Life-Cycle Price and Shopping Frequency

Transcription

1 Life-Cycle Price and Shopping Frequency Introduction Shopping frequency receives continuous attention from marketing scholars and practitioners as it is closely related to consumer segmentation and household expenditures. Accordingly, previous research has attempted to investigate the demographic characteristics that determine shopping frequency (Aguiar & Hurst, 2005, 2007; Blaylock, 1989; Doti & Sharir, 1981; Kim & Park, 1997; Kim, Srinivasan, & Wilcox, 1999), and then retailers can infer consumers demographics from their shopping frequency. Shopping frequency is also considered closely related to price sensitivity. Research on the effect of shopping frequency on grocery expenditures has been documented in both the economic and the marketing literature (Aguiar & Hurst, 2007; Ainslie & Rossi, 1998; Blaylock, 1989; Kim & Park, 1997; Ma, Ailawadi, Gauri, & Grewal, 2011). However, the relationship between shopping frequency and price receives intermittent attention from previous researchers but is managerially important for retailers. This relationship is useful for retailers to identify the price distribution that consumers choose and to predict the potential consumer surplus. Furthermore, considering that consumers can be heterogeneous in terms of their shopping preference (i.e. products, brands, sizes of products, stores), costs (i.e. transportation, stockpiling, child number) and efficiency (discount usage, planned/ in-store buying), examining the relationship between shopping frequency and price is not substitutable by observing consumer demographic characteristics, household expenditure or price sensitivity. Hence, the relationship between shopping frequency and price needs further investigation in terms of seeking clarity and validity. This paper focuses on examining the relationship between shopping frequency and price and we provide behavioural explanations on the reason why each household type's shopping pattern elicits different price levels. This paper (1) aims to examine how the shopping patterns in terms of shopping frequency and numbers of stores visited vary across demographics, and to explore how prices paid for the same goods vary across households at different stages of life cycle. These patterns are important for retailer to infer consumers demographic characteristics from shopping frequency and indicate particular group of consumers who accept higher price. We (2) aim to find store variety seeking levels that differ across different household type based on UK home scan datasets, while no such store variety seeking behaviour was found based on US datasets (Aguiar & Hurst, 2007). This paper (3) aims to test the causal relationship between shopping frequency and price. However, we find that the result of the ordinary least squares (OLS) estimate is badly biased by the endogeneity problem. Hence, we take consumers' heterogeneity and other unobserved omitted variables into consideration and have a further test. Based on two-stage least squares estimate, our further finding is strongly validated by instrumental variables models indicating a positive impact of shopping frequency on price. Furthermore, our results evidenced that consumers' store variety seeking behaviour enlarges the price differences across household type up to 22%. Literature Review and Hypotheses Shopping frequency, price and store variety seeking The most important stream of literature relevant to this study investigates the relationship between shopping frequency with price and store variety seeking. Store variety seeking behaviour is well documented in previous literature as a long-existing and popular shopping pattern (Ailawadi & Harlam, 2004; Ailawadi, Pauwels, & Steenkamp, 2008; Bell, Ho, & Tang, 1998; Bustos-Reyes & Gonzalez-Benito, 2008; Corstjens & Lal, 2000; Dhar, Dutta, & Dhar, 2002; Liu, 2007; Rhee & Bell, 2002; Tate, 1961). Prior empirical findings evidenced that loyal customers are more profitable because they tend to spend more, allocate

