Dynamic Analysis of Consumer Response to Marketing Strategies
TL;DR: In this article, the authors developed a methodology for modeling consumer response that integrates previous research in stochastic brand selection, diffusion of innovation, test market analysis, and new product design.
Abstract: This paper develops a methodology for modeling consumer response that integrates previous research in stochastic brand selection, diffusion of innovation, test market analysis, and new product design. The methodology makes it practical to extend brand selection models to include diffusion phenomena such as awareness, trial, and information flow. Purchase timing and brand selection are interdependent and both phenomena depend jointly on managerial controls such as advertising, coupons, price-off promotion, product positioning, and consumer characteristics.
Within this general structure, we provide practical estimation procedures a least squares approximation to the maximum likelihood estimates to determine the parameters which link managerial controls to consumer response. Closed form solutions are derived for cumulative awareness, cumulative trial, penetration, expected sales, and purchases due to promotion-all as a function of time. We also provide simplified expressions for equilibrium t â ∞ market share. Tradeoffs among complexity of the diffusion process, number of managerial variables, nonstationarity, complexity of purchase timing, consumer segmentation, and sample size are made explicit so that the marketing scientist can customize his analyses to the managerial problems that he faces.
The effects of sample size, data interval frequency, and collinearity in the explanatory variables are investigated with simulations based on a five-state consumer response process which depends on 8-10 marketing variables.
The paper closes with a brief description of the application and predictive test of a consumer response model based on the methodology.
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TL;DR: In this paper, the authors identify 16 topics relevant to marketing science, which they classify under five research fields: consumer response to innovation, including attempts to measure consumer innovative-ness, models of new product growth, and recent ideas on network externalities.
Abstract: Innovation is one of the most important issues in business research today. It has been studied in many independent research traditions. Our understanding and study of innovation can benefit from an integrative review of these research traditions. In so doing, we identify 16 topics relevant to marketing science, which we classify under five research fields: - Consumer response to innovation, including attempts to measure consumer innovative-ness, models of new product growth, and recent ideas on network externalities - Organizations and innovation, which are increasingly important as product development becomes more complex and tools more effective but demanding - Market entry strategies, which includes recent research on technology revolution, exten-sive marketing science research on strategies for entry, and issues of portfolio manage-ment - Prescriptive techniques for product development processes, which have been transformed through global pressures, increasingly accurate customer input, web-based communica-tion for dispersed and global product design, and new tools for dealing with complexity over time and across product lines - Defending against market entry and capturing the rewards of innovating, which includes extensive marketing science research on strategies of defense, managing through metrics and rewards to entrants For each topic, we summarize key concepts and highlight research challenges. For pre-scriptive research topics, we also review current thinking and applications. For descriptive top-ics, we review key findings.
1,040 citations
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TL;DR: In this paper, the authors identify 16 topics relevant to marketing science, which they classify under five research fields: consumer response to innovation, including attempts to measure consumer innovativeness, models of new product growth, and recent ideas on network externalities; organizations and innovation, which are increasingly important as product development becomes more complex and tools more effective but demanding.
Abstract: Innovation is one of the most important issues in business research today. It has been studied in many independent research traditions. Our understanding and study of innovation can benefit from an integrative review of these research traditions. In so doing, we identify 16 topics relevant to marketing science, which we classify under five research fields:
Consumer response to innovation, including attempts to measure consumer innovativeness, models of new product growth, and recent ideas on network externalities; Organizations and innovation, which are increasingly important as product development becomes more complex and tools more effective but demanding;.
Market entry strategies, which includes recent research on technology revolution, extensive marketing science research on strategies for entry, and issues of portfolio management;
Prescriptive techniques for product development processes, which have been transformed through global pressures, increasingly accurate customer input, Web-based communication for dispersed and global product design, and new tools for dealing with complexity over time and across product lines;
Defending against market entry and capturing the rewards of innovating, which includes extensive marketing science research on strategies of defense, managing through metrics, and rewards to entrants.
For each topic, we summarize key concepts and highlight research challenges. For prescriptive research topics, we also review current thinking and applications. For descriptive topics, we review key findings.
956 citations
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654 citations
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TL;DR: In this paper, a model of service provider switching was developed based on Keaveney's work and using related studies from the disciplines of marketing and psychology, and empirically examined using struct...
Abstract: Building on Keaveney’s work and using related studies from the disciplines of marketing and psychology, a model of service provider switching was developed. It was empirically examined using struct...
429 citations
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TL;DR: In this article, the authors present an interactive Markov chain model for predicting the box-office performance of motion pictures based on a behavioral representation of the consumer adoption process for movies as a macroflow process.
Abstract: 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.
254 citations
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"Dynamic Analysis of Consumer Respon..." refers background or methods in this paper
...In marketing, diffusion phenomena have been particularly effective in modeling the growth, and possibly decline, of sales in new product categories (Bass [9], Nevers [94], Dodds [25], Mahajan and Peterson [78], Mahajan and Muller [77])....
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...Thus for accumulated variables we make the same "discrete" analog approximation that Bass [9] and his colleagues make in their diffusion models....
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