Multiple-Category Decision-Making: Review and Synthesis

In many purchase environments, consumers use information from a number of product categories prior to making a decision. These purchase situations create dependencies in choice outcomes across categories. As such, these decision problems cannot be easily modeled using the single-category, single-choice paradigm commonly used by researchers in marketing. We outline a conceptual framework for categorization, and then discuss three types of cross-category dependence: cross-category consideration cross-category learning, and product bundling. We argue that the key to modeling choice dependence across categories is knowledge of the goals driving consumer behavior.     

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.     

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.     

A multi-category customer base analysis

Customer base analysis is an essential tool to measure and develop relationships with customers. While various models have been proposed in a noncontractual setting, they focus primarily on analyzing transactional patterns associated with a single product category or a firm-level activity, such as the times at which purchases are made at a particular retailer. This research proposes a modeling framework for customer base analysis in a multi-category context. Specifically, we model the time between a customer's purchases at the firm and the product categories that comprise her shopping basket arising from multi-category choice decisions. The proposed model uses a latent space approach that parsimoniously captures the dynamics of multi-category shopping behavior due to the interplay between purchase timing and shopping basket composition. We also account for interdependence among multiple categories, temporal dependence across category choices, and latent customer attrition. Using category-level transaction data, we show that the proposed model offers excellent fit and performance in predicting customer purchase patterns across multiple categories. The forecasts and inferences afforded by our model can assist managers in tailoring marketing efforts across categories.     

Chain-wide and store-level analysis for cross-category management

When planning and implementing their price-promotions strategy, retail chain managers face the typical dilemma of “thinking globally, but acting locally.” In other words, they must plan their strategy, keeping in mind the global chain-level impact of their promotions, to deliver on the commitments made to manufacturers. At the same time, managers need to make sure that the implementation of such strategy takes into account the fact that each store caters to a different market with different needs and responses to marketing programs. Moreover, the retail chain manager must consider not only how the promotion of a brand affects competing brands and total category sales, but also how it could affect sales in other categories.Our proposed model addresses these two important aspects of chain-wide and store-level cross-category analysis. First, our proposed factor-regression model takes store differences and longitudinal market shifts into account, thereby providing the retail chain manager with unbiased global, chain-level estimates. It also provides stable local estimates of cross-category promotion effects at the store level. Second, while allowing this flexibility, our proposed model is parsimonious enough over existing alternatives, making it particularly useful for chain-wide and store-level cross-category analysis.We apply the proposed model to store-level data from one retail chain, comparing it with several competing approaches, and demonstrate that it provides the best balance between flexibility and parsimony. Most importantly, we show that the proposed model provides useful insights regarding cross-category effects at the chain-level, for individual stores, and their patterns across stores.     

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.     

Muy local: Differentiating Hispanic and Caucasian shoppers of locally produced foods in US grocery

The current study reviews consumer purchasing of locally produced foods in retail grocery stores across Hispanic and Caucasian groups in the United States. Six hypotheses were tested via the creation and evaluation of a measurement model within the structural equation modeling process. Results suggested that group differences exist between Hispanic and Caucasian consumers across the constructs of attitudes, perceived product availability, subjective norms, intention to purchase and extent of purchase. Implications for marketing strategies across groups and product categories are provided, along with future research directions.     

Measuring Cross-Category Specials Purchasing: Theory, Empirical Results, and Implications

The typical Hi-Lo grocery retailer offers consumers thousands of price specials each week to build traffic and influence economic performance. Retailer reliance on price specials may engender heavy cross-category specials purchasing by shoppers. Retailers contend that high cross-category specials purchasing can damage profitability because many of the specials presented to consumers have reduced gross margins. The present study measures the level of cross-category specials purchasing in a Hi-Lo grocery market and develops and tests a model of the determinants of cross-category specials purchasing using shopping basket level data, information from surveys of shoppers, and retailer promotions. The results of the study show about 39% of all items purchased on a shopping trip were on special and that about 30% of consumers surveyed were highly sensitive to price specials, purchasing more specials than regular priced items on their shopping trip. The findings indicate that the consumer search behaviors, such as reading flyers, significantly affected the level of cross-category specials purchasing as did the demographic variable - household income. The study concludes with a series of practical implications for managers to help them gain profitable shopping baskets and set of implications for researchers interested in developing new insights on cross-category specials purchasing.     

Bridging the intention–behavior gap among organic grocery customers: The crucial role of point‐of‐sale information

Although the interest in organic groceries has increased, actual buying behavior falls short for reasons that are mostly unknown to researchers and practitioners. This paper addresses this so‐called intention–behavior gap by investigating the impact of point‐of‐sale (POS) information on the perception of purchase barriers and behavior. While behavior and interest differ for various product categories, the organic groceries most frequently bought worldwide are differentiated on the basis of product category involvement in a pilot study. A laboratory experiment and a field experiment containing actual purchase behavior and market data revealed the possibility of enhancing organic purchases within low‐ and high‐involvement categories, while exposed to POS information. In low‐involvement product categories, POS information should reveal new product category‐specific organic features. In high‐involvement product categories, the perceived addition of value for money is crucial for purchasing organic groceries. While the effect of POS information on perceived trust and knowledge is higher for health conscious or green consumers in low‐involvement product categories, it is the converse in high‐involvement product categories.     

