Save or (over-)spend? The impact of hard-discounter shopping on consumers' grocery outlay

An increasing number of consumers have come to patronize a hard discounter (HD) to save on their grocery budget. Given the HDs' rock-bottom prices, a complete switch from the traditional supermarket (TS) to the HD format would, indeed, substantially reduce grocery spending. However, consumers typically visit the HD on top of a TS, leading to a more complex purchase allocation and decision process. In addition to limiting the potential savings to part of the basket, these multiple store shopping patterns may therefore result in depletion of self-control resources and to self-licensing, consumers using realized savings to justify additional indulgent purchases. In this study, we explain and empirically analyze the effect of HD patronage on consumer spending, taking the selected shopping pattern (single versus multiple stores, visited on separate versus combined shopping trips) into account. To this end, we use scanner panel data on households' actual weekly purchase behavior. We also examine household differences in these effects, and relate them to household characteristics.Our results show that shifting the entire basket to the HD entails substantial savings. Yet, for the majority of consumers, adding a HD to the store set does not reduce grocery outlay: visiting the HD and TS on separate shopping trips leads to a “status quo”, and visiting them on combined shopping trips even enhances weekly spending. While consumers pay less per unit bought at the HD, this is (more than) offset by the purchase of larger quantities across a broad range of product categories, and of more expensive items at the TS. Especially older consumers or larger households, and consumers who shop more frequently or are easily enticed by promotions, are bound to overspend.     

Does Online Information Drive Offline Revenues?: Only for Specific Products and Consumer Segments!

While many offline retailers have developed informational websites that offer information on products and prices, the key question for such informational websites is whether they can increase revenues via web-to-store shopping. The current paper draws on the information search literature to specify and test hypotheses regarding the offline revenue impact of adding an informational website. Explicitly considering marketing efforts, a latent class model distinguishes consumer segments with different short-term revenue effects, while a Vector Autoregressive model on these segments reveals different long-term marketing response.We find that the offline revenue impact of the informational website critically depends on the product category and customer segment. The lower online search costs are especially beneficial for sensory products and for customers distant from the store. Moreover, offline revenues increase most for customers with high web visit frequency. We find that customers in some segments buy more and more expensive products, suggesting that online search and offline purchases are complements. In contrast, customers in a particular segment reduce their shopping trips, suggesting their online activities partially substitute for experiential shopping in the physical store. Hence, offline retailers should use specific online activities to target specific product categories and customer segments.     

Modeling Dependencies in Brand Choice Outcomes Across Complementary Categories

We build an econometric model of a household's contemporaneous brand choice outcomes in complementary product categories. This model explicitly captures cross-category dependencies in brand choice outcomes of a household. Such dependencies have not been modeled in existing multi-category demand models.Our model accommodates cross-category dependencies that arise on account of three component effects: (1) complementarity due to the additional utility that a household derives from the joint purchase of brands in complementary categories, (2) marketing spillovers due to the effects of brands’ prices in one category affecting the households’ latent utilities for brands in the complementary category, (3) unobserved dependencies due to correlations in households’ latent utilities for brands across categories.We estimate our proposed multi-category brand choice model using scanner panel data on cake mix and frosting categories. We find that complementarity accounts for the vast majority of the estimated cross-category effects in demand. We also find that as much as 55 percent of the total retail profit impact of price promotions arise on account of brand-level (focus of our study), as opposed to category-level (focus of previous studies), dependencies in household demand. Finally, we propose an easily interpretable visual representation – Largess and Free-Ride Plot – of cross-category price elasticities that summarizes the differential abilities of brands to influence, or be influenced by, brands in the complementary category.     

