Marketing Science Models Research Articles

Modeling and characterizing viewers of You Tube videos

All the viewers of an online video do not watch a video at the same time. Consequently, on the basis of the behavioral measure of an individual who is moderately watching the videos earlier than others, viewers have been classified into viewer categories. Viewers’ categorization is much needed and has to be developed as viewers can lend a hand in targeting prospects for an online video and predict the continued sharing of the video. As per internet market literature, understanding and predicting view counts has not only resulted in generation of more traffic but has also acted as popularity metric for videos. Thereby, in the present work, based on analogy with a marketing science model we have studied the behavioral hypothesis for the modeling and quantification is offered in terms of varied and yet connected classes of viewers. With view count data on four YouTube videos, we have examined the diffusion of these videos over the time and illustrated the usefulness of the viewer categorization.

Practice Prize Paper—Implementing Integrated Marketing Science Modeling at a Non-Profit Organization: Balancing Multiple Business Objectives at Georgia Aquarium

Georgia Aquarium (GA), a non-profit organization, is the world’s second largest aquarium (by water volume) and is among the most popular tourist destinations in the United States. Recently GA management has observed that the organization’s growth trajectory is stagnating. While other aquariums face a similar trend, GA wants to be proactive and reverse this direction. They face a multifaceted business challenge involving four conflicting objectives: (1) How to increase revenues without increasing ticket prices; (2) how to increase attendance without compromising visitor satisfaction; (3) how to increase the impact of media investments without spending more; (4) how to attract customers with long-term value potential. To address these challenges, we developed an integrated approach consisting of multiple marketing science models including Data Envelopment Analysis, Competition Analysis, Spatial Analysis, Media Optimization Analysis, and Pass Holder Lifetime Net Revenue Analysis. Based on the findings of our analyses and the parameter estimates of our models, GA proceeded with a field implementation to validate our suggestions. As a result, they realized a 10% increase in attendance and a 12% increase in revenue in 2013, thereby enhancing their bottom line and growth. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0932 .

Practice Prize Paper—Incorporating Emotions into Evaluation and Choice Models: Application to Kmart Australia

This paper addresses the repositioning of Kmart Australia in 2011. It shows how by calibrating emotional as well as cognitive reactions and estimating their impact on purchase intentions, Kmart was able to focus its communications, improving market share. We measure nine key emotions, ranging from surprise to anger. Including these emotions significantly improves our model for likelihood to choose a store. Measuring emotions enabled Kmart’s advertising agency to create a television commercial that tapped into the specific emotions that most strongly predict the likelihood to choose a store; that is, the model drove the development of the advertising creative. The resulting television commercial tested well and was effective when launched. At the individual level, cognitions and emotions changed dramatically. At the aggregate level, an econometric model showed that store visits were significantly enhanced. Kmart’s EBIT (earnings before interest and tax) increased by 30%, whereas Kmart’s main rival had almost no EBIT growth, despite vigorous attempts to counter Kmart’s campaign. One of our key contributions is to incorporate emotions into marketing science models of evaluation and purchase intentions. We also provide a new methodology to measure emotions. The approach enables marketing science to participate in the design of marketing stimuli, rather than just testing preexisting ones. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0954 .

2013–2014 Gary L. Lilien ISMS-MSI Practice Prize Competition

I introduce the special section highlighting the background of the competition and the work of the finalists in the 2013–2014 Gary L. Lilien ISMS-MSI Practice Prize Competition, showcasing the best applications of rigor and relevance by marketing scientists in working with practical problems. The winning paper is by a team who developed an innovative pricing tool to enable an electric utility operating in Germany to acquire customers on online price comparison sites while optimizing various metrics of interest. The other two finalists comprise a team that developed an integrated marketing model to address multiple business objectives of the Georgia Aquarium and a team that developed a marketing science model of evaluation and purchase intentions incorporating customer emotions to test advertisement effectiveness for Kmart Australia.

Forecasting new product trial with analogous series

This study develops a simple method for forecasting consumer trial for national product launches. The number of consumers who try a brand in its first year on the market is accurately predicted from the number trying the brand in the first thirteen weeks following launch. No information about the specific category or marketing activities is required — just a simple multiplier computed from analogous series in other markets. These analogues provide an empirical generalization that can be easily applied by practicing managers to track and forecast the success of new brand launches. When subject to an out-of-sample test involving 34 fresh data sets, the analogues demonstrated 43% reduction in mean absolute percentage error compared to the most accurate marketing science model.

