Contributing to the field of Marketing Science.

Marketing Mix & Attribution

Marketing Mix Models - a thought paper (Click here to obtain)

An overview of in4mation insight's approach to marketing mix showing multiple outputs and with an emphasis on the senstivity of ROI calculations to various factors. Both direct effect regression and indirect, network analysis included.

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Methods to Eliminate Bias in Models using Aggregate Data (Click here to obtain)

Many microeconomic models are based on store-level data and can be biased when the same model is applied to retailer or market level data. This paper shows the fix we use to debias the estimates.

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Bias in the Calculation of Marketing ROI

By using a common profit term for each element of the marketing mix and by not correctly allocating costs in the profit calculations, the common definition and calculation of ROI will be biased by a large amount. The standard profit definition leads to biased ROI because the cost of generating incremental sales is distributed over sales and profits that have nothing to do with those incremental sales.

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RGM/Pricing

Analytics4RGM (Click here to obtain)

An overview of in4mation insights approach to Pricing and RGM including understanding the sources of consumer demand, optimizing the volume/profit mix and systems that can embed analytics in business decisions.

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A Model for Trade-Up and Change in Considered Brands

A common theme in the marketing literature is the acquisition and retention of customers as they trade-up from inexpensive, introductory offerings to those of higher quality. Standard models of choice, however, apply to narrowly defined categories for which assumptions of near-perfect-substitution are valid. We extend the non-homothetic choice model of Allenby and Rossi (1991) to accommodate effects of advertising, professional recommendation and other factors that facilitate the description and management of trade-up. The model is applied to a national study of an over-the-counter health product.

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When Consumers Go beyond Choice: Models for Trade-up and Change in Consideration Set

Presented at the 2007 ART Forum Conference in Santa Fe, New Mexico, this conference deck explains trade-up decisions made by consumers and introduces the presenters' trade-up model that measures this effect.

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A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts

In this paper, we provide an economic model of demand for substitute brands that is flexible, parsimonious, and easy to implement. The methodology is demonstrated with a scanner panel data set of light-beer purchases. The model is used to explore the effects of price promotions on primary and secondary demand, and the utility of product assortment.

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In Today's Environment, Affordability is More Meaningful Than Elasticity.

Price elasticities are the "net" of many different effects driven by all the players in the supply chain: manufacturer, retailer and consumer. Presented at the 2010 Professional Pricing Society Conference Chicago, this conference deck uses new research in consumer behavioral models to show that what we see as price elasticity in aggregate and conjoint data is just as much the consumer decision to buy in the category at all as it is substitution within the category.

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Consumer & Market Segmentation / Market Structure

Market Structure - The Last 35 Years

This document chronicles the history of market structure methods from the pseudoscientific but popular Hendry system of the 1970's to the more recent and theoretically sound work in the area of preference heterogeneity.

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Renewing market segmentation: Some new tools to correct old problems

Nominated for Best Methodological Paper at the 2002 ESOMAR Congress, this paper critically examines the usual and standard tools and methods of benefit.

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Latent Segmentation Models: New tools to assist researchers in market segmentation

Market segmentation remains one of the most powerful marketing ideas. Since its formal introduction in the '50s, its use for customer understanding, product development and marketing strategy has grown. The development of analytic methods for segmenting markets, however, has lagged their need in business applications. The authors see the next wave as a suite of analytic procedures called latent segmentation models.

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Maximum Difference Scaling: Improved Measures of Importance and Preference for Segmentation

Maximum Difference scaling (MaxDiff), a technique for measuring the importance or preference of multiple items, is shown to provide results that have greater between-item and between-respondent discrimination, and greater predictive accuracy than either monadic ratings or paired comparisons. Steve Cohen describes the methodology and presents results for a methodological study comparing MaxDiff measurement with monadic ratings and paired comparisons, and also a case study focusing on using MaxDiff for segmentation work. Steve won the "best presentation" award with this paper at the 2003 Sawtooth Software Conference.

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Joint Segmentation on Distinct Interdependent Bases with Categorical Data

The authors discuss a latent class framework for market segmentation with categorical data on two conceptually distinct but possibly interdependent bases for segmentation (e.g., benefits sought and usage of products and services). The authors present an empirical application, using pick-any data collected by a regional bank on two popular, conceptually appealing, and interdependent bases for segmenting customers of financial services-benefits (i.e., desired financial goals) and product usage (of an array of banking services). A comparative evaluation of the approach on synthetic data demonstrates the ability of the modeling framework to detect and estimate the interdependence structure underlying the two segmentation bases and thereby provide more accurate segmentation than "traditional" (single-basis) latent segmentation methods.

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Market segmentation with Choice-based Conjoint Analysis

We discuss a method for market segmentation with choice-based conjoint models. This method determines the number of market segments, the size of each market segment, and the values of segment-level conjoint part-worths using commonly collected conjoint choice data. A major advantage of the proposed method is that current (incomplete) data collection approaches for choice-based conjoint analysis can still be used for market segmentation without having to collect additional data. We illustrate the proposed method using commercial conjoint choice data gathered in a new concept test for a major consumer packaged goods company. We also compare the proposed method with an a priori segmentation approach based on individual choice frequencies.

