Information Resources Inc Research Articles

Tobacco Retail Sales in Massachusetts and New Hampshire Before and After Massachusetts' Flavored Tobacco Restriction.

In June 2020, Massachusetts became the first state to implement a comprehensive flavored tobacco restriction. One concern was that Massachusetts residents would travel to New Hampshire to purchase restricted products. This article assesses tobacco sales in both states post-law implementation. Retail scanner data were obtained from the Nielsen Company and Information Resources, Inc (IRI), from 1 year pre-law implementation to 2 years post-law implementation. Data post-law implementation were compared with data from 1 year pre-law implementation (baseline). In Massachusetts, 2 years post-law implementation, flavored and menthol tobacco sales decreased by more than 90%. Total sales decreased by around 20%. In New Hampshire, menthol tobacco sales increased (25.1% in IRI and 18.2% in Nielsen), but total sales changed minimally (<5% increase in IRI, <5% decrease in Nielsen). When data from both states were combined, total sales decreased by around 10%. The net decrease in total tobacco sales across Massachusetts and New Hampshire indicates Massachusetts' flavored tobacco restriction resulted in a reduction in tobacco sales despite potential cross-border purchases.

Characteristics Associated With Purchasing Sugar-Sweetened Beverages and Bottled Water Among US Households, 2015

BackgroundFrequent intake of sugar-sweetened beverages (SSBs) among US adults is a public health concern because it has been associated with increased risks for adverse health outcomes such as obesity, type 2 diabetes, and cardiovascular disease. In contrast, drinking plain water (such as tap, bottled, or unsweetened sparkling water) instead of drinking SSBs might provide health benefits by improving diet quality and helping prevent chronic diseases. However, there is limited information on estimated expenditures on SSBs or bottled water among US households. ObjectiveThis study examined differences in SSB and bottled water purchasing according to household and geographic area characteristics and estimated costs spent on purchasing SSB and bottled water from retail stores among a nationally representative sample of US households. DesignThis study is a secondary analysis of the 2015 Circana (formerly Information Resources Inc) Consumer Network Panel data, which were merged with the US Department of Agriculture nutrition data using the US Department of Agriculture Purchase-to-Plate Crosswalk-2015 dataset (the latest available version of the Purchase-to-Plate Crosswalk at the time the study began), and the Child Opportunity Index 2.0 data. Participants/settingsA total of 63,610 households, representative of the contiguous US population, consistently provided food and beverage purchase scanner data from retail stores throughout 2015. Explanatory variablesThe included demographic and socioeconomic variables were household head’s age, marital status, highest education level, race and ethnicity of the primary shopper in the household, family income relative to the federal poverty level, and presence of children in the household. In addition, descriptors of households’ residential areas were included, such as the county-level poverty prevalence, urbanization, census region, and census tract level Child Opportunity Index. Main outcome measuresAnnual per capita spending on SSB and bottled water and daily per capita SSB calories purchased. Statistical analysisUnadjusted and multivariable adjusted mean values of the main outcome measures were compared by household demographic, socioeconomic, and geographic characteristics using linear regression analysis including Circana’s household projection factors. ResultsNearly all households reported purchasing SSBs at least once during 2015 and spent on average $47 (interquartile range = $20) per person per year on SSBs, which corresponded to 211 kcal (interquartile range = 125 kcal) of SSBs per person per day. About seven in 10 households reported purchasing bottled water at least once during 2015 and spent $11 (interquartile range = $5) per person on bottled water per year. Both annual per capita SSB and bottled water spending, and daily per capita SSB calories purchased was highest for households whose heads were between 40 and 59 years of age, had low household income, or lived in poor counties, or counties with a low Child Opportunity Index. Annual per capita spending was also higher for households with never married/widowed/divorced head, or at least 1 non-Hispanic Black head, and households without children, or those living in the South. Daily per capita SSB calorie purchases were highest for households where at least 1 head had less than a high school degree, households with at least 1 Hispanic or married head, and households with children or those living in the Midwest. ConclusionsThese findings suggest that households that had lower socioeconomic status had higher annual per capita spending on SSBs and bottled water and higher daily per capita total SSB calories purchased than households with higher socioeconomic status.

E-cigarette Unit Sales by Product and Flavor Type, and Top-Selling Brands, United States, 2020-2022.