2 most of their expenditures to their main store, and be less sensitive to prices and promotions (Enis & Paul, 1970; Knox & Denison, 2000). Previous literature use the number of store visited as the measurement of the level of store loyalty, which implies the level of store variety seeking (Enis & Paul, 1970; Jacoby & Kyner, 1973; Sharp & Sharp, 1997; Tate, 1961). Increased shopping frequency was likely to imply shopping in a wider variety of stores and higher store switching cost (Blaylock, 1989; Dhar & Hoch, 1997). Shopping in larger number of stores means more opportunities in spreading expenditures across stores and higher likelihood in purchasing in expensive stores (Blaylock, 1989). A recent empirical study evidenced that based on 12,000 Japanese households who disperse their expenditures across more number of stores pay higher prices in grocery shopping than household who are low in store variety seeking (Naohito & Kyosuke, 2011). Hence,Consumers may pay higher prices even if they shop more often due to visiting a wide variety of stores. According to the main objects of this study, Hypotheses are summarised as follows: Hypothesis 1: Older consumers shop more often than younger consumers. Hypothesis 2: Consumers without children shop more often than those with children. Hypothesis 3: Older consumers visit more number of stores than their younger counterpart. Hypothesis 4: Consumers without children shop in more stores than those without children. Hypothesis 5: Shopping frequency is positively associated with price paid given the fact that consumers are differing in store variety seeking level. Data Our analysis uses TNS Homescan data in Great Britain. The data set includes information about an entire range of FMCGs for 156 weeks from Oct 2002 to Dec Within 21 main groups of food and non-food products, we have 189 product categories and 185,495 brands in total. We have 12,477 UK households (39,883,691 observations in total) regionally balanced across 10 regions in UK. Specifically, the demographics information contains: the main shopper s age, marital status, number of children, and number of toilets in a house. We categorize households according to the age of main shopper into 9 cohorts: 25-29, 30-34, 35-39, 40-44, 45-49,50-54, 55-59,60-64, We also distinguish the households by a mixedindicator, which reflects the household s marriage, children and age characteristics. The indicators are: senior single, senior couple, single adult, couple no child, other no child, couple with child, other with child, lone parent. Descriptive statistics of household variables are provided upon request. Results Price Paid and Shopping Frequency across Age Groups In this subsection, we compare the price paid for a constant basket of goods among households from different age groups, and shopping frequency across different ages. We look at the price paid by each age group and the shopping frequency for each age group by running Ordinary Least Square (OLS) regressions 1 : (1) (2) In the equation (1) and (2), Age are dummy variables, dividing the whole sample into 9 cohorts; Shopping needs are control variables, including quantity of goods purchased, number of UPC codes purchased, and the number of product categories purchased; and represent residual in each regression equation respectively. 1 We build a price index that is used to compare the actual cost of a household s shopping basket to the cost of the identical shopping basket at average prices. Moreover, we control consumers shopping needs in terms of how much they buy (quantity), how many categories they buy, and how many brands they buy in order to make each household commeasurable in order to generate results based on the same basket of goods.

3 The regression results based on Equation (1) and (2) are presented in Figure 1 (Details are upon request). Results suggest that the old people tend to do shopping more frequently and pay higher price than the younger people (See diamond line in Figure 1). Even when we control the shopping needs, the pattern of prices paid remains the same (See pyramids line in Figure 1). Hence, Hypothesis 1 is supported. Our findings are in sharp contrast with those in Aguiar and Hurst (2007). Figure 1. Price and Shopping Frequency across Age Ranges Price Paid and Shopping Frequency across Household Types ln(price) = λ 1 + λ 2 HouseholdType + λ 3 ShoppingNeeds +κ (3) ln(shoppingfrequency) = ρ 1 + ρ 2 HouseholdTypes + ρ 3 ShoppingNeeds +ζ (4) We report the OLS results from Equation (3) and Equation (4) in Table A3 (upon request). Figure A1 (upon request) plots the log price index relative to households whose primary shopper is lone parent. It is shown that the households without child are shopping more frequently than those with child. Hence, Hypothesis 2 is supported. Store Variety and Store Intensity Regressing i) log number of stores visited (the measurement of store variety), or ii) log number of trips per store (the measurement of store intensity) on age, controlling for shopping needs, gives us information about life cycle pattern of store variety and store intensity. Figure 2 shows that the older shoppers visit more stores and shop more frequently for each store than their younger counterpart. Figure 2. Store Variety and Store Intensity across Age Range

4 Hence, Hypothesis 3 is supported. More number of stores visited reflects lower store loyalty and higher shopping diversity. Hence, the low store loyalty shoppers may less likely to take advantage of in-store promotion attached with store loyalty cards. Furthermore, shoppers visiting more stores are more likely to spread expenditures across stores and with higher likelihood in purchasing in expensive stores. Above all, the difference in store variety accounts for the major part of difference in overall shopping frequency. And we expect that this key feature of shopping pattern have a dominant effect on price paid during life cycle and across different household types. In Figure A2 (upon request), we plot two series (store variety and store intensity) in log deviation from benchmark group across different household types, with shopping needs being controlled. The benchmark group is lone parent, which on average visits 3.1 stores each month and shops 2.0 times for each store. The Figure A2 indicates that generally consumers without child shop in more stores than households with child. Hence, Hypothesis 4 is supported. Elasticity of Price with respect to Shopping Frequency and Store Variety In this subsection, we estimate the elasticity of price to the variation in shopping frequency and number of stores visited. We use instrumental variables (IV) estimation to correct for endogeneity of shopping frequency and store variety. First, we assume it is in a log-linear function form 2, (5) (6) In above two equations, represent residuals. is the elasticity coefficient we are interested in, which measures how sensitive price will be affected by change in shopping frequency/store variety. The equations above are first estimated using ordinary least square (OLS). As shown in Table 1, Column I, the estimation of elasticity coefficient from equation (5) is It indicates that prices will be lowered by 0.2 per cent if the shopping frequency is doubled. The Column V reports the results of OLS estimated elasticity coefficient of (6) is In other words, households will pay 0.4 per cent less prices by doubling the number of stores they visited. Table 1. Elasticity of Price with Respect to Shopping Patterns Ln_P I II III IV V VI VII VIII Estimated elasticity: Standard error Measure of shopping patterns Regression type Instrument set Sargan test (p-value) Shopping frequency per month Shopping frequency per month Shopping frequency per month Shopping frequency Number of stores visited Number of stores visited Number of stores visited OLS IV IV IV OLS IV IV IV None Toilet number 3 Marriage Marriage and Number of Toilets as None Toilet number Marriage NA NA Number of stores visited Marriage and Number of Toilets as But OLS results are biased by the fact that independent variable is endogenous. Hence, we take consumers' heterogeneity and other unobserved omitted variables into consideration 2 This is in line with the specification form adopted by Aguiar and Hurst (2007). 3 The number of toilets is used as a proxy of income.