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Modeling Variation in Brand Preference: The Roles of Objective Environment and Motivating Conditions

This academic paper was sponsored by Mark Garratt when he worked for Miller Brewing Company. The paper uses the Bayesian upper model to link motivations/need-states in various social occasions to the preference for different brands of beer.

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Choice/Conjoint

What's your preference? Asking survey respondents about their preferences creates new scaling decisions

When it comes to scaling multiple items, researchers have several options. This article compares maximum difference scaling against monadic ratings and paired comparisons. Among other benefits, it offers improved measures of discrimination across items over rating scales. This paper won the David K. Hardin Award for Best Paper in 2004 Marketing Research Magazine.

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Measuring preference for product benefits across countries: Overcoming scale usage bias with Maximum Difference Scaling

This paper briefly reviews the standard practices of benefit measurement and benefit segmentation and, along the way, points out their deficiencies. The authors then introduce Maximum Difference scaling, a method they believe is a much more powerful method for measuring benefit importance. An example is provided of how both the traditional and the newer methods were used in a cross-national consumer study of coffee drinkers and how the results compare. This paper is the winner of the John and Mary Goodyear Award for the Best International Paper presented at ESOMAR Conferences in 2003.

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Have it Your Way: Menu-based conjoint analysis helps marketers understand mass customization.

How must researchers craft studies and analyze the data to get a deeper grasp of mass customization? How must researchers investigate picking from a menu, and how must they examine the results to best expose the behavioral underpinnings of the build-your-own situation? One might think that traditional conjoint analysis is appropriate and effective for understanding how people want to construct product bundles, but the menu situation's additional complexity makes that approach entirely inadequate. Enter menu-based conjoint analysis – expressly designed for handling a build-your-own, select-from-a-menu, mass customization situation.

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Choice-Menus for Mass Customization: An Experimental Approach for Analyzing Customer

Companies are increasingly engaging in mass customization and offering consumers a "choiceboard" (or a menu of choices) of various features and options for configuring their own products and services. The authors discuss the use of experimental choice menus for assessing customers' preferences and price sensitivities for the variety of features and options that might be offered by a firm in its choiceboard.

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Developing an Optimal Product Line Using Consumer Preferences and Cost Data

Much of the activity of market researchers focuses on understanding the effect of new products on the firm. By researching product acceptance before introduction, manufacturers recognize that careful attention to consumer wants and needs will increase their likelihood of success. This paper provides a case study of the challenges that faced both a client firm (Eastman Kodak Company) and its research partner (SHC & Associates) in developing research to study consumer acceptance of potential entrants in four new product lines being contemplated by Kodak. The paper first addresses the challenges faced by the internal researcher in drawing out the needs of product management, having them accept the possibilities and limitations of consumer research, and developing an understanding of the study findings and uses of the research when the study is complete. Secondly the paper addresses the challenges faced by the research agency in developing a research design and program that fits the needs of the client, implementing that design, and developing findings that can be easily understood and implemented.

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Perfect Union: CBCA marries the best of conjoint and discrete choice models

This article reports on discrete choice models as utilized by choice-based conjoint analysis (CBCA). The nature of the discrete choice model involves statistical methods for choice response analysis while conjoint analysis requires a research technique for a systematic collection of data on choices using experimental design. Both assist managers and researchers in reading drivers of choice. During the early 1970's, conjoint analysis was used to interpret consumer preferences in a wide range of market situations. At about the same time, discrete choice models were introduced to explain the behavior of consumers. Characteristics of both were merged to form CBCA, which subdues deficiencies and offers more choices from the set of product alternatives.

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Forecasting the Market Potential for New Providers of Local Telephone Services to Business Customers

At the time this paper was written, deregulation was creating competition in the communications industry, allowing telephone service providers, media companies and other firms to offer everything from local telephone services to video-on-demand. Thus, predicting preference and likely market share for new competitors was a critical business issue. This case study is based on research conducted by U S WEST and Time Warner Communications to quantify the potential for medium and large corporations and government agencies to switch their business from the incumbent local telephone provider to companies offering similar services. We describe our approach to predicting preferences and likely market share using a designed discrete choice experiment. We also show how customer preferences were combined with managerial judgments of the potential behaviors of likely competitors to predict share gains and losses using a dynamic diffusion model.

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Efficient Experimental Design with Market Research Applications

This academic paper was one the first to show the use of computerized algorithms to generate experimental designs for conjoint and choice models.

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Sensory

BaSIC (Bayesian Sensory Model Integrated with Characteristics): A Stochastic MDS Model for Sensory Analysis

Food technologists and perfumers seek to understand how to combine dozens of ingredients and how the blend of these will affect consumers' taste perceptions and product preferences. Our solution uses Bayesian estimation methods and is called BaSIC (Bayesian Sensory Model Integrated with Characteristics). The Bayesian statistical framework provides a unique and attractive way of estimating MDS models that addresses many of the challenges of analyzing sensory preference data.

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