E-cigarette products, related policies, and use patterns change rapidly. In the United States, the prevalence of e-cigarette use is markedly higher among youths and young adults than it is among adults overall. In 2021, 4.5% of all adults aged ≥18 years (an estimated 11.1 million) and 11.0% of young adults aged 18-24 years (an estimated 3.1 million) currently (≥1 day during the previous 30 days) used e-cigarettes; during 2022, 14.1% of high school students (an estimated 2.14 million) currently used e-cigarettes (1,2). E-cigarettes often contain high concentrations of nicotine. Nicotine is highly addictive and can harm the adolescent brain, which continues to develop through approximately age 25 years (3). Since 2020, the availability of e-cigarette products has changed in response to multiple factors, including local and state policies to address flavored e-cigarette sales, actions undertaken by the Food and Drug Administration (FDA), COVID-19-related closures, and global supply chain disruptions. To assess trends in unit sales of e-cigarettes in the United States, by product and flavor, and top-selling brands, the CDC Foundation, Truth Initiative,* and CDC analyzed retail scanner data during January 26, 2020-December 25, 2022, from Information Resources, Inc. (IRI), a U.S. data analytics and market research company. Overall, unit sales increased by 46.6% during the study period. The unit share of menthol-flavored product sales remained relatively stable during this period, whereas nonmenthol flavor unit shares changed. During January 26, 2020-December 25, 2022, unit shares of tobacco-flavored and mint-flavored products decreased (from 28.4% to 20.1% and from 10.1% to 5.9%, respectively), whereas shares of other flavor sales increased (from 29.2% to 41.3%). In addition, during January 2020-December 2022, unit shares of prefilled cartridges decreased from 75.2% to 48.0%, and disposable e-cigarette unit share increased from 24.7% to 51.8% of total unit sales. The five top-selling e-cigarette brands for the 4-week period ending December 25, 2022, were Vuse, JUUL, Elf Bar, NJOY, and Breeze Smoke. Analysis of information on e-cigarette retail sales can guide strategies to prevent youth access to and use of e-cigarettes, including restrictions on flavored tobacco products (4).

Association Between County-Level Food Retail and Socioeconomic Environment and Nutritional Quality of Household Food Purchases, 2015.

About 40 million Americans do not have easy access to affordable nutritious foods. Healthier foods are less likely to be available to those living in rural and/or lower-income communities. The objective of this study was to analyze the association between nutritional quality of household food purchases and county-level food retail environment; county-level demographic, health, and socioeconomic indicators; and household composition, demographic characteristics, and socioeconomic characteristics. This study is a secondary analysis of the 2015 Information Resources Inc Consumer Network panel; Purchase-to-Plate Crosswalk, which links US Department of Agriculture nutrition databases to Information Resources Inc scanner data; County Health Rankings; and the Food Environment Atlas data. A total of 63,285 households, representative of the contiguous US population, consistently provided food purchase scanner data from retail stores throughout2015. Nutritional quality of retail food purchases was assessed using the Healthy Eating Index 2015 (HEI-2015). Multivariate linear regression analysis was used to simultaneously test the relationship between the main outcome and household-level demographic and socioeconomic characteristics as well as the county-level demographic, health, socioeconomic, and retail food environment. Household heads who had higher education and households with higher incomes purchased food of better nutritional quality (ie, higher HEI-2015 scores). Also, the association between retail food purchase HEI-2015 scores and the food environment was weak. Higher density of convenience stores was associated with lower retail food purchase nutritional quality for higher-income households and households living in urban counties, whereas low-income households in counties with higher specialty (including ethnic) store density purchased higher nutritional quality food. Both in the full sample and when stratified by household income or county rural vs urban status, no association was found between grocery store, supercenters, fast-food outlets, and full-service restaurant densities and retail food purchase HEI-2015 scores. HEI-2015 scores were negatively correlated with the county average number of mental health days for higher income and urban households. The study findings suggest that availability of healthier food alone may not improve healthfulness of retail food purchases. Future studies examining the influence of demand-side factors/interventions, such as habits, cultural preferences, nutrition education, and cost/affordability, on household purchasing patterns could provide complementary evidence to inform effective intervention strategies.

