Understanding Consumers'

Transcription

1 Submission for the Award of PhD of Business and Management (Research) Understanding Consumers' Repertoire Sizes Melissa Banelis BASc (Hons) (Industrial and Applied Mathematics) UGIIVERSITY OF SOUTH AUSTRALIA LIBRARY Supervisors: Dr Cam Rungie and Dr Erica Riebe Ehrenberg-Bass Institute University of South Australia August, 2008

2 Acknowledgements I would like to firstly thank my supervisors, Cam Rungie and Erica Riebe. Thanks Cam for the 'coffee chats' about all things nerdy. Thanks Erica for being my marketing guru and reader of everything. Both of you have not only supported and assisted my PhD journey but you have also made it a pleasant trip. I have been lucky enough to have supervisors I can learn from as well as laugh with. Thanks to MarketingScan for some of the data used in the analysis in this thesis. It is much appreciated. Thank you Carl Driesner for the use of your 'Thesis template' and other various help along the way. Thanks Kerry Mundt for our great coffees and lunches discussing our progress and our children (usually not in that order). To my friends who always encouraged me to keep at it, no matter what obstacles came up along the way. To my ever-supportive family. Thank you for all the hours of baby-sitting and words of support and encouragement when I needed them the most. To my wonderful husband, John. Thank you for always being my biggest fan and patiently listening to all my 'thesis talk'. You always believed in me and made me feel like [could accomplish anything I set my mind to. And last but not least, my sons Billy and Michael (and baby number 3 still in the oven). Thank you for making me laugh and see the lighter side of life in stressful times. You are, and will always be, my biggest achievement and my greatest inspiration.

3 Declaration I declare that: this thesis presents work carried out by myself and does not incorporate without acknowledgement any material previously submitted for a degree or diploma in any university; to the best of my knowledge it does not contain any materials previously published or written by another person except where due reference is made in the text; and all substantive contributions by others to the work presented, including jointly authored publications, is clearly acknowledged. Melissa Banelis

4 Abstract The aim of this thesis is to develop a greater understanding of consumers' brand repertoires. This research is part of the brand choice literature, which involves the analysis of all parts of the brand choice process. While there is clearly a need for research on the size of consumers' repertoires, little research has been conducted on this topic to date. This thesis provides much needed descriptive knowledge in relation to repertoire size, as well as providing information about the potential influence ofa selection of consumer characteristics on this measure. Repertoire size is defined as the number of brands a consumer purchases over a specified period of time. It is not only seen as a measure of loyalty (the smaller the repertoire, the higher the loyalty), but also a measure of competitiveness in a market (the bigger the repertoire, the greater the competition). Although these areas are of considerable interest to marketing managers and researchers alike, this measure has rarely been emphasised in previous research (Colombo and Jiang 2002). Colombo and Jiang (2002) provide the most significant paper in the research of repertoire size to date. They found that repertoire size depends on the number of brands in a category, the market shares of the brands in the category, the degree of heterogeneity in consumers' choice vectors, and the number ofcategory purchases that are made by the buyers of the category. Knowing this information provides some context for managers aiming to determine whether their marketing efforts have increased the average repertoire size of shoppers. It provides some basis for a benchmark expected repertoire size, against which managers can compare the buyers of their own brands. While Colombo and Jiang (2002) were able to provide some insight in relation to repertoire size, there is still more to be learnt about the measure. What is a typical repertoire size? How does this vary across categories? Can the measure be accurately described mathematically and which modelling methods produce the most accurate descriptions of the empirical data? This thesis not only answers these initial questions, but also greatly extends previous literature by developing and implementing a new Repertoire Regression model to analyse the potential impact of consumer attributes on the size of a repertoire. This model has not been previously published in the marketing literature and certainly has not previously been used

5 for investigating the potential factors that may influence the size of a buyer's repertoire. Developing and using this model in such a way is therefore just one of the major contributions that this thesis makes to the brand choice literature. The methodology employed to calculate typical repertoire sizes involved calculating the repertoire for each buyer and then taking the average across all buyers of the category. Colombo and Jiang's (2002) model for generating an estimated repertoire size across a market was instead calculated using a formula. This validates the use of this formula for subsequent analyses of repertoire size conducted in this thesis and in any future research. The second stage of the thesis then involved the use of the Repertoire Regression model. The development of the model involved the introduction of covariates (consumer attributes) to the theoretical repertoire size calculation through the use of an exponential link function in the Dirichlet multinomial distribution. The first key finding from this thesis is that repertoire size varies across categories and increases with longer observation periods. The observed repertoire size across the 48 fast moving consumer goods categories considered varied between 1.1 and 4.6. A time period analysis (for 1 month, 3 months, 6 months, 1 year and 4 years) of the observed results showed that the repertoire size increased steadily as a longer period of time was used to generate repertoire size estimates. While the measure of repertoire size becomes larger over longer periods of observation, it was also found that the size of a buyer's repertoire can mostly be determined from just a few observed purchases from the category. For many buyers in most categories considered in this thesis, the size of the repertoire was established in the first few months of observation. This has implications for managers in terms of the amount of data that is needed to accurately record the average repertoire size for a category. It shows that data collected over shorter periods is sufficient to record such information. Another key finding was that the yearly average repertoire size across all categories is two, and that this did not vary substantially from one category to the next. This result implies that consumers are in fact quite loyal to the brands they buy. Prior to conducting this research, it may have been expected that buyers would make purchases from quite an array of brands over a year, particularly for many FMCG categories where many category

