Alan L. Montgomery

Consumer Shopping and Spending across Retail Formats

We present an empirical study of household shopping and packaged goods spending across retail formats—grocery stores, mass merchandisers, and drug stores. Our study assesses competition between formats and explores how retailers’ assortment, pricing, and promotional policies, as well as household demographics, affect shopping behavior. We find that consumer expenditures respond more to varying levels of assortment (in particular at grocery stores) and promotion than price. We also find that households that shop more at mass merchandisers also shop more in all other formats, suggesting that visits to mass merchandisers do not substitute for trips to the grocery store.

Forecasting the U.S. Unemployment Rate

Abstract This article presents a comparison of forecasting performance for a variety of linear and nonlinear time series models using the U.S. unemployment rate. Our main emphases are on measuring forecasting performance during economic expansions and contractions by exploiting the asymmetric cyclical behavior of unemployment numbers, on building vector models that incorporate initial jobless claims as a leading indicator, and on utilizing additional information provided by the monthly rate for forecasting the quarterly rate. Comparisons are also made with the consensus forecasts from the Survey of Professional Forecasters. In addition, the forecasts of nonlinear models are combined with the consensus forecasts. The results show that significant improvements in forecasting accuracy can be obtained over existing methods.

Applying Quantitative Marketing Techniques to the Internet

Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework for implementing versioning and price-segmentation strategies. Bayesian updating is a natural tool for profiling users with clickstream data. A key challenge for practitioners of Internet marketing is to extract value from the huge volume of data that can be collected. These techniques illustrate how this information can be leveraged to create better decisions.

Designing a Better Shopbot

A primary tool that consumers have for comparative shopping is the shopbot, which is short for shopping robot. These shopbots automatically search a large number of vendors for price and availability. Typically a shopbot searches a predefined set of vendors and reports all results, which can result in time-consuming searches that provide redundant or dominated alternatives. Our research demonstrates analytically how shopbot designs can be improved by developing a utility model of consumer purchasing behavior. This utility model considers the intrinsic value of the product and its attributes, the disutility from waiting, and the cognitive costs associated with evaluating the offers retrieved. We focus on the operational decisions made by the shopbot: which stores to search, how long to wait, and which offers to present to the user. To illustrate our model we calibrate the model to price and response time data collected at online bookstores over a six-month period. Using prior expectations about price and response time, we show how shopbots can substantially increase consumer utility by searching more intelligently and then selectively presenting offers.

Cross-Selling the Right Product to the Right Customer at the Right Time

Firms are challenged to improve the effectiveness of cross-selling campaigns. The authors propose a customer-response model that recognizes the evolvement of customer demand for various products; the possible multifaceted roles of cross-selling solicitations for promotion, advertising, and education; and customer heterogeneous preference for communication channels. They formulate cross-selling campaigns as solutions to a stochastic dynamic programming problem in which the firms goal is to maximize the long-term profit of its existing customers while taking into account the development of customer demand over time and the multistage role of cross-selling promotion. The model yields optimal cross-selling strategies for how to introduce the right product to the right customer at the right time using the right communication channel. Applying the model to panel data with cross-selling solicitations provided by a national bank, the authors demonstrate that households have different preferences and responsiveness to cross-selling solicitations. In addition to generating immediate sales, cross-selling solicitations also help households move faster along the financial continuum (educational role) and build up goodwill (advertising role). A decomposition analysis shows that the educational effect (83%) largely dominates the advertising effect (15%) and instantaneous promotional effect (2%). The cross-selling solicitations resulting from the proposed framework are more customized and dynamic and improve immediate response rate by 56%, long-term response rate by 149%, and long-term profit by 177%.

An Empirical Analysis of the Impact of Pre-Release Movie Piracy on Box Office Revenue

Digital distribution channels raise many new challenges for managers in the media industry. This is particularly true for movie studios where high-value content can be stolen and released through illegitimate digital channels, even prior to the release of the movie in legal channels. In response to this potential threat, movie studios have spent millions of dollars to protect their content from unauthorized distribution throughout the lifecycle of films. They have focused their efforts on the pre-release period under the assumption that pre-release piracy could be particularly harmful for a movies success. However, surprisingly, there has been little rigorous research to analyze whether, and how much, pre-release movie piracy diminishes legitimate sales. In this paper, we analyze this question using data collected from a unique Internet file-sharing site. We find that, on average, pre-release piracy causes a 19.1% decrease in revenue compared to piracy that occurs post-release. Our study contributes to the growing literature on piracy and digital media consumption by presenting evidence of the impact of Internet-based movie piracy on sales and by analyzing pre-release piracy, a setting that is distinct from much of the existing literature.

A system-independent graphical user interface for statistical software

Abstract In this paper we develop an approach for creating a graphical user interface (GUI) for an existing command driven mainframe computer program. The general principle of this approach is to create a GUI which translates users requests into appropriate command syntax for the host computer program. This GUI serves as an intelligent front-end for the command driven program. All input and output to the host computer program are generated and received by this intelligent front-end. Since the front-end and the host computer programs are separated, this GUI program is independent of the host computer and the same GUI program can function with the host computer program under different operating systems. Such an approach does not require any modification to the existing host computer program, which provides significant benefits to software users and developers. In general, this approach can be applied to any existing command driven software. As an example, we present an implementation using the SCA Statistical System. The windows front-end to the SCA System runs on a personal computer using a native windowing environment, Microsoft Windows. The front-end communicates with the SCA System running on a mainframe or workstation through a serial communication device. This implementation demonstrates the advantage of using such an approach.

Reflecting uncertainty about economic theory when estimating consumer demand

Economic theory provides a great deal of information about demand models. Specifically, theory can dictate many relationships that expenditure and price elasticities should fulfill. Unfortunately, analysts cannot be certain whether these relationships will hold exactly. Many analysts perform hypothesis tests to determine if the theory is correct. If the theory is accepted then the relationships are assumed to hold exactly, but if the theory is rejected they are ignored. In this paper we outline a hierarchical Bayesian formulation that allows us to consider the theoretical restrictions as holding stochastically or approximately. Our estimates are shrunk towards those implied by economic theory. This technique can incorporate information that a theory is approximately right, even when exact hypothesis tests would reject the theory and ignore all information from it. We illustrate our model with an application of this data to a store-level system of demand equations using supermarket scanner data.

The Dual Impact of Movie Piracy on Box-Office Revenue: Cannibalization and Promotion

There are two main hypothesized effects from movie piracy: a cannibalization effect which reduces legitimate sales, and a promotional effect which increases word-of-mouth and stimulates sales. While these two effects are commonly discussed, there has been no research to measure their relative impact on motion picture sales. In this paper we use a hidden Markov model adapted from MOVIEMOD to decompose, and separately measure, the cannibalization and promotional impacts of piracy.Using data from all wide release movies in the US from 2006 to 2008 we show that if piracy could be eliminated from the theatrical window then box-office revenues would increase by 15% or

Analyzing Bank Overdraft Fees with Big Data

1.3b per year. An analysis for the time period from 2011 to 2013 shows a similar increase of 14%. Our decomposition of piracy into separate cannibalization and promotional effects shows that the negative effects from piracy due to cannibalization dwarf any positive, promotional benefits: if piracy did not generate promotional effects through word-of-mouth communication then box-office revenues would drop by another 1.5%. We also find that, in rare instances (less than 3% of movies) promotional effects from pre-release piracy could increase revenue compared to piracy that occurs at release. Nonetheless, all of the movies in our counterfactual analysis would experience increased box-office revenue if piracy were eliminated altogether.