from the TNS panel. Next, the Dirichlet model is fitted using the software developed by Zane Kearns (Kearns 2002). The mean absolute deviation (MAD) and the mean absolute percentage error (MAPE) are used as measures of goodness of fit to the Dirichlet model (Scriven and Bound 2004; Wright et al. 2002). According to Scriven and Bound (2004) MAD values for penetration should 2

4 be over 0.9 and for purchase frequency should be at least 0.6. MAPE values greater than 10% indicate a poor fit, whereas a score of less or around 10% is considered a good fit (Driesener and Wright 1999). Loyalty is often measured by standard brand performance measures, such as repeat-purchase, share of category requirements and 100% loyals. Such measures have been criticised for their lack of theoretical foundation and being a function of market share (Bloemer and Odekerken- Schroder 2003; Ehrenberg et al. 2004). An alternative loyalty measure utilised in this paper is the polarization index (φ) or (phi). Several authors suggest that φ is a reliable measure of consumer behavioural loyalty (Corsi et al. 2011; Jarvis et al. 2007). The polarisation index varies from zero to one, with higher values for φ indicating higher loyalty (Rungie and Laurent 2004). The φ index is calculated from the S parameter value obtained from the Dirichlet model using the following equation φ = 1/(1-S) (Rungie and Laurent 2004). The best way to measure competition intensity between two brands is by observing to what degree these two brands share their customers. When there is low sharing between groups of brands that indicates the existence of partitions (Scriven and Danenberg 2010). In our study, two partitions were tested on whether they exist: light vs. regular brands. The analysis was done using the Duplication of Purchase Automation Software (DOPAS) (Banelis et al. 2011). The software allows for the estimation of the Partition Sharing Index (PSI). The PSI measures the degree at which brands (or partitions) share the same customers (Banelis et al. 2011). PSI value of 1.0 indicates normal sharing, whereas a value of 1.2 suggests that two brands or partitions share 20% above of what is expected in the market. Results Do light brands perform similarly to regular brands? The analysis of market share, penetration and purchase frequency show that both light and regular brands follow the DJ law. Brands in the light sub-category tend to have lower relative market share values than those in the regular sub-category. For example, in the Cola category (1998) the light sub-category market leader Diet Coke (39.9%) has a lower market share than the market leader in regular sub-category Coca Cola (55.1%). Likewise, the market leader in the regular Soups sub-category Heinz has a market share of 28.6%, while the leader in the light Soups sub-category Weight-Watchers has a market share of 3.7%. The purchase frequencies follow the same trends seen for market shares. For example, in the light Cola subcategory the leading brand Diet Coke has a purchase frequency of 9.2, while the leader in the regular sub-category Coca Cola has a purchase frequency of The penetration for the sub-categories also follows the same patterns. In the light Soups sub-category the market leader Weight-Watchers has a penetration of 8.8% while the market leader in the regular Soups subcategory Heinz has a penetration of 51.7%. Most sub-categories show a poor fit to the Dirichlet model in terms of penetration and purchase frequency (see Table 2). For example, the penetration for the light Margarine sub-category shows a MAPE of 40% while the regular Margarine sub-category showed a lower MAPE value 20%. The situation for the aggregated Soft Drinks in 1998 is reversed. The light sub-category showed a MAPE of 20% and the regular sub-category showed a MAPE of 30%. Similar results were observed in terms of purchase frequency, for the light Margarine (MAPE = 30%) and the regular Margarine (MAPE=20%) and for the aggregated Soft Drinks sub-categories the light sub- 3

5 category (MAPE=60%) and the regular sub-category (MAPE=30%). Two exceptions to the bad fit of the Dirichlet model are relative to the regular FCB (MAPE=10% in 2007) and light Soup (MAPE=10% in 1998) in terms of penetration. The only sub-category that has shown a good fit in terms of both purchase frequency (MAPE=0%) and penetration (MAPE=10%) is that of light Soups. In addition, when looking at MAPE values for Cola, FCB and the aggregated Soft Drink category, we can see an improvement, but the model fit is still poor. For example, purchase frequency MAPE for both the light and regular sub-categories were identical in 1998 (60%), and they improved to 30% and 20% respectively in This was also visible in the FCB category, where the purchase frequency MAPEs for the light sub-category declined from 40% to 20%, while purchase frequency MAPEs for regular brands went from 80% to 20%. Due to space limitations, it is not possible to report all the Dirichlet figures produced in the analysis. The table below (Table 2) summarises the values described above. Table 2: Example of MAD and MAPE Dirichlet results relative to the Soft Drinks category Sub-Category Market Share (%) Penetration (%) Brand Purchase Frequency O T Deviation O T Deviation O T Deviation Light&'COLA'1998 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Regular&'COLA'1998 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Light&'Carbonated'Flavours'1998 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Regular&'Carbonated'Flavours'1998 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Light&'Aggregated'Soft'Drink'1998 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Regular&'Aggregated'Soft'Drink'1998 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Light&'COLA'2007 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Regular&'COLA'2007 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Light&'Carbonated'Flavours'2007 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Regular&'Carbonated'Flavours'2007 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Light&'Aggregated'Soft'Drink'2007 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Regular&'Aggregated'Soft'Drink'2007 Average Mean2absolute2deviation2(MAD) Mean'Absolute'Percentage'Error'(MAPE) Do light brands achieve similar loyalty levels to regular brands? The polarisation values for all sub-categories are displayed in Table 3. Regular sub-categories attract more loyalty than light sub-categories when considering the φ values. In the case of Soups 4