6 purchases are made over such a period. This has implications for managers as it demonstrates the expected difficulty in getting a brand into a consumer's repertoire. Across all categories and covariates where Repertoire Regression was performed, there was one main finding. The influence of all covariates (or consumer attributes) on repertoire size is through the intermediary of the category purchase rate. This thesis has not only reported valuable initial information on repertoires that has not been seen in previous research and assessed Colombo and Jiang's (2002) repertoire size model but has also developed a new model for the analysis of repertoire size. This thesis has made a major contribution to repertoire size research.

7 Refereed Publications from this Thesis Banelis, M., C. Rungie and R. Colombo (2003). Empirical verification oftwo expected portfolio size formulae. ANZMAC, Adelaide. Banelis, M., C. Rungie, E. Riebe and L. Meyer-Waarden (2005). "Do higher spending households buy a greater variety of brands?: An application of repertoire regression." Marketing Bulletin iv -

8 Table of Contents ABSTRACT REFEREED PUBLICATIONS FROM THIS THESIS LIST OF TABLES LIST OF FIGURES LIST OF EQUATIONS CHAPTER 1 INTRODUCTION 1. I CHAPTER OVERVIEW 1.2 RESEARCH TOPIC 1.3 IMPORTANCE OF THIS THESIS 1.4 RESEARCH OBJECTIVES 1.5 METHODOLOGY OVERVIEW 1.6 SUMMARY OF KEY FINDINGS 1.7 STRUCTURE OF THESIS 1.8 CHAPTER SUMMARY IV VII VIII IX CHAPTER 2 REPERTOIRE SIZE AND CONSIDERATION SETS CHAPTER OVERVIEW 2.2 REPERTOIRE SIZE 2.3 CONSIDERATION SETS Definition of Consideration Sets The Formation of Consideration Sets Effects on Consideration Sets Models of Consideration Set Formation COMPARISON OF REPERTOIRE SIZE AND CONSIDERATION SETS 2.5 CHAPTER SUMMARY CHAPTER 3 THE DIRICH LET MODEL CHAPTER OVERVIEW THE DIRICHLET MODEL Use of the Dirichlet Model Definitions 3.3 REPERTOIRE SIZE FORMULAE 3.4 CHAPTER SUMMARY CHAPTER 4 REPERTOIRE REGRESSION CHAPTER OVERVIEW THE DIRICHLET MULTINOMIAL DISTRIBUTION INTRODUCING COVARIATES TO THE DIRICHLET MULTINOMIAL DISTRIBUTION MAXIMUM LIKELIHOOD ESTIMATION INTRODUCING COVARIATES TO REPERTOIRE SIZE CHAPTER SUMMARY 48 CHAPTER 5 METHODOLOGY AND DATA CHAPTER OVERVIEW RESEARCH OBJECTIVES Repertoire size across categories Empirical Verification of the Repertoire Size Formula Effects of Covariates 51 on Repertoire Size Likelihood Ratio Test THE DATA Adlab Data MarketingScan Data CHAPTER SUMMARY CHAPTER 6 DESCRIPTIVE RESULTS v -

9 6.1 CHAPTER OVERVIEW 6.2 RESULTS 6.3 KEY FINDINGS 6.4 CHAPTER SUMMARY CHAPTER 7 EMPIRICAL VALIDATION RESULTS 7.1 CHAPTER OVERVIEW 7.2 RESULTS 7.3 KEY FINDINGS 7.4 CHAPTER SUMMARY CHAPTER 8 REPERTOIRE REGRESSION RESULTS 8.1 CHAPTER OVERVIEW 8.2 RESULTS Household Spend Results Household Loyalty Results Household Size Results Household Visits Results 8.3 GOODNESS OF FIT STATISTIC 8.4 KEY FINDINGS 8.5 CHAPTER SUMMARY CHAPTER 9 IMPLICATIONS, LIMITATIONS AND FUTURE RESEARCH CHAPTER OVERVIEW 9.2 IMPLICATIONS FOR MARKETING MANAGERS 9.3 LIMITATIONS AND FUTURE RESEARCH 9.4 CHAPTER SUMMARY REFERENCES APPENDIX A: DMD REGRESSION IN EXCEL 111 APPENDIX B: MATLAB PROGRAMS 123 B.1 LOGARITHM OF COVARIATES 123 B.2 CALCULATE OBSERVED AVERAGE REPERTOIRE SIZE 124 B.3 EMPIRICAL VERIFICATION 126 B.3.1 Main Program 126 B.3.2 Empirical Verification program 127 B.4 REPERTOIRE REGRESSION 129 B.4.1 DMD Regression Program 129 B.4.2 Calculate and Display Results 131 APPENDIX C: DATA REQUIREMENTS 135 C.1 REDUCTION IN NUMBER OF BRANDS 135 C.2 LOGARITHM OF COVARIATES 139 APPENDIX D: DMD REGRESSION ANALYSIS RESULTS D.1 CHOCOLATE BAR ANALYSIS RESULTS D.2 SHAMPOO ANALYSIS RESULTS D.3 DETERGENTS ANALYSIS RESULTS D.4 STORE CHOICE ANALYSIS RESULTS vi -