5 and have a further test. Based on two-stage least squares estimate, Column II, III, IV, VI, VII VIII in Table 1 shows that our further finding is strongly validated by instrumental variables models indicating a positive impact of shopping frequency on price. Therefore, Hypothesis 5 is supported. Whether the store variations explain the differences in price paid? To get an answer, we re-estimated the regressions of Table A2 and Table A3 (both upon request), Column II, including the average number of store visited (store variety). This additional control explains a large proportion of variation in price paid across age ranges and household types. Specifically, the 1.4 per cent differential in price paid between those aged and those aged increases to 1.7 per cent. It means that 22 percent of the increase in price post-middle age can be explained by store variety seeking behavior. Conclusion Based on 30+ millions UK scanner data points, the most important contribution of this paper is that consumers shopping patterns are completely different from what was found in US. Our finding indicates that increased number of shopping frequency results in paying higher price, in particular, the older and consumers without children shop more often and pay more. On the contrary, US study found that the older consumers who pay lower prices shop more often, visit the same store more often but not visit more numbers of stores (Aguiar & Hurst, 2007). The major reason is that the store variety seeking behaviour differs between UK and US. We found that store variety seeking provides behavioural explanations to why shopping more often results in paying higher price. It indicates that consumers who shop in more numbers of stores face higher price even if they shop more often. This finding is interesting to present big differences exist in shopping patterns across countries, and it is helpful for retailers to revaluate the importance of attract existing customers to not only repeatedly visiting theirs stores but also visiting more often. It also indicates further research direction addressing the reasons why Aguiar and Hurst (2007) s findings based on US datasets are different from ours based on UK datasets. Further research may aim to develop a comprehensive demand analysis in order to discover how and to what extend households are able adopt their shopping behaviour in terms of selecting what to buy, when to buy, how much to buy and where to buy. Since most consumer studies focused on grocery purchases only, non-food purchases receive intermittent attention of researchers mainly due to unavailability of the data set. The second contribution of this paper is that our study covers not only food but also non-food datasets, thus it helps us to document consumers shopping patterns more comprehensively. Recent study based on food and non-food purchases documents the potential and actual saving that consumers realize from different shopping choices(griffith et al., 2009), and our study extends it in terms of analysing how expensive, instead of how much, they pay for the same basket of goods. Such an analysis is important not only for researchers but also for manufacturers and retailers who are endeavour to find the most efficient way to respond to changes in consumers shopping strategy and understand price acceptance for different consumer segments. Third contribution of this paper is that it takes consumer heterogeneity into consideration due to the fact that households may differ in their shopping skills, preferences and inventory costs by using instrumental variables 4. By adopting instrumental variables, we generated a more reliable comparison in real purchasing power across time and consumers 5. We have to admit that this paper has not take the effect of online shopping into consideration due to the limited data access. Further research can be developed in terms of 4 Durbin-Watson tests for equation (5) and (6) are significant at 5% level suggesting that the ordinary least square results are seriously biased by omitted variables. 5 The p-value of Sargan tests for all two-stage least squares range from 0.3 to 0.9 (See Table 1).

6 using online shopping datasets to address the impact of online shopping behaviour in shaping traditional consumer behaviour.