Volumetric choice experiments (VCEs)

Volumetric Choice Experiments (VCEs) are designed to capture purchase quantities rather than a single, discrete choice. They can be seen as an extension of Discrete Choice Experiments (DCEs) where individuals decide how many units of a specific good or service to buy/use rather than deciding whether to buy/use it or not. There is different information in such integer count data than is contained in traditional binary or multinomial discrete choices, which presents new opportunities and interesting challenges. Like DCEs, VCEs have different components ranging from experimental design to modelling and our focus is on the overall process of implementation rather than detailed analysis of components. Our empirical examples come from large-scale VCEs embedded in surveys administered to samples drawn from Information Resources, Inc. (IRI) consumer panel for two product categories: single serve-coffee K-pods and canned tuna. The response for each alternative is a planned purchase count, possibly zero. These counts are fit using a negative binomial regression with a multilevel mixed-effects specification. Our VCE design allows for statistical identification of own- (brand by size) and cross-price elasticities, plus the effects of other attributes and demographics and their interactions with prices. The external validity of our approach is compared to results on actual canned tuna data purchases from the same IRI panelists. Advantages and limitations of VCEs as well as many unresolved research issues are discussed.

Lactose-Reduced Infant Formulas Are Over-Consumed Whereas Hypoallergenic Infant Formulas Are Not - When Compared to Medical Necessity

ObjectivesWe aimed to report the macronutrient composition of powdered infant formula purchased from major US physical-location retailers from 2017 through 2019. We then compared the percentage of lactose-reduced and hypoallergenic powdered infant formula consumed with the estimated prevalence of infant conditions necessitating a lactose-reduced or hypoallergenic formula. We also compared the proportion of formula consumed that was lactose-reduced between WIC and non-WIC purchases. MethodsAnnual powdered infant formula volume and purchase data from all major brick-and-mortar stores in the United States (excluding Costco) from 2017 through 2019 was obtained from IRi (Information Resources Inc). Protein, carbohydrate, fat composition and scoop size for each formula was obtained from formula companies and equivalent liquid ounces (“formula consumed”) was calculated. Small can sizes of “standard” (intact and partially hydrolyzed protein) formulas served as proxies for WIC purchases. Proportions were compared to published medical prevalence by calculating a z-ratio, t-test. ResultsAverage infant carbohydrate consumption consisted of 56.6% lactose, 37.7% glucose, and 5.7% sucrose. 21% of formula consumed contained sucrose. 55% of all formula and 47% of standard formula consumed was lactose-reduced, both higher than the estimated 7.5% prevalence of medical necessity (p < 0.0002). Lactose-reduced standard formula represented 51% of non-WIC sales which was greater than the 44% of WIC sales (p = 0.003). Protein consumption consisted of 5.0% soy protein, 74.1% intact dairy, 11.1% partially hydrolyzed dairy, 7.1% fully hydrolyzed protein, and 0.3% amino acid based. Hypoallergenic (fully hydrolyzed and amino acid-based) formula represented 7.4% of formula consumed, which did not differ from liberal estimates of cow’s milk allergy prevalence (7.5%). ConclusionsEstimated consumption of hypoallergenic formula in the US matches liberal estimates of medical necessity. However, a high proportion of formula-fed infants are consuming non-lactose-based carbohydrate; more than is medically warranted, necessitating further research into metabolic implications. Funding SourcesNone.

E-cigarette Unit Sales, by Product and Flavor Type - United States, 2014-2020.

Since electronic cigarettes (e-cigarettes) entered the U.S. marketplace in 2007, the landscape has evolved to include different product types (e.g., prefilled cartridge-based and disposable products) and flavored e-liquids (e.g., fruit, candy, mint, menthol, and tobacco flavors), which have contributed to increases in youth use (1,2). E-cigarettes have been the most commonly used tobacco product among U.S. youths since 2014; in 2019, 27.5% of high school students reported current e-cigarette use (3). To assess trends in unit sales of e-cigarettes in the United States by product and flavor type, CDC, CDC Foundation, and Truth Initiative analyzed retail scanner data during September 14, 2014-May 17, 2020, from Information Resources, Inc. (IRI). During this period, total e-cigarette sales increased by 122.2%, from 7.7 million to 17.1 million units per 4-week interval. By product type, the proportion of total sales that was prefilled cartridge products increased during September 2014-August 2019 (47.5% to 89.4%). During August 2019-May 2020, the proportion of total sales that was disposable products increased from 10.3% to 19.8%, while the proportion that was prefilled cartridge products decreased (89.4% to 80.2%). Among prefilled cartridge sales, the proportion of mint sales increased during September 2014-August 2019 (<0.1% to 47.6%); during August 2019-May 2020, mint sales decreased (47.6% to 0.3%), as menthol sales increased (10.7% to 61.8%). Among disposable e-cigarette sales during September 2014-May 2020, the proportion of mint sales increased (<0.1% to 10.5%), although tobacco-flavored (52.2% to 17.2%) and menthol-flavored (30.3% to 10.2%) sales decreased; during the same period, sales of all other flavors combined increased (17.2% to 62.1%). E-cigarette sales increased during 2014-2020, but fluctuations occurred overall and by product and flavor type, which could be attributed to consumer preferences and accessibility. Continued monitoring of e-cigarette sales and use is critical to inform strategies at the national, state, and community levels to minimize the risks of e-cigarettes on individual- and population-level health. As part of a comprehensive approach to prevent and reduce youth e-cigarettes use, such strategies could include those that address youth-appealing product innovations and flavors.