10 List of Tables Table 1: Table from Hauser and Wernerfelt (1990, p394) of Mean Consideration Set sizes 19 Table 2: Adlab data information (*These categories have data for less than 4 yrs) 57 Table 3: MarketingScan data information 58 Table 4: Observed Repertoire Size across time for Adlab data 63 Table 5: Observed Average Repertoire Size across Adlab data 64 Table 6: Average Repertoire Size for each category purchase rate for the deodorants category 68 Table 7: Weighted Average Repertoire Size across Adlab product categories Table 8: Values of comparison 69 across consideration sets and repertoire size 74 Table 9: Mean Absolute Deviations for Observed and Theoretical Average Repertoire Size 99 Table 10: Brand reduction results for the Chocolate Bar data 137 Table 11: Shampoo data set results for reduction in number of brands 138 Table 12: Detergents results for reduction in number of brands 139 Table 13: Logarithm of covariates results for Chocolate Bar category 141 Table 14: Logarithm of covariates results for Shampoo data 142 Table 15: Logarithm of covariates results for Detergents data 143 Table 16: Logarithm of covariates results for Store Choice data 144 Table 17: Parameter values for the Chocolate Bar category 146 Table 18: Covariate analysis results for Chocolate Bar category 147 Table 19: Parameter values for the Shampoo category 148 Table 20: Covariate analysis results for the Shampoo category 148 Table 21: Parameter values for the Detergents category 149 Table 22: Covariate analysis results for the Detergents category 150 Table 23: Parameter values for the Store Choice category 150 Table 24: Covariate analysis results for the Store Choice category 151

11 List of Figures Figure 1: Number of buyers for each category purchase rate in Deodorants 66 Figure 2: Number of buyers for each Observed Repertoire Size in Deodorants 67 Figure 3: Observed and Theoretical Average Repertoire size results from data 79 Figure 4: Repertoire Size vs Household Spend in Chocolate Bars 86 Figure 5: Repertoire Size vs Household Spend in Shampoos Figure 6: Repertoire Size 86 vs Household Spend in Detergents 87 Figure 7: Repertoire Size vs Household Spend in Store Choice 87 Figure 8: Repertoire Size vs Household Loyalty in Chocolate Bars 89 Figure 9: Repertoire Size vs Household Loyalty in Shampoos 90 Figure 10: Repertoire Size vs Household Loyalty in Detergents 90 Figure 11: Repertoire Size vs Household Loyalty in Store Choice 91 Figure 12: Repertoire Size vs Household Size in Chocolate Bars 93 Figure 13: Repertoire Size vs Household Size in Shampoos 93 Figure 14: Repertoire Size vs Household Size in Detergents Figure 15: Repertoire Size 94 vs Household Size in Store Choice 94 Figure 16: Repertoire Size vs Household Visits in Chocolate Bars Figure 17: Repertoire Size 96 vs Household Visits in Store Choice 96 Figure 18: Repertoire Size vs Household Visits in Detergents 97 Figure 19: Repertoire Size vs Household Visits in Store Choice 97 Figure 20: Pasting data in Excel 113 Figure 21: Calculating category purchase rate 113 Figure 22: Setting up parameter values 114 Figure 23: Pasting covariates data 114 Figure 24: Calculating brand alphas 115 Figure 25: Calculating S values 115 Figure 26: Part of DMD calculation 116 Figure 27: DMD calculation Figure 28: Log-likelihood 116 calculation 117 Figure 29: Selecting Solver from the Tools menu 117 Figure 30: Selecting Add-Ins from the Tools menu 118 Figure 31: Solver dialog box 119 Figure 32: Setting up the Solver parameters Figure 33: Solver results Figure 34: Final results - Part 1 of spreadsheet Figure 35: Final results - Part 2 of spreadsheet

12 List of Equations Equation 1 Probability density function for the Negative Binomial Distribution 33 Equation 2 Probability density function for the Dirichlet Multinomial Distribution 34 Equation 3 Probability density function for the Dirichlet model 34 Equation 4 Average repertoire size formula sum of penetrations 38 Equation 5 Average repertoire size proof result 39 Equation 6 Penetration formula 39 Equation 7 Theoretical average repertoire size formula 40 Equation 8 Final average repertoire size formula Equation 9 S value formula Equation 10 Probability density function for the Dirichlet Multinomial distribution 43 Equation 11 Exponential link function for brand alpha calculation 45 Equation 12 Joint probability formula 46 Equation 13 Log-likelihood formula 46 Equation 14 Average Repertoire size formula 47 Equation 15 Average Repertoire size formula with exponential link function 47 Equation 16 Average Repertoire size formula 52 Equation 17 Likelihood Ratio Test statistic 54 Equation 18 Probability density function for the Dirichlet Multinomial distribution 111 Equation 19 Expanded example Dirichlet Multinomial distribution 111 Equation 20 Logarithm of Dirichlet Multinomial distribution 111 Equation 21 Logarithmic function rule for multiplication 112 Equation 22 Logarithmic function rule for division 112 Equation 23 DMD after logarithmic rules used 112 Equation 24 DMD broken down into manageable parts 112 Equation 25 Formula for part one of DMD 112 Equation 26 Formula for part two of DMD 112