6 and Cola the φ values were lower for the light sub-category, though not by much. Differences in loyalty were much bigger in the Margarine category, where light brands had a φ value of 0.60 and regular sub-category had a φ of Loyalty values for Cola, FCB and the aggregated Soft Drink category also show that loyalty declined over time. From 1998 to 2007, loyalty for light FCB decreased from 0.40 to Likewise, loyalty to regular FCB decreased from 0.32 to Table 3: Polarisation values for each sub-category across the three-year period Cola FCB Aggregated Soft Drink Light Regular Light Regular Light Regular Are there similar partitions between regular and light brands? Table 4 presents the PSI values obtained from the DOPAS software. The results indicate higher sharing than expected between light brands. For example, in the light Cola (1998) sub-category a sharing of PSI 1.9 that is 90% higher than expected based on market share. In 2007 the light Cola sub-category had a slightly lower sharing PSI 1.6 (60%) more than expected. The light FCB subcategory had a PSI of 1.4 that is 40% higher sharing than expected based on market share. Light FCB also had a lower sharing in 2007 with a PSI of 1.2 (20%). When all the soft drinks are aggregated together their sharing levels tend to be lower. Light Aggregated Soft Drink in 1998 accumulate a PSI of 1.4 (40%), while in 2007 they show a PSI of 1.2 that is 20% higher than expected sharing, based on market share. In all the categories displayed in the table the light subcategories have higher sharing values than the regular counterparts. Importantly, the interpartition between the two sub-categories (light and regular) indicates normal sharing (that is a PSI of approximately 1.0) for Soups, Margarine, FCB and the aggregated Soft Drink brands. This means that buyers of light brands buy regular brands as much as expected (based on MS) and vice versa. The Cola category has higher than expected (40%) sharing between light and regular brands. These findings also indicate that the customer base for both regular and light brands is essentially the same. It consists of one segment that tends to buy both light and regular brands, with a small tendency towards regular brands, as these are bigger brands. Our findings show that there is not a large segment of people that only buy light brands and exclude regular brands. Table 4 Partition sharing index (PSI) values for each sub-category across the 3-year period Cola FCB Aggregated Light Reg. IP Light Reg. IP Light Reg. IP *IP = Inter-partition PSI Discussion and Conclusion Prior research suggests that consumers show greater preference toward light products (Roininen and Tuorila 1999). In the case that these considerations were true, we would have expected to find differences in the buying behaviour patterns for regular and light brands. This does not appear to be the case when we look at purchasing data. Apart from a difference in size, both BPMs and the Dirichlet model have established that the light brands do perform in the same manner as the regular brands. Finally, the Duplication of Purchase law has provided insight in the way brands compete, suggesting that light and regular brands largely share the same customers. The Polarisation index at the sub-category level has indicated different values for light and 5

7 regular sub-categories, where the light sub-categories have a slightly higher loyalty. The possible reason behind this result is that, light brands have a functional difference and therefore somewhat higher loyalty than expected. This result has already been found by Scriven (2010) for diet soft drinks. Furthermore, the Polarisation index has shown that loyalty tends to decrease over time, a result in line with previous research (Wright and Riebe 2010). The expectation that light brands do not share customers with regular brands, was not verified. This research found that while light brands share customers more than expected, those customers also buy regular brands as much as expected; hence there is not a distinct group of customers that only buy light brands. Future research is recommended to verify if light brands should be classified as functionally different. The motivation to purchase could also be included in future research to take into consideration consumers that are loyal to these products due to health related problems (e.g. purchasing low fat due to a diabetes disease). Finally, the analysis will be expanded to other product categories to verify whether the results obtained in this study could be empirically generalised. References Banelis, M., Kennedy, R., Driesener, C., Tanusondjaja, A. and Lion, S Title., Ehrenberg-Bass Institue. Bloemer, J. M. M. and Odekerken-Schroder, G Antecedents and consequences of affective commitment. Australasian Marketing Journal 11, no. 3: Chrysochou Food health branding: The role of marketing mix elements and public discourse in conveying a healthy brand image. Journal of Marketing Communications 16, no. 1: Corsi, A., Rungie, C. and Casini, L Is the polarization index a valid measure of loyalty for evaluating changes over time? Journal of Product and Brand Management 20, no. 2: Drewnowski, A., Monsivias, P., Maillot, M. and Darmon, N Low-energy-denisty diets are associated with higher diet quality and higher diet costs in french adults. Journal of the American Dietetic Association June: Driesener, C. and Wright, M 'Extra time' extending juster estimates to longer time periods. In Australian & New Zealand Marketing Academy Sydney: School of Marketing, University of New South Wales. Ehrenberg, A. S. C., Uncles, M. D. and Goodhardt, G. G Understanding brand performance measures: Using dirichlet benchmarks. Journal of Business Research 57, no. 12: Food Standards Australia and New Zealand Nutrition and health claims: Food Standards Australia & New Zealand. Goodhardt, G. J., Ehrenberg, A. S. C. and Chatfield, C The dirichlet: A comprehensive model of buying behaviour. Journal of the Royal Statistical Society 147, no. 5: Gray, J., Armstrong, G. and Farley, H Opportunities and constraints in the functional food market. Nutrition & Food Science 33, no. 5: Hill, D., Knox, B., Hamilton, J., Parr, H. and Stringer, M Reduced-fat foods: The shopper's viewpoint. International Journal of Consumer Studies 26, no. 1: Jarvis, W., Rungie, C. and Lockshin, L The polarisation method for merging data and analysing loyalty to product attributes, price and brands in revealed preference. International Journal of Market Research 49, no. 4: Data Integration Special Issue: Kähkönen, P. and Tuorila, H Consumer responses to reduced and regular fat content in different products: Effects of gender, involvement and health concern. Food Quality and Preference 10: Kearns, Z Dirichlet.Xls. Palmerston North: Massey University. 6

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