Impact of Reference Prices on Product Positioning and Profits

The existence of reference price effects in consumer decision making is well documented in prior research, but few studies focus on its implications for firms’ strategic behavior. Using a competitive model, we address this gap by examining how firms’ product positioning and pricing strategies in a non‐durable goods market (where consumers repeatedly purchase products from the category) are affected compared with a benchmark situation in which reference price effects are not pertinent. In a model with internal reference price effects, we find that as the salience of reference price effect increases, (a) product differentiation first decreases and then increases; and (b) firm profits first decrease and then increase. Using data from Information Resources, Inc. (IRI) dataset, we empirically validate our findings. We contribute to the product positioning literature by uncovering the role of internal reference price effects on product positioning and profits.

Developing an Ingredient Database of Commercially Packaged Retail Foods (IngIDB), Using the USDA Branded Food Products Database, Illustrated with Food Category ‘Cookies’ (P04-131-19)

ObjectivesTo develop a database of ingredients used in high-selling commercially packaged retail foods, using the publicly available USDA Branded Food Products Database (BFPD). MethodsNutrient Data Laboratory analyzed 2016 IRI (Information Resources Inc.) market sales data, and identified 32 top food categories that contribute > = 1% based on either total volume/weight or number of products sold in the U.S. These include 5 baked product categories – cookies, crackers, fresh breads and rolls, pastries and doughnuts, and pies and cakes. Ingredient lists for the products contributing over 0.001% of the total weight sold for the category were obtained from BFPD. If information for the product was not available in BFPD, other sources such as manufacturer’s website were used for top-selling products (contributing over 0.1% of the total weight sold for the category), with the goal to obtain ingredient lists for at least 70–80% of the products sold by total weight for the category. Individual ingredients listed in the ingredients lists were parsed and reviewed. Many of these ingredients were synonyms, necessitating the need for a thesaurus, to facilitate combining same ingredients described otherwise. A prototype thesaurus was developed for flour and fat ingredients in the food category ‘cookies’. ResultsFor food category ‘cookies’, there are ∼13,500 products available, of which ingredient lists were obtained for 1718 products representing ∼84% of the total cookies sold by weight (BFPD: 1699 products (79%); other sources: 19 top-selling products (4%)), hence, meeting our goal. These 1718 cookie products use about 2500 uniquely described ingredients, as per the ingredients lists on the labels. These ingredients include about 30 different types of enriched flours described in ∼ 220 unique ways, including spelling errors etc on the ingredient lists. The thesaurus will need to be expanded to other ingredients. ConclusionsThe publicly available BFPD can be a useful resource for developing an Ingredient Database for commercially packaged retail foods. The database will be used to prioritize ingredients that need to be chemically analyzed for nutrient information, and provide insights on ‘What is in the foods we eat in America? ’ Funding SourcesN/A.

The effects of retail concentration on retail dairy product prices in the United States

This study provides an empirical investigation of the relationship between grocery retail concentration and retail dairy product prices in the United States. The analysis was performed based on a unique data set on store-level retail prices provided by the Information Resources Inc. Further, alternative measures of retail concentration were considered, which included revenue and store selling space-based Herfindahl-Hirschman Index that were computed based on a Nielsen TDLinx data set on store characteristics. Results from a reduced-form empirical framework estimated via panel data techniques indicated that grocery retail concentration had a positive statistically significant effect on retail dairy product prices in the analyzed locations during the analyzed period of time. Specifically, a 10% increase in concentration was found to lead to a 0.46% rise in retail dairy product prices. This central result was robust to the way in which retail concentration was measured and was consistent with broader empirical evidence in the literature on retail market power.