Effective Marketing Science Applications: Insights from the ISMS-MSI Practice Prize Finalist Papers and Projects

From 2003 to 2012, the ISMS-MSI Practice Prize/Award competition has documented 25 impactful projects, with associated papers appearing in Marketing Science. This article reviews these papers and projects, examines their influence on the relevant organizations, and provides a perspective on the diffusion and impact of marketing science models within the organizations. We base our analysis on three sources of data—the articles, authors' responses to a survey, and in-depth interviews with the authors. We draw some conclusions about how marketing science models can create more impact without losing academic rigor while maintaining strong relevance to practice. We find that the application and diffusion of marketing science models are not restricted to the well-known choice models, conjoint analysis, mapping, and promotional analysis—there are very effective applications across a wide range of managerial problems using an array of marketing science techniques. There is no one successful approach, and although some factors are correlated with impactful marketing science models, there are a number of pathways by which a project can add value to its client organization. Simpler, easier-to-use models that offer robust and improved results can have a stronger impact than academically sophisticated models can. Organizational buy-in is critical and can be achieved through recognizing high-level champions, holding in-house presentations and dialogues, doing pilot assignments, involving multidepartment personnel, and speaking the same language as the influential executives. And we find that intermediaries often, but not always, play a key role in the transportability and diffusion of models across organizations. Although these applications are impressive and reflect profitable academic–practitioner partnerships, changes in the knowledge base and reward systems for academics, intermediaries, and practitioners are required for marketing science approaches to realize their potential impact on a much larger scale than the highly selective sample that we have been able to analyze.

Practice Prize Paper—PROSAD: A Bidding Decision Support System for Profit Optimizing Search Engine Advertising

This paper reports on a large-scale implementation of marketing science models to solve the bidding problem in search engine advertising. In cooperation with the online marketing agency SoQuero, we developed a fully automated bidding decision support system, PROSAD (PRofit Optimizing Search engine ADvertising; see http://www.prosad.de), and implemented it through the agency's bid management software. The PROSAD system maximizes an advertiser's profit per keyword without the need for human intervention. A closed-form solution for the optimized bid and a newly developed “costs-per-profit” heuristic enable advertisers to submit good bids even when there is significant noise in the data. A field experiment demonstrates that PROSAD can increase the return on investment by 21 percentage points and improve the yearly profit potential for SoQuero and its clients by €2.7 million.

Effective Marketing Science Applications: Insights from ISMS-MSI Practice Prize Finalist Papers and Projects

From 2003 to 2012, the ISMS Practice Prize/Award competition has documented 25 impactful projects, with associated papers appearing in the Marketing Science. This article reviews these papers and projects, examines their influence on the relevant organizations, and provides a perspective on the diffusion and impact of marketing science models within the organizations. We base our analysis on three sources of data — the articles, authors’ responses to a survey, and in-depth interviews with the authors. We draw some conclusions on how marketing science models can create more impact without losing academic rigor, while maintaining strong relevance to practice.We find that the application and diffusion of marketing science models are not restricted to the well-known choice models, conjoint analysis, mapping, and promotional analysis — there are very effective applications across a wide range of managerial problems using an array of marketing science techniques. There is no one successful approach and, while some factors are correlated with impactful marketing science models, there are a number of pathways by which a project can add value to its client organization. Simpler, easier-to-use models that offer robust and improved results can have stronger impact than academically sophisticated models. Organizational buy-in is critical and can be achieved through high-level champions, in-house presentations and dialogs, doing pilot assignments, involving multi department personnel, and speaking the same language as the influential executives. And we find that intermediaries often, but not always, play a key role in the transportability and diffusion of models across organizations.While these applications are impressive and reflect profitable academic-partnerships, changes in the knowledge base and reward systems for academics, intermediaries and practitioners are required for marketing science approaches to realize their potential impact on a much larger scale than the highly selective sample that we have been able to analyze.

Practice Prize Paper—Applying a Dynamic Model of Consumer Choice to Guide Brand Development at Jetstar Airways

This paper describes the use of a marketing science model by Jetstar, a subsidiary of Australia's leading airline, Qantas, to effectively and profitably compete in the low-cost carrier marketplace. We trace the evolution of the Jetstar strategy from a baseline calibration of its initial position, to its efforts to attain price competitiveness and service parity, followed by its highly focused, cost-effective service delivery strategy. We develop a hierarchical model with parameters estimated at the individual level. This allows us to study not only how service design and pricing initiatives shift the perceived performance of Jetstar relative to its competitors but also how the airline can move market preferences toward areas in which it has competitive advantage. The contribution of the research is substantial. The Jetstar market share went from 14.0% to 18.1% during the first five quarterly waves of the research, and profits went from $79 million in 2006–2007, before the study was commissioned, to $124 million in 2008–2009.

Forecasting New Product Trial with Analogous Series

We develop a simple method for forecasting and benchmarking consumer trial of new products. First, we extend the exponential trial growth models used in controlled test markets to the context of national product launches. This provides a marketing science benchmark against which our new approach can be compared. Second, as managers do not generally possess the skills to apply marketing science models, we apply the rarely-used method of analogous series to forecast consumer trial. We show that this method performs just as well as the marketing science approach. Third, we convert the results of the analogous series into a simple empirical generalization that enables the cumulative trial metric to be both benchmarked and used for forecasting. These results will allow managers and other researchers to systematically interpret this widely used metric against scientifically developed norms.