13 Chapter 1 Introduction 1.1 Chapter Overview This thesis examines repertoire size, a loyalty measure that is scarcely researched in the marketing literature (Colombo and Jiang 2002). Repertoire size is examined in detail including descriptive information on average values of the measure across a number of product categories and investigation of the impact of consumer attributes on repertoire size. This chapter, which provides an overview of the contents of this thesis, begins by introducing brand choice research, and more specifically, the literature on repertoire size itself. An outline of the research problem is then provided highlighting the gap in the literature that this thesis seeks to address. The contribution of this thesis to brand choice research is then discussed. The specific research objectives of this thesis and an overview of the methodology employed to address each objective are then outlined and explained. A summary of the key findings and a brief summary of the chapters of this thesis conclude this chapter.

14 1.2 Research Topic There are recognised empirical generalisations that have evolved from the study of repeat purchase. An empirical generalisation is a pattern that is observed across a broad range of conditions (Bass and Wind 1995). The generalisations that have been observed in the analysis of repeat purchase data include (1) double jeopardy a smaller brand has fewer customers who also buy less often (Martin 1973; Ehrenberg, Goodhardt and Barwise 1990; Ehrenberg and Goodhardt 2002) and (2) duplication when a brand shares customers with other brands it will do so in proportion to the market shares of the other brands (Ehrenberg 1988; Sharp and Wright 1999). These generalisations are of considerable importance to brand management because they have such important implications for managers (Ehrenberg, Uncles and Goodhardt 2004). It is for this reason that researchers have sought to model these patterns. They have done so using discrete multi-variate density functions (Goodhardt, Ehrenberg and Chatfield 1984; Ehrenberg 1988). The joint density function for repeat purchases of the brands in a product category is known as the Dirichlet model. It is specified by combining the Negative Binomial Distribution and the Dirichlet Multinomial Distribution. The Dirichlet Model allows the use of the generalisations to estimate characteristics of brands and product categories. This allows marketing managers to judge the performance of their brand as observed and theoretical levels for a range of brand performance. One such brand performance measure is repertoire size. So far, the academic research work on repeat purchase and the Dirichlet model has not emphasised repertoire size as an important measure of buyer behaviour even though it is an area of interest in marketing and brand management. Repertoire size may be considered a measure of brand loyalty. An individual consumer who buys from a large range of brands in a category (i.e. has a large repertoire) must be less behaviourally loyal to the brands he/she does include in his/her repertoire. Brand loyalty is an important marketing concept. There are two main reasons for interest in brand loyalty. Firstly, behaviourally it translates into repeat purchase, therefore knowing about loyalty levels allows for calculations of revenue streams. Secondly, attitudinally, measures of loyalty such as the brands in a consideration set, are also thought important as it is assumed that if marketers can influence attitudes, that they might be able to influence -2-

15 buying. In any case, both types of measure tend to move together with brand size and given that most academics advocate measures of loyalty that combine both behavioural and attitudinal components, I have considered both forms of measures in this thesis. Research in this area is extensive with a number of techniques being used to measure loyalty. Some of the main measurements of loyalty are share of category requirements (SCR) and average purchase frequency. Repertoire size is a different loyalty measure because it is calculated across the category. It is not brand specific like the other measures, and thus it can be used to understand consumer loyalty to the category. Category measures allow researchers to compare loyalty across a variety of categories, which may bring with it additional knowledge about how that market works in comparison to other industries. Clearly, this kind of information is relevant to any brand in the category. Repertoire size is also a scarcely researched area in terms of loyalty and repeat purchase and may reveal some new characteristics of markets that brands managers can use to better market their brands. Research into repertoire size can be described, at best, as limited. Not only are there few studies conducted on the topic, but also those that do exist do not even provide basic descriptive information, such as describing or reporting on the average repertoire sizes across different product categories. This means that researchers in this area are unable to determine whether their findings are similar to other categories. Estimating what a typical repertoire size would be in any particular market is straightforward, however there are no benchmarks for comparison. This thesis aims to firstly provide some of this information by reporting the average repertoire sizes of a number of product categories. Furthermore, this thesis aims to empirically verify the theoretical formula for average repertoire size and to develop and implement a technique for analysing repertoire size with the inclusion of covariates. This new technique is called "Repertoire Regression". This thesis outlines the development of this new technique for measuring patterns in repertoire size across categories. Furthermore, data has been obtained to apply this technique. Repertoire Regression is developed as a special case of the Generalized Dirichlet model that extends the Dirichlet model to calculate repertoire size and include covariates. Once this model has been developed and defined, it can be used to see how repertoire size changes as a function of covariates such as size of household, income, etc. -3-

