Knowledge that Transforms

In spite of the high financial stakes involved in marketing new motion pictures, marketing science models have not been applied to the prerelease market evaluation of motion pictures. The motion picture industry poses some unique challenges. For example, the consumer adoption process for movies is very sensitive to word-of-mouth interactions, which are difficult to measure and predict before the movie has been released. In this article, we undertake the challenge to develop and implement MOVIEMOD—a prerelease market evaluation model for the motion picture industry. MOVIEMOD is designed to generate box-office forecasts and to support marketing decisions for a new movie after the movie has been produced (or when it is available in a rough cut) but before it has been released. Unlike other forecasting models for motion pictures, the calibration of MOVIEMOD does not require any actual sales data. Also, the data collection time for a product with a limited lifetime such as a movie should not take too long. For MOVIEMOD it takes only three hours in a “consumer clinic” to collect the data needed for the prediction of box-office sales and the evaluation of alternative marketing plans. The model is based on a behavioral representation of the consumer adoption process for movies as a macroflow process. The heart of MOVIEMOD is an interactive Markov chain model describing the macro-flow process. According to this model, at any point in time with respect to the movie under study, a consumer can be found in one of the following behavioral states: undecided, considerer, rejecter, positive spreader, negative spreader, and inactive. The progression of consumers through the behavioral states depends on a set of movie-specific factors that are related to the marketing mix, as well as on a set of more general behavioral factors that characterize the movie-going behavior in the population of interest. This interactive Markov chain model allows us to account for word-of-mouth interactions among potential adopters and several types of word-of-mouth spreaders in the population. Marketing variables that influence the transitions among the states are movie theme acceptability, promotion strategy, distribution strategy, and the movie experience. The model is calibrated in a consumer clinic experiment. Respondents fill out a questionnaire with general items related to their movie-going and movie communication behavior, they are exposed to different sets of information stimuli, they are actually shown the movie, and finally, they fill outpostmovie evaluations, including word-of-mouth intentions.These measures are used to estimate the word-of-mouth parameters and other behavioral factors, as well as the movie-specific parameters of the model. MOVIEMOD produces forecasts of the awareness, adoption intention, and cumulative penetration for a new movie within the population of interest for a given base marketing plan. It also provides diagnostic information on the likely impact of alternative marketing plans on the commercial performance of a new movie. We describe two applications of MOVIEMOD: One is a pilot study conducted without studio cooperation in the United States, and the other is a full-fledged implementation conducted with cooperation of the movie's distributor and exhibitor in the Netherlands. The implementations suggest that MOVIEMOD produces reasonably accurate forecasts of box-office performance. More importantly, the model offers the opportunity to simulate the effects of alternative marketing plans. In the Dutch application, the effects of extra advertising, extra magazine articles, extra TV commercials, and higher trailer intensity (compared to the base marketing plan of the distributor) were analyzed. We demonstrate the value of these decision-support capabilities of MOVIEMOD in assisting managers to identify a final plan that resulted in an almost 50% increase in the test movie's revenue performance, compared to the marketing plan initially contemplated. Management implemented this recommended plan, which resulted in box-office sales that were within 5% of the MOVIEMOD prediction. MOVIEMOD was also tested against several benchmark models, and its prediction was better in all cases. An evaluation of MOVIEMOD jointly by the Dutch exhibitor and the distributor showed that both parties were positive about and appreciated its performance as a decision-support tool. In particular, the distributor, who has more stakes in the domestic performance of its movies, showed a great interest in using MOVIEMOD for subsequent evaluations of new movies prior to their release. Based on such evaluations and the initial validation results, MOVIEMOD can fruitfully (and inexpensively) be used to provide researchers and managers with a deeper understanding of the factors that drive audience response to new motion pictures, and it can be instrumental in developing other decision-support systems that can improve the odds of commercial success of new experiential products.

In collaborating to compete, firms forge different types of strategic alliances: same-function alliances, parallel development of new products, and cross-functional alliances. A major challenge in the management of these alliances is how to control the resource commitment of partners to the collaboration. In this research we examine both theoretically and experimentally how the type of an alliance and the prescribed profit-sharing arrangement affect the resource commitments of partners. We model the interaction within an alliance as a noncooperative variable-sum game, in which each firm invests part of its resources to increase the utility of a new product offering. Different types of alliances are modeled by varying how the resources committed by partners in an alliance determine the utility of the jointly-developed new product. We then model the interalliance competition by nesting two independent intra-alliance games in a supergame in which the groups compete for a market. The partners of the winning alliance share the profits in one of two ways: equally or proportionally to their investments. The Nash equilibrium solutions for the resulting games are examined. In the case of same-function alliances, when the market is large the predicted investment patterns under both profit-sharing rules are comparable. Partners developing new products in parallel, unlike the partners in a same-function alliance, commit fewer resources to their alliance. Further, the profit-sharing arrangement matters in such alliances—partners commit more resources when profits are shared proportionally rather than equally. We test the predictions of the model in two laboratory experiments. We find that the aggregate behavior of the subjects is accounted for remarkably well by the equilibrium solution. As predicted, profit-sharing arrangement did not affect the investment pattern of subjects in same-function alliances when they were in the high-reward condition. Subjects developing products in parallel invested less than subjects in same-function alliance, irrespective of the reward condition. We notice that theory seems to underpredict investments in low-reward conditions. Aplausible explanation for this departure from the normative benchmark is that subjects in the low-reward condition were influenced by altruistic regard for their partners. These experiments also clarify the support for the mixed strategy equilibrium: aggregate behavior conforms to the equilibrium solution, though the behavior of individual subjects varies substantially from the norm. Individual-level analysis suggests that subjects employ mixed strategies, but not as fully as the theory demands. This inertia in choice of strategies is consistent with learning trends observed in the investment pattern. A new analysis of Robertson and Gatignon's (1998) field survey data on the conduct of corporate partners in technology alliances is also consistent with our model of samefunction alliances. We extend the model to consider asymmetric distribution of endowments among partners in a same-function alliance. Then we examine the implication of extending the strategy space to include more levels of investment. Finally, we outline an extension of the model to consider cross-functional alliances.