Linking marketing and supply chain models for improved business strategic decision support

A supply chain (SC) model incorporating business strategic decision components is an important tool for gaining a competitive edge in todays global market. Enterprise models of this type must encompass not only the SC, but also the demand chain since understanding the market is crucial for developing good business policies. To operate effectively, marketing activities must be coordinated with other corporate functional areas. Specifically, managers should evaluate the trade-off between marketing and SC decisions to enhance the performance of the overall metric: the shareholders value. Recently, there has been significant progress in developing marketing science models for reaching quantitatively based marketing decisions. In this work, we build on these developments to formulate a mathematical model that accounts for the main relevant business functionalities. The result is a MINLP formulation which optimizes the SC and marketing strategic decisions in an integrated fashion. Moreover, a financial model that evaluates the enterprise value is also incorporated.

Spatial Models in Marketing

Marketing science models typically assume that responses of one entity (firm or consumer) are unrelated to responses of other entities. In contrast, models constructed using tools from spatial statistics allow for cross-sectional and longitudinal correlations among responses to be explicitly modeled by locating entities on some type of map. By generalizing the notion of a map to include demographic and psychometric representations, spatial models can capture a variety of effects (spatial lags, spatial autocorrelation, and spatial drift) that impact firm or consumer decision behavior. Marketing science applications of spatial models and important research opportunities are discussed.

Practice Prize Article—CHAN4CAST: A Multichannel, Multiregion Sales Forecasting Model and Decision Support System for Consumer Packaged Goods

We discuss the development and implementation of CHAN4CAST, a sales forecasting model, by pack size, category, channel, region, customer account and a Web-based decision support system (DSS) for consumer packaged goods. In addition to capturing the effects of such variables as past sales, trend, own and competitor prices and promotional variables, and seasonality, the model accounts for the effects of temperature, significant holidays, new product introductions, trading day corrections, and adjustments to the wholesale level. In general, the model forecasts sales volume satisfactorily for a leading consumer packaged goods company. The DSS enables top- and mid-level executives in sales, marketing, strategic planning, and finance to develop accurate forecasts of sales volume, plan prices, and promotional activities over a long time horizon; to track sales response to marketing actions over time; and to simulate forecast scenarios based on possible marketing decisions and other variables. CHAN4CAST is being rolled out for more users and more divisions in the company. The key take-aways are that successful development and implementation of a rigorous marketing science model require a strong internal champion, a careful balance between modeling sophistication and practical relevance, good diagnostic features, regular validations, and greater attention to the development of a fast and responsive DSS.

Consumption and marketing in Japan

This study will show how macromarketing affects Japan's sluggish consumption level. In this research, we will refer to the steps of the marketing science model, thereby advancing the area developmental stages model, which is based on the social indicators analysis. Total marketing comprised of the pull and push marketing performs the manifest-positive function to the products' consumption level, presented by retail stores' annual purchase. In evaluating its contribution, however, the area's developmental stage, pettiness, urbanization, and saturation will also be taken into consideration as they represent all the properties constituting social space. This will lead the discussion to the conclusion and several suggestions for future research.

MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures

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.

Testing Competitive Market Structures

An accurate understanding of the structure of competition is important in the formulation of many marketing strategies. For example, in new product launch, product reformulation, or positioning decisions, the strategist wants to know which of his competitors will be most affected and hence most likely to respond. Many marketing science models have been proposed to identify market structure. In this paper we examine the managerial problem and propose a criterion by which to judge an identified market structure. Basically, our criterion is a quantification of the intuitive managerial criterion that a “submarket” is a useful conceptualization if it identifies which products are most likely to be affected by “our” marketing strategies. We formalize this criterion within the structure of classical hypothesis testing so that a marketing scientist can use statistical statements to evaluate a market structure identified by: (1) behavioral hypotheses, (2) managerial intuition, or (3) market structure identification algorithms. Mathematically, our criterion is based on probabilities of switching to products in the situation where an individual's most preferred product is not available. ‘Submarkets’ are said to exist when consumers are statistically more likely to buy again in that ‘submarket’ than would be predicted based on an aggregate “constant ratio” model. For example, product attributes (e.g., brand, form, size), use situations (e.g., coffee in the morning versus coffee at dinner), and user characteristics (e.g., heavy versus light users) are specified as hypotheses for testing alternate competitive structures. Measurement and estimation procedures are described and a convergent approach is illustrated. An application of the methodology to the coffee market is presented and managerial implications of six other applications are described briefly.

Challenges and new research avenues in marketing science

This paper is a revised version of the key note speech which the author gave at the 1984 Annual Conference of the European Marketing Academy (EMAC). The author critically evaluates the contributions of marketing science with respect to practical relevance and managerial decision making. He diagnoses a number of substantial discrepancies between research efforts and adoption of marketing science models. Some causes for this situation are identified and new more comprehensive research avenues are proposed and discussed. It is the intention of the article to stimulate further discussion on these important issues. Necessarily, many statements in this position paper are based on subjective judgement and not validated in a scientific sense.