Positive and Negative Antecedents of Purchasing Eco-friendly Products: A Comparison Between Green and Non-green Consumers

This study aims to analyze what drives and prevents the purchasing of eco-friendly products across different consumer groups and develops a conceptual model embracing the positive altruistic (care for the environmental consequences of purchasing), positive ego-centric (green self-identity and moral obligation), and negative ego-centric (perceived personal inconvenience of purchasing eco-friendly products) antecedents of eco-friendly product purchase intention and behavior. We empirically validate the conceptual model for green (n = 453) and non-green (n = 473) consumers (i.e., consumers who engage in a set of pro-environmental behaviors for environmental reasons versus consumers who do not engage in these behaviors). Data are analyzed using structural equation modeling and multi-group analysis of the two groups. The results confirm the relevance of the determining factors in the model and show significant differences in eco-friendly product purchasing patterns between green and non-green consumers. Altruistic motives are more important for green than for non-green consumers. Negative ego-centric motives affect the purchase intentions of non-green consumers more than the intentions of green consumers, whereas the impact of negative motives on behavior is stronger for green than for non-green consumers. The first contribution of this paper is the development and testing of a parsimonious model of eco-friendly products purchasing that embraces both positive (altruistic and ego-centric) and negative (ego-centric) antecedents, which have been theoretically suggested in the past but have rarely been empirically tested together. The second contribution of this study is that it develops insight into the specific antecedents of eco-friendly products purchasing for green and non-green consumers to assess potential similarities and differences in eco-friendly products purchasing process, the hypothesized antecedents, their impact on eco-friendly products purchase intention and behavior, and the intention–behavior relation.     

The Pareto rule for frequently purchased packaged goods: an empirical generalization

Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. Several papers have provided empirical evidence to explain the Pareto rule, although with limited data. This article provides a comprehensive empirical investigation on the extent to which the Pareto rule holds for mass-produced and distributed brands in the consumer-packaged goods (CPG) industry. We used a rich consumer panel dataset from A.C. Nielsen with 6 years of purchase histories from over 100,000 households. Our analysis utilizes a large number of potential factors such as brand attributes, category attributes, and consumer purchase behavior to explain variation in the Pareto ratio at the brand level across products. Our main conclusion is that the Pareto principle generally holds across a wide variety of CPG categories with the mean Pareto ratio at the brand level across product categories of .73. Several variables related to consumer purchase behavior (e.g., purchase frequency and purchase expenditure) are found to be positively correlated with the Pareto ratio. In addition, niche brands are more likely to have a higher Pareto ratio. Finally, brand/category size, promotion variables, change-of-pace brands, and market competition variables are negatively correlated with the Pareto ratio.     

Counting your customers from an ��always a share�� perspective

Models based on the Pareto/NBD framework are among the most popular for customer base analysis. The Pareto/NBD framework assumes that purchasing follows a Poisson process until the customers defect. Therefore, models based on this framework may systematically underestimate the number of future transactions from customers whose probability of returning is greater than zero. In this paper, we propose a new model which assumes that customers do not defect, but instead switch freely between an active and an inactive state. We call this model the ??interrupted Poisson process??. According to the model, customers purchase through a Poisson process when they are active and they do not purchase when they are inactive. Bayesian simulation methods for parameter estimation are developed and implemented via a Markov chain Monte Cacrlo (MCMC) simulation. Several useful expressions for customer base analysis are derived. Through simulation experiments, we show that the rate of customers moving from an inactive to an active state is an important factor determining the fit and predictive ability of the Pareto/NBD model and our model. An empirical analysis, using two real-life datasets, demonstrates the superior performance of the proposed model.     

Identifying Sales Performance Gaps with Internal Benchmarking

This research investigates how retailers can benefit from identifying the sales growth potential of in-store salespeople in every product category. The proposed novel approach helps retailers develop an equitable evaluation of their sales force, using both observable and unobservable factors that affect sales. By extending stochastic frontier regression to a multivariate case, the resulting factor-analytic frontier formulation leverages the correlation pattern of sales across product categories and salespeople over time. A unique data set, from a franchise chain with 481 salespeople, operating in 35 stores and selling 11 related product categories, identifies a gap between observed category profits and an estimated profit frontier for each salesperson and category. This novel model also can benchmark each salesperson against all others across product categories while accounting for observable and unobservable factors. This approach has practical value, in that retailers can identify both top-performing salespeople and underperformers, who then might be matched to establish a positive learning environment that aligns top performers with proven sales expertise in a product category with peers who struggle with that category.     