Too many products? Reaching the next billion customers of the beauty market

Purpose – The purpose of this article is to describe the rationale behind and analyze the results of a strategy in regards to changing conditions and market share dominance. For more than 20 years, with the growth in available product varieties, product and brand proliferation have become increasingly evident in many consumer markets. Design/methodology/approach – This article examines how three L’Oréal mass market businesses, i.e. L’Oréal Paris, Lascad and Gemey-Maybelline-Garnier (GMG), managed proliferation activities between 1988 and 2012 on their domestic market. Data were extracted from the information resources, inc. (IRI) Census and Sample Databases (retail panel data), and information was collected from internal sources and semi-structured interviews with top executives. Brand performance was assessed using panel data structure analysis, as recommended by IRI and Nielsen. Some data, as they remain confidential, were not included in the paper. Findings – The study reveals that when opportunities are lacking, demand is declining, and competition is fierce – the situation that marks most mature markets – a proliferation strategy actually can yield diminishing results and reduced brand dominance. Research limitations/implications – Offering broader lines appears to generate confusion and to be counterproductive in relation to theoretical assumptions. Additional research on proliferation strategies is needed, particularly in declining market conditions, which implies diminished demand and market saturation due to increased competition and isomorphic practices. Practical implications – When deciding to extend product lines, managers should take into account competition and more qualitative factors than those included in the models developed by and for store brands. The respective positioning and marketing strategy (i.e. challenger and leader) of the brands involved have also to be considered. Originality/value – Prior research on proliferation strategies has relied on strong assumptions such as increase in demand and unsaturated markets. Through these case studies, this article shows that making the shelf space denser affects brand dominance, particularly when market conditions change. These results challenge current thinking as, in facing internal and external contingencies, managers might think which scenario is most favorable for maintaining a dominant position: changing the structure of the market by reducing (i.e. concentration) or increasing the number of brands/products in the market.

Assessing the Demand for a Functional Food Product: Is There Cannibalization in the Orange Juice Category?

The demand for functional foods has increased notably in recent years due to growing consumer interest in diet and health issues. Currently, the food industry is introducing many types of new food products with functional attributes. Consequently, cannibalization is a critical issue for firms that offer multiple products within a certain product category. The identification and assessment of cannibalization are integral factors when making strategic decisions about new product introductions. Using scanner data from Information Resources, Inc. (IRI), pertaining to a particular functional food, namely a phytosterol-enriched product for orange juice, we find that no cannibalization effects exist with respect to its introduction. We also provide estimates of own-price and cross-price elasticities of the orange juice category using a synthetic demand system.

2000–2001 food label and package survey: an update on prevalence of nutrition labeling and claims on processed, packaged foods

The food label is an important tool for improving the public’s understanding of the health benefits of following a nutritious diet. The Center for Food Safety and Applied Nutrition (CFSAN) of the Food and Drug Administration (FDA) has continued to study food labels with its Food Label and Package Survey (FLAPS). Data from the 2000–2001 FLAPS characterize various aspects of the labeling of processed, packaged foods, including nutrition labeling and various types of label claims. The FDA used a multistage, representative sample of food products from the Information Resources Inc (IRI) 1999 supermarket database as the basis for the FLAPS sample. The final FLAPS database consists of 1,281 foods. An estimated 98.3% of FDA-regulated processed, packaged foods sold annually have nutrition labels, with an additional 1.7% of products exempt from nutrition labeling requirements. Health claims (4.4%), structure/function claims (6.2%), and nutrient content claims (49.7%) were identified on food labels. In addition to the resource this survey provides to CFSAN in assessing health and nutrition information on the food label, registered dietitians and other health professionals can use FLAPS data to assist consumers in choosing a more nutritious diet to improve their health and well-being.

Empirical Analysis Using Scanner Data: Econometric Issues and Policy Implications

Scanner data, data collected at the store or distribution point through electronic means, are now available for food products and offer new opportunities for empirical research. Large retail chains use scanner data to track price and inventory movement and control product movement along their supply chain. In addition, the two major market tracking firms-A.C. Nielsen and Information Resources, Inc. (IRI)-assemble, collect, and provide two types for market analysis: supermarket/store-level data collected from a nationally representative survey of stores, and household panel data collected at the household level (see Cotterill for a review). The three papers make use of both types of data and illustrate new opportunities and challenges faced in using the scanner data. Chung and Kaiser use household panel data to evaluate the issue of aggregation for cross-section analysis in evaluating consumers' response to advertising programs, and whether the heterogeneity of households (captured by the use of scanner data) leads to differences in estimated response to advertising estimated when more aggregated household data are used. Capps and Love explore fundamental aggregation issues related to appropriate demand system model specification by using national (retail market) scanner data. Cotterill and Samson take advantage of the product and brand detail available in market-level data combined