16 y2l Importance of this Thesis While repertoire size is not a new measure, it has been the topic of very few studies in marketing literature or in any related discipline. Colombo and Jiang (2002) highlight the lack of previous research using this measure. One of the key features in the use of brand performance measures, including repertoire size, is in benchmarlcing. For repertoire size, using the measure as a benchmark involves comparing average repertoire size from a data set against other reported repertoire sizes. The first way in which this thesis makes a contribution to the literature, is that it provides basic descriptive information about the repertoire size measure. This has not been reported in the literature before now. This thesis fills this void by producing average repertoire size results across 48 fast moving consumer goods (FMCG) categories. Secondly, a formula for the calculation of theoretical repertoire size has been derived by Colombo and Jiang (2002). Before this formula can be used in further research of repertoire size, it must be empirically verified. That is it is necessary to check whether the formula provides an accurate description of the observed size of the repertoire from which people purchase. This involves demonstrating the strength of the relationship between the theoretical average repertoire sizes found using the formula and the observed values calculated directly from the data. This thesis will provide the empirical verification of the repertoire size formula as a precursor to the use of this formula in later sections of the thesis. And finally, this thesis makes a contribution to the literature by examining the impact of covariates on repertoire size. This thesis develops a new technique for the analysis of the impact of covariates on repertoire size. This technique is suitable for the analysis of consumer attributes and their relationship with the repertoire size measure. This thesis implements this technique, and by doing so makes a substantial contribution to the literature. While DMD Regression has been tested mathematically and used for different purposes (i.e. Guimaraes and Lindrooth 2005), it has not previously been developed further for use in examining the impact of covariates on the size of the repertoires from which buyers purchase. This will provide a rich and detailed analysis of variables which -4-

17 impact upon the size of repertoires, which has not previously been seen in repertoire literature. 1.4 Research Objectives There are three research objectives that this thesis addresses. The following is a definition of each objective and a brief description. Research Objective 1: size varies across product categories. To empirically demonstrate the extent to which repertoire In addition to providing descriptive information for each product category, this thesis also seeks to investigate variation in the repertoire size measure over time. Average repertoire size is calculated across 48 FMCG categories and 5 time frames (1 month, 3 months, 6 months, 1 year and 4 years). Research Objective 2: prescribed by Colombo and Jiang (2002). To empirically validate the average repertoire size formula Theoretical (based on Colombo and Jiang's formula) and observed (calculated from the observed data) repertoire sizes are calculated for four product categories. Research Objective 3: To determine whether a range of consumer characteristics have an impact on the size of the repertoire and, if so, to determine the level of that impact across categories. Four consumer attributes (covariates) are analysed across four different product categories. The impact of these covariates on repertoire size is examined. 1.5 Methodology Overview Separate analysis methodologies were employed to address each research objective, depending upon what was considered most appropriate for each objective. A common feature of all approaches taken was the use of Matlab software for undertaking analysis. In each case, a Matlab program (or in some cases several programs) was written to carry out the data analysis and output the required result. -5-

18 The method for the first objective involved the observed calculation of average repertoire size for each data set and time period. Firstly, each data set was split up into the relevant time periods 1 month, 3 months, 6 months, 1 year and 4 years. Next, for each time period, the observed repertoire size for each buyer was calculated by summing the number of brands purchased. The average of these observed repertoire sizes was then calculated by summing all the repertoire sizes and dividing this total by the total number of buyers. The result is the average repertoire size for the data set over the particular time period. For the second objective, the first step was to calculate the observed average repertoire size for four product categories over a one year time period. The method of this calculation is the same as discussed for the first objective. Next, the theoretical average repertoire size is calculated for the four data sets using Colombo and Jiang's formula. These observed and theoretical values are then graphed to assess the ability of the formula to describe the actual repertoire size. The analysis for the third objective involved the development and implementation of the Repertoire Regression technique. Colombo and Jiang's (2002) formula calculates average repertoire size using estimates of the brand alphas, which are found by fitting the Dirichlet multinomial distribution (DMD) to the data. The development of the Repertoire Regression technique involved the introduction of covariates into the theoretical repertoire size calculation. This was carried out through the introduction of the parameter values and covariates to the brand alpha calculation. To implement this technique for a data set the first step is to initialise the parameter values to some arbitrary number. The model is then fitted to the data using the maximum likelihood estimation technique. The results are estimates of the parameter values. These parameter values are then used in the calculation of average repertoire size. Likelihood ratio tests were also carried out on the covariates to determine if the inclusion of each covariate improved the fit. Colombo and Jiang (2002) explain that there is a strong relationship between category purchase rate and repertoire size. A standardized average repertoire size was also calculated to account for this relationship and attempt to show the true relationship between each covariate under analysis and the repertoire size measure. -6-

19 'VIM= 1.6 Summary of Key Findings The key findings of this thesis include: Repertoire size varies across categories and increases with longer periods of observation. Observed repertoire sizes across 48 FMCG categories varied between 1.1 and 4.6 brands bought in a one-year period. A time period analysis (1 month, 3 months, 6 months, 1 year and 4 years) of the observed results showed that the repertoire size increased steadily as the period of observation increased. While repertoire size increases with longer periods of observation (as buyers have the opportunity to buy more brands from the category), the size of a buyer's repertoire can mostly be determined from just a few observed purchases from the category. For many buyers, in most categories, the size of the repertoire was established in the first few months of observation. This is in line with time series analysis of other loyalty measures where loyalty has been observed as being "near instant" (Ehrenberg and Goodhardt 2000). The yearly average repertoire size across all categories is 2. A comparison of these repertoire size results against consideration set research shows that repertoires are much smaller than consideration sets in the same categories. The average repertoire size formula accurately describes the observed repertoires sizes across four of the categories included in this thesis. Across all categories and covariates studied in this thesis, the effect of the covariates on repertoire is through the intermediary of the category purchase rate. The results of the analysis of all four categories examined using Repertoire Regression show that while each covariate is related to the size of the repertoire, that category purchase rate accounts for each of these relationships. 1.7 Structure of Thesis The chapters of this thesis cover the following material: -7-