Violation behavior in vertical restraint: Empirical analyses in the case of retail price maintenance

An extensive body of work within the marketing and economics literature has been devoted to studying vertical restraints, yet only a few researchers have investigated the violation behavior of retailers. In this paper, we investigate violation behavior in the context of retailer price maintenance. We investigate this behavior using a unique data set from a subsidy program in China, which includes transaction-level information that shows retail price maintenance (RPM) practices in multiple product categories by multiple manufacturers across multiple markets. The results from our fixed effects regression show that retailer violations are more likely to occur when intra-product competition is high. However, how retailer violation likelihood varies with inter-product competition may depend on the product category. We find that inter-product competition, is negatively associated with the likelihood of violation, for “less popular” product categories in the program such as washing machines, air conditioners, etc., but is positively associated with the likelihood of violation for “popular” product categories such as refrigerators, televisions, and cell phones. Our research provides some of the first empirical evidence about retailer violation behavior under RPM in the world’s largest emerging market by focusing on the relationship between violation behavior and market structure. We discuss the implications for monitoring efforts of manufacturers and regulators.     

A cross-category investigation into the effects of nine-ending pricing on brand choice

Retailers largely adopt nine-ending prices and these prices have attracted greater attention from researchers in marketing. Despite this increased interest, very few empirical studies have tried to quantify the effects of nine-ending prices on consumer actual behaviors. Those who have studied the behavioral effects of nine-ending prices have produced mixed findings. In this article, we investigated the cross-category effects of nine-ending pricing on consumer brand choice at the SKU level. We distinguished between different types of nine-ending while controlling for the rounded prices and other marketing-mix variables. We conducted our analysis on over 11,000 SKUs in 102 product categories of two (2) grocery retailers. We find that the effects of 99 ending prices on the SKU's category choice are larger in concentrated and promotional categories but smaller in expensive categories. However, their influence on purchase quantity is larger in expensive categories but smaller in concentrated categories.     

Cross-category sales promotion effects

We introduce a multivariate binomial logit model measuring cross-category dependence and sales promotion effects of a retail assortment. This model requires as data both the market baskets of individual shoppers and the categories currently promoted in a retail outlet. A special section describes the stepwise procedure used to estimate parameters of this model. Its application is demonstrated analyzing 6147 purchases that were acquired in a medium-sized supermarket. We finally discuss the managerial relevance of this model for sales promotion decisions of retail firms.     

Consumer heterogeneity in the longer-term effects of price promotions

While a significant literature has emerged recently on the longer-term effects of price promotions, as inferred from persistence models, there is very little if any attention paid to whether such longer-term effects vary across different types of consumers. This paper takes a first step in that direction by exploring whether the adjustment, permanent, and total effects of price promotions, and the duration of the adjustment period, differ between consumers segmented based on their usage rates in a product category and their loyalty to a brand. We also investigate whether such consumer segmentation will improve the forecasting performance of persistence models at both product category and brand levels. Expectations are developed based on consumer behavior theory on various effects of price promotions, such as the post-deal trough, the mere purchase effect, the promotion usage effect, and responsiveness to competitor's reactions. Evidence from household-level supermarket scanner data on four product categories is provided. We find substantial differences between consumer segments and provide insights on how managers can increase the longer-term effectiveness of price promotions by targeting each consumer segment with a different promotion program. In addition, consumer segmentation is found to significantly improve the forecasting performance of the persistence model for two of the four product categories. For the other two product categories, consumer segmentation provides forecasting performance similar to that obtained from aggregate-level persistence models.     

An Exploration of Consumer Attitudes and Purchasing Patterns in Fair Trade Coffee and Tea

This study aims to understand the consistency between attitudes toward ethical brands and consistency with the respondents’ recent purchase behavior. A study of 202 respondents was carried out in order to observe consistency between claimed ethical attitudes and self-reported purchase behavior across three product categories. Panel data (n = 8000) was utilized to provide a large sample on which a duplication of purchase analysis could be conducted. Results suggest many consumers claim to have ethical attitudes, but their reported purchases suggest their behavior is not consistent. Consumers who purchase fair trade brands are just as likely to purchase other, non–fair trade brands.     

The role of ethical problems related to a brand in the purchasing decision process: An analysis of the moderating effect of complexity of purchase and mediation of perceived social risk

The objective of this article is to identify the effect of the perception of a brand's ethical problems on consumer behavior. The research contemplates two experimental studies, in which different brands were used in different product categories. Based on the results of the experiments, we verified the moderating effect of the perceived purchase complexity and the mediating effect of perceived social risk in the relationship between the perception of ethical problems related to a brand and the declared purchase intention, both by university students and by real consumers. Through the analysis of means differences and conditional models analyzed with PROCESS (Hayes, 2012), we identified that the perception of ethical problems related to a brand affects consumer confidence and, in some cases, the perception of product quality. In addition, we find that for products with low purchasing complexity, the effect of ethical problems is mitigated both in relation to the purchase intention and in the formation of the perception of social risk linked to consumption.