This article describes the implementation of a promotion-event forecasting system, PromoCast™, and its performance in several pilot applications and validity studies. Pilot studies involved retail grocery chains with 95 to 185 stores per trading area. The goal was to provide short-term, tactical forecasts useful for planning promotions from a retailer's perspective. Thus, the forecast system must be able to handle any of the over 150,000 UPCs in each store's item master file, and must be scalable to produce approximately 800,000,000 forecasts per year across all the retailers served by efficient marketing services, inc. (ems, inc.). This is a much different task than one that confronts a manufacturer, even one with a broad product line. Manufacturers can benefit from custom modeling in a product line or category. Retailers need a production system that generates forecasts that help promotion planning. Marketing scientists have typically approached promotion analysis from the manufacturer's perspective. One objective of this article is to encourage marketing scientists to rethink promotion analysis from a different perspective. From the retailer's point of view the “planning unit” is the promotion event. Neither weekly store-tracking data nor shopping-trip data from consumer panels are easily aggregated to reflect total sales during a promotion event. We describe the promotion-event databases and the statistical model developed using these databases. The data are the strategic asset. Our goal is to help retailers use their data to increase the profitability of promotions. We have data on the performance of each UPC in each store under a variety of promotion conditions, on each store's adeptness at executing various styles of promotions, as well as on chain-wide historical performance for each UPC. We use many historical averages from these databases to build a 67-variable, regression-style model. The forecast incorporates a simple bias correction needed when using a log-transformed dependent variable (the natural log of total unit sales). We argue that the historical averages matching the planned ad and display conditions provide a benchmark superior to the widely used “base-times-lift” method. When aggregated into case units (the natural unit for product ordering), 69% of the forecasts in our first validation study were within ± one case compared to 39% within ± one case using the appropriate historical averages. We report the results of two over-time validity studies that reflect the value of our model for retailers. The limitations and implications of this planning tool for managerial decision making concerning stocking levels are discussed. Whenever historical data are the strategic asset we face inherent limitations. Our model does not forecast new products. The forecast error increases when an existing product is promoted in a new way. Over 99.5% of the time, we have full data from which to create a forecast. However, with a database for a typical chain market containing over 20 million promotion events in the 30-month time frame we use, 100,000 events have less than ideal data. The breadth of the database (typically 150,000 UPS) makes it impractical to incorporate data on competitive offerings. We find that regression-style modeling is not adept at incorporating information on the 1,200 subcommodities managed in our pilot stores or the 1,000 manufacturers who supply those stores. Despite these limitations we show the value of using promotion-event data, how tactical forecasts based on these data can directly impact the bottom line of grocery retailers, and how store-by-store forecasts can help retailers with problems of running out of stock or overstocking.

