Peter Boatwright

Reducing Assortment: An Attribute-Based Approach

Most supermarket categories are cluttered with items, or stockkeeping units (SKUs), that differ very little at the attribute level. Previous research has found that reductions (up to 54%) in the number of low-selling SKUs need not affect perceptions of variety and therefore sales, significantly. In this research, the authors analyze data from a natural experiment conducted by an online grocer, in which 94% of the categories experienced dramatic cuts in the number of SKUs offered, particularly low-selling SKUs. Sales were indeed affected dramatically, increasing an average of 11% across the 42 categories examined. Sales rose in more than two-thirds of these categories, nearly half of which experienced an increase of 10% or more; 75% of households increased their overall expenditures after the cut in SKUs. In turn, the authors examine how different types of SKU reductions—defined by how the cuts affect the available attributes or features of a category (e.g., the number of brands)—affected purchase behavior differently. The results indicate that consumers experienced divergent reactions to the reduction in sizes, but they uniformly welcomed the elimination of clutter brought on by the reduction in redundant items. In addition, of households that were loyal to a single brand, size, or brand–size combination that was eliminated, nearly half continued purchasing within the category. Also, contrary to previous research on SKU reductions, the authors find that category sales depend on the total number of SKUs offered. The authors extend the previous research by showing that (1) category sales depend on the availability of key product and category attributes and (2) two particularly important attributes to consumers in an assortment are brand and flavor.

Incidental Prices and Their Effect on Willingness to Pay

Previous research has explored how both internal and external references prices affect consumer perceptions and consequently the price that consumers are willing to pay for a product or service. Historically researchers have examined the effects of exposure to prices for the same product, the same brand, or products in the same category. This research explores the effect of incidental prices on the consumers willingness to pay. The authors define incidental prices as prices advertised, offered, or paid for unrelated products or goods that neither sellers nor buyers regard as relevant to the price of an item that they are engaged in selling or buying. More specifically, the authors examine how prices for products that buyers encounter unintentionally can serve as anchors, thus affecting willingness to pay for the product that they intend to buy. The findings have important implications for auction houses and online vendors as well as for conventional retailers.

Account-Level Modeling for Trade Promotion: An Application of a Constrained Parameter Hierarchical Model

Abstract We consider the problem of utilizing data at the retail/market level on sales and marketing mix variables to help manufacturers optimize the allocation of trade promotional budgets across areas. Major consumer packaged goods manufacturers budget at least one-half of their total marketing expenses to trade promotions. Trade promotional deals are designed to encourage retailers to promote products by temporarily reducing the price, putting them in in-store displays, or advertising in local media. A profit-based trade promotional allocation system will require estimates of the responsiveness of sales at each retailer to a given promotion. A major barrier to the use of retailer data is the proliferation of incorrectly signed coefficients in standard least squares analyses. Even more sophisticated adaptive shrinkage methods will not remove the problem of improper signs. We propose a hierarchical model to modeling retailer response that uses a first-stage prior with inequality constraints on the regres...

A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music

In a situation where several hundred new music albums are released each month, producing sales forecasts in a reliable and consistent manner is a rather difficult and cumbersome task. The purpose of this study is to obtain sales forecasts for a new album before it is introduced. We develop a hierarchical Bayesian model based on a logistic diffusion process. It allows for the generalization of various adoption patterns out of discrete data and can be applied in a situation where the eventual number of adopters is unknown. Using sales of previous albums along with information known prior to the launch of a new album, the model constructs informed priors, yielding prelaunch sales forecasts, which are out-of-sample predictions. In the context of new product forecasting before introduction, the information we have is limited to the relevant background characteristics of a new album. Knowing only the general attributes of a new album, the meta-analytic approach proposed here provides an informed prior on the dynamics of duration, the effects of marketing variables, and the unknown market potential. As new data become available, weekly sales forecasts and market size (number of eventual adopters) are revised and updated. We illustrate our approach using weekly sales data of albums that appeared inBillboards Top 200 albums chart from January 1994 to December 1995.

Conjugate Analysis of the Conway-Maxwell-Poisson Distribution

This article explores a Bayesian analysis of a generalization of the Poisson distribution. By choice of a second parameter , both under-dispersed and over-dispersed data can be modeled. The Conway-Maxwell-Poisson distribu- tion forms an exponential family of distributions, so it has sucien t statistics of xed dimension as the sample size varies, and a conjugate family of prior distribu- tions. The article displays and proves a necessary and sucien t condition on the hyperparameters of the conjugate family for the prior to be proper, and it discusses methods of sampling from the conjugate distribution. An elicitation program to nd the hyperparameters from the predictive distribution is also discussed.