20 Chapter 2: Repertoire Size and Consideration Sets This chapter provides a review of the relevant literature in terms of the study of repertoire sizes and consideration sets. The majority of this chapter involves a review of the consideration set literature as there is very little research on repertoire size as a measure of category structure or brand loyalty. Consideration sets are measured similarly to repertoire size, thus it was deemed important to evaluate the research carried out in this area as a precedent to under taking a study of repertoire size. This chapter describes the issues involved in consideration set research in definition and measurement of this construct thus emphasizing the significance of repertoire size as an alternative. Chapter 3: The Dirichlet Model Since the Dirichlet model is the model through which the theoretical estimate of repertoire size is calculated, this model is described in detail in this chapter. This chapter defines the model in technical terms and discusses the typical uses of this model. Brand performance measures, which are calculated using the model, will also be discussed. The theoretical formula for repertoire size will also be defined in this chapter. Chapter 4: Repertoire Regression This chapter describes Repertoire Regression the analysis technique developed and used in this thesis. A full mathematical description of Dirichlet Multinomial Regression (DMR) is given first to familiarize readers with this broader technique. The development of DMR to become Repertoire Regression is then given to provide researchers with all the information necessary to completely understand and implement this technique in their own research. Chapter 5: Methodology and the Data Chapter 5 outlines the methodology employed in response to each research objective. A description of the Matlab functions and Excel spreadsheet written to carry out the -8-

21 analysis of this thesis is given in this chapter. A description of the data is also given including discussion of descriptive summaries and collection methods for all data sets used. Chapter 6: Descriptive Results The results of the thesis are split into three chapters one for each research objective. Chapter 6 is the first results chapter. This chapter describes and explains the results from the analysis in response to the first research objective. The key findings from these results are also discussed in this chapter. Chapter 7: Empirical Validation Results Chapter 7 is set up the same as the previous chapter but addressing the second research objective. The results for the second objective are presented and explained. Key findings are also described. Chapter 8: Repertoire Regression Results This final results chapter provide the reader with the findings in response to the third research objective. Descriptions of the interpretations of the results as well as discussion of the key findings for this research objective are also given in this chapter. Chapter 9: Implications, Limitations and Future Research Chapter 9 provides a conclusion to the thesis. This chapter discusses the implications of the findings of the thesis for marketing managers and researchers working in the area. The limitations of the work are also discussed, along with an outline of future research that should be undertaken in this research area. -9-

22 TvCreal 1.8 Chapter Summary This chapter has provided an introduction to the thesis. This chapter has described the research topic for the thesis and has discussed the importance of this thesis as a contribution to the existing knowledge and understanding of repertoires and factors that may affect it. It has highlighted the lack of research in the area and the value of understanding repertoire size further. The research objectives have been given to help guide the thesis. The methodology has also been summarized in this chapter along with the key findings of the thesis. The chapter is concluded with a detailed description of the structure of this thesis. The following chapter will explore this area of research in more detail, with a review of the literature relating to repertoire sizes and consideration sets. -10-

23 -pcsam Chapter 2 Repertoire Size and Consideration Sets 2.1 Chapter Overview Repertoire size as a measure of market structure or loyalty is rarely discussed in the marketing literature. The opposite is true for consideration set measures. Although there are clear areas of overlap between the two measures, the volume of research on both varies considerably. Nevertheless, the literature on each measure is useful for this thesis. Consideration sets and repertoire size have many similarities and differences, both of which stem from the measurement of the constructs. Measurement of a consideration set is generally conducted using an attitudinal response from panellists, whereas the measurement of repertoire size is based on past buying behaviour of a market. With this in mind, this chapter reviews the relevant literature on both repertoire size and consideration set as measures of market structure and loyalty. It begins with a discussion of repertoire type markets and a review of the key studies (which are few) that have focused on the measurement of repertoire size. The consideration set literature is then discussed. This begins with the origin of the term and discussion of the range of measures that have been used to capture the construct. A table from the literature is then presented of mean consideration set sizes reported for a number of categories. There are three main areas of research in relation to consideration sets, which are all discussed in this chapter. This discussion will detail how these three areas of research are relevant to the issues addressed in this thesis.