Price promotions are used extensively in marketing for one simple reason—consumers respond. The sales increase for a brand on promotion could be due to consumers accelerating their purchases (i.e., buying earlier than usual and/or buying more than usual) and/or consumers switching their choice from other brands. Purchase acceleration and brand switching relate to the primary demand and secondary demand effects of a promotion. Gupta (1988) captures these effects in a single model and decomposes a brand's total price elasticity into these components. He reports, for the coffee product category, that the main impact of a price promotion is on brand choice (84%), and that there is a smaller impact on purchase incidence (14%) and stockpiling (2%). In other words, the majority of the effect of a promotion is at the secondary level (84%) and there is a relatively small primary demand effect (16%). This paper reports the decomposition of total price elasticity for 173 brands across 13 different product categories. On average, we find that 25% of the elasticity is due to primary demand expansion (i.e., purchase acceleration) and 75% to secondary demand effects or brand switching. Thus, while Gupta's finding that the majority of promotional response stems from brand switching is supported, the average magnitude of the effect appears to be smaller than first thought. More important, there is ample evidence that promotions have a significant primary demand effect. The relative emphasis on purchase acceleration and brand switching varies systematically across categories, and the second goal of the paper is to explain this variation as a function of exogeneous covariates. In doing this, we recognize that promotional response is the consumer's reaction to a price promotion, and therefore develop a framework for understanding variability in promotional response that is based on the consumer's perspective of the benefits from a price promotion. These benefits are posited to be a function of: (i) category-specific factors, (ii) brand-specific factors, and (iii) consumer characteristics. The framework is formalized as a generalized least squares meta-analysis in which the brand's price elasticity is the dependent variable. Several interesting results emerge from this analysis. • Category-specific factors, brand-specific factors, and consumer demographics explain a significant amount of the variance in promotional response for a brand at both the primary and secondary demand levels. • Category-specific factors have greater influence on variability in promotional response and its decomposition than do brand-specific factors. • There are several instances where exogenous variables do not affect total elasticities yet significantly affect individual components of total elasticity. In fact, the lack of a significant relationship between the variables and total elasticity is often due to offsetting effects within two or more of the three behavioral components of elasticity. This is particularly true for brand-specific factors, which typically have no effect on total elasticity, yet have important effects on the individual behaviors. • There is some evidence to suggest that not all promotion-related increases in primary demand are due to forward-buying—in some cases promotions appear to increase consumption. We use these results to illustrate how category- and brand-specific factors work to drive primary and secondary demand elasticities in different directions. In short, this paper offers an empirical generalization of a key finding on promotional response—how elasticities decompose across brand choice, purchase incidence, and stockpiling—and new insights into factors that explain variance in promotional response. These findings are likely to be of interest to researchers who are concerned with theory development and the generalizability of marketing phenomena, and to managers who plan promotion campaigns.

We propose a model that seeks the optimal timing and depth of retail discounts with the optimal timing and quantity of the retailer's order over multiple brands and time periods. The model is based on an integration of consumer decisions in purchase incidence, brand choice and quantity with the dynamics of household and retail inventory. The major contribution of the model is that it shows how the optimum depth and timing of discount varies with key demand characteristics such as consumer stockpiling, loyalty, response to the marketing mix, and segmentation. In addition, the optima also vary with key supply characteristics such as retail margins, depth and frequency of manufacturer deals, retail inventory, and retagging costs. The most valuable contribution of the model is that it can provide an optimal discount strategy for multiple brands over multiple time periods. The optimization model runs on a user-friendly personal computer program. An application based on UPC scanner data illustrates the model's uses. Sensitivity analyses of the optimization model under alternative scenarios reveal novel insights as to how optimal discounts vary as a function of the key demand and supply characteristics.

We consider two broad categories of incentives by which a manufacturer can motivate its retailers to provide high customer satisfaction: (1) manufacturer assistance that reduces the retailer's cost of providing customer satisfaction (CS assistance); and (2) customer satisfaction index (CSI) bonus. We show that if a retailer has a long-term orientation, CS assistance is a more effective coordination mechanism that induces the retailer to expend more effort at customer satisfaction. However, if the retailer has a short-term orientation, CSI bonus is a more effective coordination mechanism. We then show that a long-term oriented retailer is more valuable to a manufacturer than a short-term oriented one. Finally, we show that the use of CS incentives results in greater profits for both the manufacturer and the retailer.

In this paper we review evidence of a generalized convex cross-sectional relationship between retail distribution and unit market share, i.e., large-share brands have more share points per percentage of distribution than small-share brands. The dynamics and structure of distribution and share can help explain many phenomena in marketing, including this convex shape: (1) market share is both a cause and an effect of distribution, and (2) in the typical convenience goods distribution system there are a few large outlets that stock many brands and numerous smaller outlets that stock the leading brands only. Generally, the observed cross-sectional “curve” relating distribution and share will reflect the retailers' stocking decisions, not the incremental effect of distribution on share. However, a logically consistent model of share based on (1) and (2), when combined with the assumption of low search loyalty, results in customers being willing to switch from preferred to available brands. A further consequence is that the marginal effect of weighted distribution on share is likely to be increasing, i.e., result in convex curves relating distribution and share for a given brand. In some cases, and for some measures of distribution, these convex curves have been observed in time-series data for brands that failed and lost distribution over a relatively short period of time. The implication is that marketers should monitor distribution carefully, as it is the result of combined effects of brand preference, loyalty, and “push” programs. With a better understanding of the market share/distribution relationship, managers should be in a better position to forecast marketplace results for a given level of distribution.

Are marketing efforts able to affect long-term trends in sales or other performance measures? Answering this question is essential for the creation of marketing strategies that deliver a sustainable competitive advantage. This paper introduces persistence modeling to derive long-term marketing effectiveness from time-series observations on sales and marketing expenditures. First, we use unit-root tests to determine whether sales are stable or evolving (trending) over time. If they are evolving, we examine how strong this evolution is (univariate persistence) and to what extent it can be related to marketing activity (multivariate persistence). An empirical example on sales and media spending for a chain of home-improvement stores reveals that some, but not all, advertising has strong trend-setting effects on sales. We argue that traditional modeling approaches would not pick up these effects and, therefore, seriously underestimate the long-term effectiveness of advertising. The paper concludes with an agenda for future empirical research on long-run marketing effectiveness.