Maximizing Profits for a Multi-Category Catalog Retailer

It is a common trend in the retail industry for catalog retailers to mail multiple catalogs, each promoting different product categories. The existing catalog mailing models do not address the issue of optimizing multi-category catalog mailing. We address this research gap by introducing a model that integrates the when and what components of a customer's purchase decision into the how much component (number of catalogs) of a firm's cross-selling strategy. In addition to comparing the impact of category-specific versus full product catalogs in generating sales in a specific category, the study also finds relative impacts of various category-specific catalogs. We jointly estimate the probability of purchase and purchase amounts in multiple product categories by using multivariate proportional hazard model (MVPHM) and a regression based purchase amount model in a Hierarchical Bayesian framework. The model accounts for unobserved heterogeneity, and uses a control function (CF) approach to account for endogeneity in catalog mailing. The results from the Genetic Algorithm (GA) based optimization suggest that the catalog mailing policy as per the proposed model would be able to generate 38.4 percent more customer lifetime value (CLV) from a sample of 10 percent of the households as compared to the current catalog mailing policy of the retailer by reallocation of the catalogs across customers and mailing periods based on their propensity to buy.     

A Bayesian multivariate Poisson regression model of cross-category store brand purchasing behavior

The availability of cross-category transaction data in the retailing industry has enabled the investigation of interdependence in consumer purchase behavior across product categories. In this paper, we develop a multivariate count model to uncover and predict the pattern of cross-category store brand purchasing behavior. The proposed multivariate Poisson regression model, which we estimate using a Bayesian approach, provides flexibility in capturing cross-category correlations for sparse multivariate purchase data associated with infrequently purchased categories or purchasing in retail outlets such as warehouse clubs. We compare the goodness-of-fit of the proposed Poisson regression model with alternate benchmark models using customer purchase records across five product categories from a national warehouse club and find that the proposed model provides a superior fit. We also carry out a profitability analysis to illustrate the use of the model in planning cross-promotions.     

Omnichannel-based promotions’ effects on purchase behavior and brand image

This paper examines the effects of utilizing consumers’ digital shopping traces when designing in-store promotions on purchase behavior and brand image. In two experimental studies with 526 and 550 espondents, the authors examine the effects of omnichannel-based promotions (e.g. using digital shopping trace to offer a promotion when the consumer enters the physical store) in two different product categories (utilitarian vs. hedonic), spontaneous/planned purchases and two different retail industries (durable good vs. travel). The results show that retailers benefit from using digital shopping traces as it increases purchases and enhances brand imagery. The effects are moderated by product category and type of purchase.     

The relationship between purchase regularity and propensity to accelerate

Identifying random and regular-buying household segments and relating them to demographic and shopping characteristics has been the focus of many marketing studies. Missing from the marketing literature, however, is a study that relates purchase regularity to marketing mix sensitivities. Such study could provide substantive implications since it would explore a practical dimension of a segmentation scheme based on purchase regularity. In this article, we investigate the relationship between purchase regularity and propensity to accelerate through the use of a mixture Weibull model of purchase timing. Applying this perspective to purchase timing data on four product categories (ketchup, sugar, bathroom tissue, margarine), we show that in the frequently purchased categories of bathroom tissue and margarine, random buyers do not exhibit any propensity to accelerate while regular buyers do. In the occasionally purchased categories of ketchup and sugar on the other hand, random buyers exhibit at least as much propensity to accelerate their purchases as regular buyers do. Our rationale for these results is based on information-theoretic arguments suggesting that propensity to accelerate depends on the frequency at which a product category is purchased.     

Leveraging Information Across Categories

Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers' behavior in a variety of situations and product categories. For example, information about a customer's purchase behavior in one category can be helpful in predicting his potential behavior in a related category, which in turn could help a firm in its cross-selling efforts. In this paper, we present a model to better understand and predict a consumer's purchases and preferences when we may have limited or no information about him in one or more product categories. Conceptually this involves leveraging information from purchases of other consumers in multiple categories as well as partial information (e.g., purchase in one of the categories) of the target consumer. Our approach builds on the pioneering work of (Rossi et al. (1996)) who demonstrate the value of purchase information in the context of a single product category. We present results from an extensive simulation as well as an application on scanner panel data. Our simulation shows many interesting and somewhat surprising results. Specifically, we find that compared to a single-category analysis, a cross-category analysis does not lead to any significant improvement in data likelihood in most cases. Therefore, the single-category analysis of (Rossi et al. (1996)) is even more powerful than previously thought. However, we also find that a cross-category analysis does improve parameter recovery in many situations as compared to a single-category analysis. It is in these conditions that retailers can use cross-category information to better implement micro marketing programs. We demonstrate the transfer of information across categories in an application of two grocery products—Breakfast Foods and Table Syrup. In spite of a reasonable correlation (0.21) in the price parameter across these two categories, our simulation guidelines predict very little benefit of cross-category analysis over single-category analysis. Our empirical results confirm this prediction.     