24 2.2 Repertoire Size Repertoire size (sometimes referred to as portfolio size) is a measure that is scarcely considered by researchers. An important contribution to the literature on this measure is a study by Sharp, Wright and Goodhardt (2002). These authors present empirical evidence to support the notion that competitive repeat purchase markets can be categorised as either repertoire or subscription markets. In repertoire markets, there are a small number of solely loyal customers with most customers purchasing over a number of brands (Sharp, Wright et al. 2002). Examples of this type of market are Fast Moving Consumer Goods (FMCG) categories, like detergents or shampoos where customers purchase from a range of brands over an extended time period. Subscription markets, on the other hand, are more commonly composed of solely loyal customers who buy from a single brand despite making numerous purchases from the category over time. An example of this type of market is insurance where customers tend to buy from one insurance provider for any single insurance product rather than to regularly change from one brand to the next. It is important to note that there is also a middle ground between these two types of markets. One example of this are magazines which are bought through subscription services and also through newsagents and online platforms. While it may be argued that subscription markets are not substantially different from slow moving repertoire markets where few purchases are made from the category, Sharp and Wright (2002) empirically demonstrated that there are no examples (that they could identify) of categories that occupied the middle ground between these two types of markets in terms of brand performance measure and other diagnostics for market share. While Sharp and Wright (2002) have pointed out the clear difference between repertoire and subscription markets based on the number of brands bought by buyers in the market, Colombo and Jiang's study (2002) is the only one in the literature that has specifically looked at patterns in repertoire size. It is recognized early on in this paper that there is a lack of research in this area. Colombo and Jiang (2002) define repertoire size as the number of different brands a consumer buys for a given level of category usage. This implies that these authors believe that repertoire size is dependant upon category usage. Instead of looking at the repertoire size across all buyers, these authors look at the shape and size of the repertoire for consumers with a common level of category usage. Colombo and Jiang -12-

25 (2002) have derived an analytical expression for repertoire size, which will be detailed and explained in Chapter 3 (page 37). Their study takes this analytical expression for repertoire size and investigates its properties. More specifically, Colombo and Jiang (2002) look at how repertoire size varies as a function of the size of the category, category heterogeneity and the number of purchases made by buyers of the category. Purchase frequency is obviously related to repertoire size, with higher purchase frequencies being correlated with higher repertoire sizes. Repertoire size also increases as the number of brands in the category increase. This is simply because, with more brands in the category, there are more to choose from and so customers are likely to increase the number of brands from which they purchase. Colombo and Jiang (2002) also found that repertoire size increases as the degree of heterogeneity increases. The degree of heterogeneity in a market, as they define it, measures the extent to which buyers in that market switch between the brands available to them. Heterogeneity can be caused by either (1) more buyers switching between brands or (2) buyers switching between a wider range of brands. Colombo and Jiang (2002) report that the first type of heterogeneity would be expected to cause repertoire size variation whereas the second type could simply be a result of category growth. As the second type of heterogeneity increases (i.e. more buyers switch between brands), so does repertoire size. Despite being so few in number, these studies are (to the author's knowledge) the only direct contribution to the literature on repertoire size. Due to the scarcity of research on this measure, this thesis will make an important contribution in terms of theory and application in this area. While the studies focussing on repertoire size are few, research in other areas provides a good grounding for the analysis undertaken in this thesis. In terms of the measurement of repertoire size, the construct can only be captured after a shopper makes a purchase. There is much research that has been conducted on the brands a consumer considers prior to making a purchase. Authors have considered the cognitive difficulty of the brand choice process and suggest that consumers break down the brand choice problem into smaller more manageable problems. To do this, the consumer mentally sorts brands into different 'sets' using some form of criteria. These 'sets' are a sub-set of all the brands available in the product category, thus reducing the cognitive load that would be required if all brands -13-

26 were to be considered on all purchase occasions. There are a large number of definitions of these 'sets', such as awareness sets, inept sets, inert sets, evoked sets, consideration sets etc. The name given to these 'sets' is defined by the individual interpretation made by different researchers of how the choice process occurs. The most common of these definitions is the term consideration sets. The literature pertaining to the measurement of consideration sets is more extensive than that which has discussed repertoire size. 2.3 Consideration Sets The choice process has been an area of interest to researchers for many years (Bettman 1971; Bass, Givon, Kalwani, Reibstein and Wright 1984; Morgan and Trivedi 1996; Bettman, Luce and Payne 1998). Knowledge of how consumers make choices can help marketers understand and manipulate the elements of the marketing mix that influence such choice. When presented with choice, consumers are generally faced with a large number of alternatives to choose from. Such a choice is therefore seen as quite a difficult task that is high in terms of cognitive load for consumers, and subsequently steps are taken to simplify this process (Nedungadi 1990; Roberts and Lattin 1991; Shocker, Ben-Akiva, Boccara and Nedungadi 1991). It is for these reasons that it is suggested (Howard and Sheth 1969) that instead of considering all available brands in the market, a consumer is more likely to break down this brand choice process into more manageable parts. It is proposed that prior to making a purchase, consumers form a consideration set of the brands they would consider buying. This consideration set is a subset of all available brands in the market. From this set the consumer then makes their final decision of a brand for purchase. This is envisaged as a two-stage process of brand choice as opposed to the more simple one-stage process of considering all available brands in making a choice for purchase (Nedungadi 1990; Shocker, Ben-Akiva et al. 1991; Brown and Wildt 1992) Definition of Consideration Sets The founding literature that introduced the concept of a consideration set and defined most of the terms subsequently used by authors in this area, began with Howard and Sheth (1969). Howard and Sheth (1969) formalized the concept of consumers only considering a subset of brands for purchase. Their theory of purchasing behaviour began with the idea that buyers had an evoked set of brands that would come to mind in a buying situation and -14-