7 References Aguiar, M., & Hurst, E. (2005). Consumption versus expenditure. Journal of Political Economy, 113(5), Aguiar, M., & Hurst, E. (2007). Life-cycle prices and production. American Economic Review, 97(5), Ailawadi, K. L., & Harlam, B. (2004). An empirical analysis of the determinants of retail margins: The role of store-brand share. Journal of Marketing, 68(1), Ailawadi, K. L., Pauwels, K., & Steenkamp, J. B. E. M. (2008). Private-Label Use and Store Loyalty. Journal of Marketing, 72(6), Ainslie, A., & Rossi, P. E. (1998). Similarities in choice behavior across product categories. Marketing Science, 17(2), Bawa, K., & Ghosh, A. (1999). A Model of Household Grocery Shopping Behavior, (1999) 10:2, Marketing Letters, 10(2), Bell, D. R., Ho, T. H., & Tang, C. S. (1998). Determining where to shop: Fixed and variable costs of shopping. Journal of Marketing Research, 35(3), Blattberg, R., Buesing, T., Peacock, P., & Sen, S. (1978). Identifying Deal Prone Segment. Journal of Marketing Research, 15(3), Blaylock, J. R. (1989). An Economic-Model of Grocery Shopping Frequency. Applied Economics, 21(6), Bustos-Reyes, C. A., & Gonzalez-Benito, O. (2008). Store and store format loyalty measures based on budget allocation. Journal of Business Research, 61(9), Corstjens, M., & Lal, R. (2000). Building store loyalty through store brands. Journal of Marketing Research, 37(3), Dhar, S. K., Dutta, S., & Dhar, S. K. (2002). Asymmetric store positioning and promotional advertising strategies: Theory and evidence. Marketing Science, 21(1), Dhar, S. K., & Hoch, S. J. (1997). Why store brand penetration varies by retailer. Marketing Science, 16(3), Doti, J. L., & Sharir, S. (1981). Households Grocery Shopping Behavior in the Short-Run - Theory and Evidence. Economic Inquiry, 19(2), Enis, B. M., & Paul, G. W. (1970). Store Loyalty as a Basis for Market Segmentation. Journal of Retailing, 46(3), Griffith, R., Leibtag, E., Leicester, A., & Nevo, A. (2009). Consumer Shopping Behavior: How Much Do Consumers Save? Journal of Economic Perspectives, 23(2), Ho, T. H., Tang, C. S., & Bell, D. R. (1998). Rational shopping behavior and the option value of variable pricing. Management Science, 44(12), S145-S160. Hursh, S. R., & Silberberg, A. (2008). Economic demand and essential value. Psychological Review, 115(1), Jacoby, J., & Kyner, D. B. (1973). Brand Loyalty Vs Repeat Purchasing Behavior. Journal of Marketing Research, 10(1), 1-9. Kahn, B. E., & Schmittlein, D. C. (1992). The Relationship between Purchases Made on Promotion and Shopping Trip Behavior. Journal of Retailing, 68(3), Kalyanaram, G., & Little, J. D. C. (1994). Am Empirical-Analysis of Latitude of Price Acceptance in Consumer Package Goods. Journal of Consumer Research, 21(3), Kennedy, R., & Ehrenberg, A. S. C. (2001). Competing retailers generally have the same sorts of shoppers. Journal of Marketing Communications, 7(1), Kim, B. D., & Park, K. (1997). Studying patterns of consumer's grocery shopping trip. Journal of Retailing, 73(4), Kim, B. D., Srinivasan, K., & Wilcox, R. T. (1999). Identifying price sensitive consumers: The relative merits of demographic vs. purchase pattern information. Journal of Retailing, 75(2), Knox, S., & Denison, T. J. (2000). Store loyalty: its impact on retail revenue. An empirical study of purchasing behaviour in the UK. Journal of Retailing and Consumer Services, 7, Liu, Y. P. (2007). The long-term impact of loyalty programs on consumer purchase behavior and loyalty. Journal of Marketing, 71(4),

8 Ma, Y., Ailawadi, K. L., Gauri, D. K., & Grewal, D. (2011). An Empirical Investigation of the Impact of Gasoline Prices on Grocery Shopping Behavior. Journal of Marketing, 75(2), Miller, H. L. (1976). Matching-Based Hedonic Scaling in Pigeon. Journal of the Experimental Analysis of Behavior, 26(3), Miranda, M. J., Kónya, L., & Havrila, I. (2005). Shoppers' satisfaction levels are not the only key to store loyalty. Marketing Intelligence & Planning, 23(2), Naohito, A. & Kyosuke, S. (2012). Who faces higher price? -- An empirical evidence based on Japanese Homescan data. Working Paper Hitotsubashi University. Rhee, H., & Bell, D. R. (2002). The inter-store mobility of supermarket shoppers. Journal of Retailing, 78(4), Rupert, P., Rogerson, R., & Wright, R. (1995). Estimating Substitution Elasticities in Household Production Models. Economic Theory, 6(1), doi: Doi /Bf Sharp, B., & Sharp, A. (1997). Loyalty programs and their impact on repeat-purchase loyalty patterns. International Journal of Research in Marketing, 14(5), Tate, R. S. (1961). The Supermarket Battle for Store Loyalty. Journal of Marketing, 25(6), 8-13.