Buying Groceries in Brick and Click Stores: Category Allocation Decisions and the Moderating Effect of Online Buying Experience

The large majority of online grocery shoppers are multichannel shoppers who keep visiting offline grocery stores to combine convenience advantages of online shopping with self-service advantages of offline stores. An important retail management question, therefore, is how these consumers divide grocery purchases across the retailer's online and offline channel. We provide a comprehensive analysis of the impact of category characteristics on the allocation pattern of multichannel grocery shoppers and find that category allocation decisions are affected not only by marketing mix differences between the online and offline channel, but also by intrinsic category characteristics like perceived purchase risk and shopping convenience. In addition, we examine the effect of online buying experience. In line with expectations, we find that it can affect allocation patterns in different ways: (i) it attenuates the perceived risk of buying sensory categories online, thereby reducing differences in online category share, (ii) it reinforces marketing mix (assortment) effects, thereby making online category share differences more pronounced, and (iii) it has no effect for factors such as promotions that are easy to evaluate without experience, thereby leaving the online category share stable. In addition to different experience effects across allocation factors, we also observe variations in experience effects across consumer segments.     

Basket Composition and Choice Among Direct Channels: A Latent State Model of Shopping Costs

Shoppers of multi-channel retailers often place orders using different channels on different shopping occasions. The differential use of channels is related to both basket composition and channel characteristics, such as the ability of the channel to provide additional information that resolves uncertainty about the purchase. In this paper, we examine the impact of basket composition on the choice among direct channels. We develop a two-stage, shopping cost model with two, latent states. Given a shopping basket, the shopper first decides if she needs additional information about items in the basket. If she is uncertain about the items in the basket meeting her needs, she uses an information rich channel, such as the retailer's website or call center, and risk reduction costs become salient in addition to the other shopping costs. If she does not require additional information, she places her order by choosing among all available channels, and she may incur a welfare loss from making a purchase that does not optimally meet her needs. We operationalize welfare loss with Shannon information and various metrics based on purchase history.Our empirical setting is a data set from a catalog retailer that offers multiple direct channels. Our estimates show that basket composition impacts channel choice. Large baskets shift to the Internet channel, suggesting that the Internet channel has lower ordering costs. High-risk baskets shift to call centers and this suggests that the call center has lower risk reduction costs. Collectively these estimates provide evidence for the notion of channel specialization—some channels are better at addressing certain shopping costs compared to others. Our estimates also show that electronic self-service channels have high initial access costs and a significant learning curve compared to the call center suggesting that these channels might be better suited to heavy users. We use the estimated model to quantify the value of channels, to identify categories that need risk reduction, and to segment and target shoppers for Internet ordering based on basket size and the potential to accumulate experience.     

Online Purchase Paths and Conversion Dynamics across Multiple Websites

Low transportation costs online allow shoppers to visit multiple e-commerce sites for a purchase decision. This research investigates online shoppers’ visit and purchase behaviors across competing websites. To consider that shoppers’ longitudinal cross-site visit data may consist of several unobserved shopping episodes, we propose a modeling approach to probabilistically clustering and relating online visits to latent shopping episodes, based on the temporal patterns of the visit events. The inferences are then used to examine shoppers’ visit-to-purchase behavior across websites. Using Internet clickstream data on individual-level browsing and transaction records at major air travel sites, we find that online shoppers’ cross-site visit patterns tend to be clustered and the purchase propensity is significantly higher at later visits within a visit cluster, compared to earlier visits. As our results suggest the possibility that visit clusters can serve as a reasonable proxy for shopping episodes, we look further into shoppers’ website choice and purchase behaviors within a cluster. We discuss how the cluster-based analysis can help managers tailor online marketing and advertising strategies based on shoppers’ cross-site visit and purchase patterns.     