27 from which buyers would then choose a brand for purchase. These authors defined an evoked set as the brands that are alternatives in the buyer's choice decision. This concept of an evoked set, was later replaced by the words 'consideration set', perhaps because it better conveys what is being measured. The term 'evoked set' suggests that the values being measured are the brands that are evoked in the consumer's mind at the time of each purchase occasion. A consideration set is defined as those brands considered by the consumer for purchase. This concept was further developed by Gruca (1989) in the definition of an evoked set as the set of products or brands consumers are aware of and which are considered for purchase. Gruca (1989) therefore highlights the awareness of a brand and consideration for purchase as separate constructs. Even at this early stage in the development of research in this area, discrepancies in definitions of terms were already apparent. These discrepancies have continued throughout the literature. Brown and Wildt (1992) and Shocker, Ben- Akiva, Boccara and Nedungadi (1991) highlighted the lack of consistency in the definition and measurement of these constructs. They aimed to clarify any differences in the definitions and even more specifically in the wording of definitions (Brown and Wildt 1992). Some of the literature demonstrates only subtle differences in the conceptualisation and measurement of constructs termed "evoked" and "consideration" sets, while other authors use these terms to describe measures with substantial variations in their definition. As Brown and Wildt (1992) and Shocker et al (1991) correctly note, this may lead to major differences in the measures used by different authors, and as a result could also lead to different findings and interpretations of data. It also makes it particularly difficult to compare the different studies that have been undertaken in the area. The following diverse set of definitions for a consideration set is a combination of definitions captured by Brown and Wildt (1992) and others found in more recent papers. It provides a summary of the different ways in which the term "consideration set" has been used in the literature. Some definitions of the term consideration set are: The brands that the consumer recalls from memory that may fulfil their needs (Desai and Hoyer 2000) The goal-satisfying brands that are salient or accessible at a particular time (Shocker, Ben-Akiva et al. 1991) -15-

28 The subset of brands in a product category that a particular individual considers when making a purchase (Divine 1995) The brands that are brought to mind on a particular choice occasion (Nedungadi 1990) The brands the consumer perceives as substitutes for future purchase (Paulssen and Bagozzi 2006) The brands that are more seriously considered (Gensch and Soofi 1995) The brands a consumer would consider purchasing (Alba and Chattopadhyay 1985) The brands the subject would consider if faced with an immediate purchase decision (Klenosky and Rethans 1988) The brands the subject would consider for a specified consumption situation (Brisoux and Laroche 1981) The brands the subject would buy if buying today, and the other brands that the subject is willing to buy if the first choice is not available (Laroche, Rosenblatt and Sinclair 1984) Some of these definitions differ only subtly in their wording, however others differ substantially, predominantly due to their reference to buying situations when establishing such a consideration set. While some of these differences in definition are so subtle that it may seem unnecessary to acknowledge the difference, clarity of concept definition is crucial for concept measurement and development. This is particularly the case here, as measurement of a consideration set requires that customers be asked to describe the composition of their consideration set. The way in which this is done (or the nature and composition of the measure) is therefore crucial to collecting comparable consideration set data. For the situation specific definitions, consumers are asked which brands they would consider or have considered in reference to a specific choice situation. Different definitions of consideration sets also cause further issues in the measurement of this term. This means that the construct of 'consideration set' is measured differently across studies making it difficult to compare findings across studies. In more recent studies, however, the definition of consideration sets has become more consistent, and generally is the brands a consumer -16-

29 considers for purchase. This will hopefully lead to a universal definition and measurement standard for consideration set researchers in the future. Hauser and Wernerfelt (1990 p394) make some effort towards comparing descriptive results in the consideration set literature by providing a summary of mean consideration set sizes across a number of product categories. This table has been replicated in Table 1. An important take out from this table is that consideration set size varies considerably across the wide range of categories analysed. In some categories (i.e. beer, beer (USA), coffee, food product, margarine and tea), consideration set size is recorded as the number of brands purchased over a specified period of time. This is the definition of repertoire size used in this thesis. While this makes it impossible to compare these results for these categories with those gained using more traditional measures of 'consideration sets', it does provide some information to use for comparison with the descriptive information provided in this thesis. It also means that the information from these categories is comparable. This demonstrates the confusion that exists in the literature in terms of defining this construct and promotes the use of repertoire size as a more consistent and realistic measure of the range of brands from which people buy. It is important to understand that there are situations where a brand will be in the consideration set, but not the repertoire, and visa versa. For example where there is very low involvement, the consumer may fail to report or recall that the brand is in the consideration set, and yet they buy that brand. The same might occur where consumers have low awareness of the brands in the market, even those that are in the consideration set. Similarly, a brand may not even reach the consideration set until the point of purchase. There may also be brands with apparently effective marketing but minimal sales because they are often in the consideration set, but rarely purchased. Table 1 shows a wide variation in mean 'consideration set' size ranging from 2.2 for air fresheners to 7.0 for beer in Canada. Table 1 is only included in this thesis as a starting point for comparing consideration sets and repertoires as potential measures of likely future buying behaviour. The table, however, must be viewed with some caution. If the consumer samples used in the component studies are very different from one another, then the figures may reflect these sample characteristics more than the category characteristics. Clearly, a representative cross-section of the population could exhibit very different -17-