Unobserved Retailer Behavior in Multimarket Data: Joint Spatial Dependence in Market Shares and Promotion Variables

Marketing scholars and practitioners frequently infer market responses from cross-sectional or pooled cross-section by time data. Such cases occur especially when historical data are either absent or are not representative of the current market situation. We argue that inferring market responses using cross-sections of multimarket data may in some cases be misleading because these data also reflect unobserved actions by retailers. For example, because the (opportunity) costs of doing so do not outweigh the gains, retailers are predisposed against promoting small share brands. As a consequence, local prices and promotion variables depend on local market shares—the higher the local share, the higher the local observed promotion intensity. We refer to this reverse causation as an endogeneity. Ignoring it will inflate response estimates, because both the promotion effects on share as well as the reverse effects are in the same direction. In this paper, we propose a solution to this inference problem using the fact that retailers have trade territories consisting of multiple contiguous markets. This implies that the unobserved actions of retailers cause a measurable spatial dependence among the marketing variables. The intuition behind our approach is that by accounting for this spatial dependence, we account for the effects of the retailer's behavior. In this context, our study hopes to make the following contributions at the core of which lies the above intuition. First, we separate the market response effect from the reverse retailer effect by computing responses to price and promotion net of any spatial—and therefore retailer—influence. Second, underlying this approach is a new variance-decomposition model for data with a panel structure. This model allows to test for endogeneity of prices and promotion variables in the cross-sectional dimension of the data. This test aims to complement the one developed by Villas-Boas and Winer (1999), who test for endogeneity along the temporal dimension. Third, to illustrate the approach, we use Information Resources Inc. (IRI) market share data for brands in two mature and relatively undifferentiated product categories across 64 IRI markets. Whereas we only use data with very short time horizons to estimate price and promotion responses with the spatial model, we do have data over long time windows. We use the latter to validate the approach. Specifically, within-market estimates of price and promotion response are not subject to the same endogeneity because we hold the set of retailers constant. Therefore, comparing within- and across-market estimates of price and promotion responses is a natural way to validate the approach. Consistent with our argument, ignoring the reverse causation in the cross-sectional data leads to inferences of price and promotion elasticities that are farther away from zero than the elasticities obtained from within-market analysis. In contrast, cross-sectional spatial estimates and time-series estimates show convergent validity. From a practical point of view, this means it is possible to obtain reasonable within-market estimates of price and promotion elasticities from (predominantly) cross-sectional data. This may benefit marketing managers. The manager who would act on the inflated elasticities will over-allocate marketing resources to promotions because she ignores retailers' censorship of promotions on the basis of already existing high share. We explore other approaches to correct for the inference bias, and discuss further managerial issues and future research.

Mining sales data using a neural network model of market response

Modeling aggregate market response is a core issue in marketing research. In this research, we extend previous forecasting comparative research by comparing the forecasting accuracy of feed-forward neural network models to the premier market modeling technique, Multiplicative Competitive Interaction (MCI) models. Forecasts are compared in two separate studies: (1) the Information Resources Inc. (IRI) coffee dataset from Marion, IN and (2) the A. C. Nielsen catsup dataset from Sioux Falls, SD. Our results suggest neural networks are a useful substitute for MCI models when there are too few observations available to estimate a fully-extended MCI model. Implications are discussed.

Studying patterns of consumer's grocery shopping trip

Previous research on (grocery) shopping trips has focused on studying the relationship between shopping frequency and shopper's demographic characteristics. Recognizing that shoppers are heterogeneous in terms of their shopping trip regularity as well as shopping frequency, we propose a mixture model of the exponential and the Erlang-2 distribution with heterogeneous frequency for shopping trip intervals. Applied to IRI (Information Resources, inc.) shopping trip data, we found that 70 percent of shoppers visit grocery stores with random intervals and 30 percent with relatively fixed intervals. We also found clear differences between these “random” and “routine” shoppers in terms of several demographic and purchase behavioral characteristics. The “routine” shoppers are identified to have higher opportunity costs which make it difficult for them to visit grocery stores more often and/ or to switch stores. However, they spend more dollars for a given shopping trip. Finally, we discuss managerial iniplications of segmenting shoppers by their shopping trip regularity.