The Relationship Between Customer Loyalty and Purchase Incidence

Little is known about customer loyalty to brands over many dozens or even hundreds of purchases. In this paper we describe, and seek to explain, such patterns of loyalty in two very different markets: a consumer market (laundry detergents), and a more frequently used service (physicians' prescribing of anti-hypertension drugs). Purchase incidence heterogeneity – a problem in most loyalty studies – is addressed by measuring loyalty at different rates of category purchase (rather than over time). Using share-based measures we expect that loyalty will decline as purchase incidence increases, however we clarify the shape of that decline. We find that, as the number of purchases rises, loyalty initially falls steeply, but after around 15 purchases it starts to stabilize, and from 60 to 200 purchases there is very little change in observed measures of customer loyalty. A comparison of the findings with those expected from a stationary market model (the Dirichlet), suggests that the decline in loyalty seen as the number of purchases rises is largely a statistical artifact, dependent on the number of purchases used to calculate loyalty. However, we also find that the higher loyalty exhibited by heavier buyers at low purchase levels is not captured well by the model. The implication here is that, contrary to a central assumption of the Dirichlet model, brand choice is partially dependent on purchase weight.     

It is different than what I saw online: Negative effects of webrooming on purchase intentions

Webrooming is a two-stage shopping process that begins with examining product options online followed by making a purchase at an offline store. In four experiments, we investigate webrooming effects on product evaluation and purchase intentions. The results suggest that webrooming (vs. non-webrooming) has negative impacts on (1) perceived product performance vis-à-vis expectations and (2) purchase intentions for the products offline. Our moderated-mediation analyses show that webrooming leads to lower perceived product performance, which in turn results in lower purchase intentions, and participants’ Need for Touch (NFT) moderates the negative mediation effect, which is stronger with instrumental NFT than autotelic NFT. However, this moderated-mediation effect is attenuated when products are searched across multiple categories. These findings contribute to the marketing literature by providing a more nuanced understanding of how two-stage, webrooming behavior affects consumers’ cognitions and purchase decisions. They also provide several managerial implications that when controlling for time intervals between the stages, (1) webrooming may adversely affect retailers’ business outcomes when webrooming within a single (e.g., blankets), related (e.g., baby products), and unrelated product categories; (2) thus, creating an integrated online-to-offline cross-channel customer experiences is critical to minimize the negative webrooming effects on final sales.     

Share of wallet in retailing: the effects of customer satisfaction, loyalty cards and shopper characteristics

In retailing, consumers typically patronize multiple outlets. Thus, an important issue is why consumers vary in how they divide their purchases across outlets and how outlets can get a greater share of consumer expenditures. Two potential avenues for increasing customer share are to raise customer satisfaction, and increase repeat purchase through loyalty cards. This study examines the effects of customer satisfaction and loyalty cards as well as consumer characteristics on customer share spent on the primary grocery store. The findings suggest that customer satisfaction has a positive, albeit modest, effect on share while consumer economic shopping orientation has a negative direct effect on share. The economic orientation of shoppers, their felt importance of a personal relationship with store personnel, and the level of their aggregate purchase volume moderate the effect of satisfaction on customer share. Finally, the results provide mixed support for the impact of loyalty cards on customer behavior.     

The impact of the introduction and use of an informational website on offline customer buying behavior

Do customers increase or decrease their spending in response to the introduction of an informational website? To answer this question, this study considers the effects of the introduction and use of an informational website by a large national retailer on offline customer buying behavior. More specifically, we study a website's effects on the number of shopping trips and the amount spent per category per shopping trip. The model is calibrated through the estimation of a Poisson model (shopping trips) and a type-II tobit model (the amount spent per category per shopping trip), with effect parameters that vary across customers. For the focal retailer, an informational website creates more bad than good news; most website visitors engage in fewer shopping trips and spend less in all product categories. The authors also compare the characteristics of shoppers who exhibit negative website effects with those few shoppers who show positive effects and thus derive key implications for research and practice.     