A Model of the Joint Distribution of Purchase Quantity and Timing

Prediction of purchase timing and quantity decisions of a household is an important element for success of any retailer. This is especially so for an online retailer, as the traditional brick-and-mortar retailer would be more concerned with total sales. A number of statistical models have been developed in the marketing literature to aid traditional retailers in predicting sales and analyzing the impact of various marketing activities on sales. However, there are two important differences between traditional retail outlets and the increasingly important online retail/delivery companies, differences that prevent these firms from using models developed for the traditional retailers: (1) the profits of the online retailer/delivery company depend on purchase frequency and on purchase quantity, whereas the profits of traditional retailers are simply tied to total sales, and (2) customers in the tails of the frequency distribution are more important to the delivery company than to the retail outlet. Both of these differences are due to the fact that the delivery companies incur a delivery cost for each sale, whereas customers themselves travel to retail outlets when buying from traditional retailers. These differences in costs translate directly into needs that a model must address. For a model intended to be useful to online retailers, the dependent variable should be a bivariate distribution of frequency and quantity, and the frequency distribution must accurately represent consumers in the tails. In this article we develop such a model and apply it to predicting the consumers joint decision of when to shop and how much to spend at the store. Our approach is to model the marginal distribution of purchase timing and the distribution of purchase quantity co nditional on purchase timing. We propose a hierarchical Bayes model that disentangles the weekly and daily components of the purchase timing. The daily component has a dependence on the weekly component, thereby accounting for strong observed periodicity in the data. For the purchase times, we use the Conway-Maxwell-Poisson distribution, which we find useful to fit data in the tail regions (extremely frequent and infrequent purchasers).

A Mixture Model for Internet Search-Engine Visits

The authors extend the marketing literature on stochastic interpurchase-time models by allowing for purchase periodicities and unobserved heterogeneity in a proportional hazards mixture model. Their parsimonious framework builds on commonly used baseline hazard functions. They use the search-engine visits data to highlight the benefits of the proposed model.

The Role of Retail Competition, Demographics and Account Retail Strategy as Drivers of Promotional Sensitivity

We study the determinants of sensitivity to the promotional activities of temporary price reductions, displays, and feature advertisements. Both the theoretical and empirical literatures on price promotions suggest that retailer competition and the demographic composition of the shopping population should be linked to response to temporary price cuts. However, datasets that span different market areas have not been used to study the role of retail competition in determining price sensitivity. Moreover, little is known about the determinants of display and feature response. Very little attention has been focused on retailer strategic decisions such as price format (EDLP vs. Hi-Lo) or size of stores. We assemble a unique dataset with all U.S. markets and all major retail grocery chains represented in order to investigate the role of retail competition, account retail strategy, and demographics in determining promotional response. Previous work has not simultaneously modeled response to price, display, and feature promotions, which we do in a Bayesian Hierarchical model. We also allow for retailers in the same market to have correlated sales response equations through a variance component specification. Our results indicate that retail strategic variables such as price format are the most important determinants of promotional response, followed by demographic variables. Surprisingly, we find that variables measuring the extent of retail competition are not important in explaining promotional response.

A Satisficing Choice Model

Although the assumption of utility-maximizing consumers has been challenged for decades, empirical applications of alternative choice rules are still very new. We add to this growing body of literature by proposing a model based on the idea of a “satisficing” decision maker. In contrast to previous models (including recent models implementing alternative choice rules), satisficing depends on the order in which alternatives are evaluated. We therefore conduct a visual conjoint experiment to collect search and choice data. We model search and product evaluation jointly and allow for interdependence between them. The choice rule incorporates a conjunctive rule for the evaluations and, contrary to most previous models, does not rely on compensatory trade-offs at all. The results strongly support the proposed model. For instance, we find that search is indeed influenced by product evaluations. More importantly, the model results strongly support the satisficing stopping rule. Finally, we perform a holdout prediction task and find that the proposed model outperforms a standard multinomial logit model.

A step‐by‐step process to build valued brands

Purpose – The primary purpose of this study is to illustrate an analytical method to identify tangible and intangible customer values and to translate those values into brand identity differentiators and product specifications.Design/methodology/approach – The authors adapt a product development analysis tool, the Value Opportunity Analysis (VOA), to the design of a brand identity, illustrating the use of the tool in a case study with International Truck and Engine.Findings – The paper illustrates how the VOA was used as a tool for evaluating and crafting both a brand identity, and shows how the brand identity translates into product specifications so that products embody, communicate, and deliver the brand identity.Research limitations/implications – Although the VOA has been used in diverse markets (business to business, consumer software, physical product), in this article the VOA is illustrated in the context of brand identity for physical products. Future studies should illustrate how the application...