Cross-buying evaluations in the retail banking industry

Is a customer’s past purchase experience of traditional banking products applicable to the continuing purchase of insurance and investment products at a bank branch? Are service attributes used with similar extensions evaluated differently from when used with dissimilar extensions? In response to these questions, this study develops and examines a framework of service attributes (e.g., locational and one-stop shopping convenience, functional and technical service quality, and firm reputation and size) having positive effects on cross-buying. Meanwhile, this study also examines the mediating roles of satisfaction and trust on the relationship between services attributes and cross-buying. Our results indicate that the relative importance of locational convenience and functional service quality is likely to decline, while the relative importance of one-stop shopping convenience and firm size is likely to increase as category dissimilarity increases. Technical service quality and firm reputation only have indirect effects on cross-buying dissimilar product categories through trust. Instead, satisfaction plays the mediating role for cross-buying similar product categories. Our findings reinforce the view that the relative effects of service attributes, satisfaction and trust on cross-buying vary under different category similarity conditions.     

Retail Market Power in a Shopping Basket Model of Supermarket Competition

Supermarket retailers typically operate with relatively low margins, suggesting a highly competitive retail environment. However, despite the fact that consumers purchase an entire shopping basket at a time from supermarkets, this evidence is largely based on models of retail competition with single-category purchases. In this paper, we develop and test an empirical model of retail price competition that explicitly accounts for the effect of demand complementarity among items in consumer shopping baskets. Relative to the case where consumers purchase products with independent demands, we demonstrate that equilibrium prices are higher for all items when retailers take demand-complementarity into account. Our findings indicate that non-price strategies intended to encourage complementarity, such as co-merchandising, strategic shelf-positioning, or featuring complementary goods tend to soften price competition, and lead to higher equilibrium prices.     

Managing retailer profitability—one customer at a time!

This study examines how customer lifetime value (CLV) can be computed at individual customer level in a retail setting to maximize profitability. The study finds that maximum positive impact to CLV occurs when the customer cross-purchases, shows multi-channel shopping behavior, stays longer with the firm, buys specific product categories and purchases more frequently with the firm. Interestingly, the CLV follows an inverted U relationship with increase in return of prior purchases. Other interesting findings include a surprisingly low correlation between customer loyalty and future profitability and low correlation between stores’ historic revenues and future profitability. Several implications are suggested for retailers to manage and maximize customer profitability as well as store profitability.     

The impact of search costs on consumer behavior: A dynamic approach

Prices for grocery items differ across stores and time because of promotion periods. Consumers therefore have an incentive to search for the lowest prices. However, when a product is purchased infrequently, the effort to check the price every shopping trip might outweigh the benefit of spending less. I propose a structural model for storable goods that takes into account inventory holdings and search. The model is estimated using data on laundry detergent purchases. I find search costs play a large role in explaining purchase behavior, with consumers unaware of the price of detergent on 70 % of their shopping trips. Therefore, from the retailer’s point of view raising awareness of a promotion through advertising and displays is important. I also find a promotion for a particular product increases the consumer’s incentive to search. This change in incentives leads to an increase in category traffic, which from the store manager’s perspective is a desirable side effect of the promotion.     

Consumer product search and purchase behaviour using various retail channels: the role of perceived retail usefulness

The purpose of this study was to examine the influences of consumers' perceptions of retail usefulness for product information search and their previous purchase satisfaction on their frequencies of product information search and product purchase behaviours for apparel products. These relationships were investigated in five retail settings – Internet shopping, catalogue shopping, television shopping, local retail shopping, and non‐local retail shopping. One hundred seventy‐six students in a US Midwestern university provided usable responses. The results of causal model analyses showed that the proposed model fits the data well for all five retail channels. Consumers who perceived a certain retail channel more useful for product information search searched for product information more frequently via that retail channel, and purchased products more often via that retail channel. Consumers who were more satisfied with apparel purchases from a retail channel purchased the products more frequently via that retail channel. Theoretical and managerial implications are discussed.