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Research Fusion: Merging public health, consumer and healthcare market research to inform health initiatives in developing countries
Melissa Moodley, Colin Baker, Greg Zwisler and Evan Simpson, ESOMAR, Congress, Istanbul, September 2013
This paper explains how PATH, a non-profit health-focused organisation, utilised healthcare market research in a public health campaign.
This paper explains how PATH, a non-profit health-focused organisation, utilised healthcare market research in a public health campaign. The research examined issues around low cost healthcare solutions that could prevent deaths in poor countries to better understand why those solutions were under-utilised. The findings demonstrate the value of including market research techniques in public health strategy. It highlights the importance of taking a consumer centric approach and prioritising local insight over global.
We know exactly what you want: the development of a completely individualised conjoint analysis
Markus Voeth, Uta Herbst and Frank Liess, International Journal of Market Research, Vol. 55, No. 3, 2013, pp. 437-458
Improving the predictive validity of conjoint analysis has been an important research objective for many years.
Improving the predictive validity of conjoint analysis has been an important research objective for many years. Whereas the majority of attempts have been different approaches to preference modelling, data collection or product presentation, only a few scholars have tried to improve predictive validity by individualising conjoint designs. This comes as a surprise because many markets have observed an augmented demand for customised products and highly heterogeneous customers’ preferences. Against this background, the authors develop a conjoint variant based on a completely individualised conjoint design. More concretely, the new approach not only individualises the attributes, but also the attribute levels. The results of a comprehensive empirical study yield a significantly higher validity than existing standardised-level conjoint approaches. Consequently, they help marketers to gain deeper insights into their customers’ preferences.
Using response surface methodology to optimise factors in conjoint experiments
Rubén Huertas-Garcia, Juan Carlos Gázquez-Abad, Francisco J. Martínez-López and Irene Esteban-Millat, International Journal of Market Research, Vol. 55, No. 2, 2013, pp. 267-288
Identifying relevant attributes or variables is the first objective of conjoint analysis in market research.
Identifying relevant attributes or variables is the first objective of conjoint analysis in market research. As a result of technological development, today it is common for researchers to use sequential experimental methods for adjusting design factors in successive phases. In particular, in the field of consumer behaviour these models are used predominantly for assessing subjective perceptions relating to the attributes of different products with high sensorial components (e.g. food, drinks and personal care products). This paper illustrates the use of response surface methodology in conjoint experiments, allowing sequential research in which the evaluation of a choice set determines the weight of factors in the next choice set and continues until the optimum combination is achieved. To this end we have carried out a computer simulation to determine the optimal combination of ingredients for a sauce. The simulation shows that the model needs only a few steps to reach the optimal combination of ingredients. This result indicates that response surface methodology can be considered a useful tool in the field of market research and, in particular, in studies on consumer behaviour.
The difference between 'less bad' and 'much better': Helping conjoint to live up to its promises by leveraging 'behavioural economics'
Florian Bauer, ESOMAR, Congress, Atlanta, September 2012
This ESOMAR paper looks at how to integrate behavioural economics insights with conjoint analysis, thereby making predictions more valid while maintaining the core advantages of conjoint analysis.
This ESOMAR paper looks at how to integrate behavioural economics insights with conjoint analysis, thereby making predictions more valid while maintaining the core advantages of conjoint analysis. More generally, the authors argue that results can only be improved by merging conjoint analysis with other research disciplines, rather than merely attempting to develop even better conjoint analysis. They also discuss a 'General Algorithm for Patching Conjoint Analyses' tool that corrects the main cognitive and motivational distortions which occur in conjoint analysis.
Next please - online game for bank tellers: Educate your business partner's sales force through the interactive online game
Jan Lajka, ESOMAR, CEE Research Forum, Krakow, March 2012
Using the example of a research project for the CSOB, a leading Czech bank, this presentation demonstrates how a research assignment can be turned into a highly useful, multi-purpose tool benefiting both the client and the bank's customer.
Using the example of a research project for the CSOB, a leading Czech bank, this presentation demonstrates how a research assignment can be turned into a highly useful, multi-purpose tool benefiting both the client and the bank's customer. Results delivered by conjoint analysis on the bank's personal banking product portfolio were used to develop an educative online game that simulates a sales communication of bank representatives with their customers. As an innovative concept for training the sales force, the game was eventually merged into CSOB's internal education system.
Make or break: a simple non-compensatory customer satisfaction model
Keith Chrzan and Michael Kemery, International Journal of Market Research, Vol. 54, No. 2, 2012, pp. 163-176
We propose a model that allows analysts to capture and quantify realistic non-linear, non-compensatory effects in customer satisfaction modelling.
We propose a model that allows analysts to capture and quantify realistic non-linear, non-compensatory effects in customer satisfaction modelling. For too long, academic and applied marketing researchers have relied upon restrictive linear, compensatory statistical models to inform their understanding of how performance on product and service attributes impacts overall satisfaction, loyalty, etc. An extended case study and a summary of 22 further empirical studies illustrate the utility and robustness of the proposed Make or Break model of customer satisfaction.
Market share predictions: a new model with rating-based conjoint analysis
Hervé Guyon and Jean-François Petiot, International Journal of Market Research, Vol. 53, No. 6, 2011, pp. 831-857
Conjoint Analysis (CA) is a technique heavily used by industry in support of product development, pricing and positioning, and market share predictions.
Conjoint Analysis (CA) is a technique heavily used by industry in support of product development, pricing and positioning, and market share predictions. This generic term CA encompasses a variety of experimental protocols and estimation models (e.g. rating-based or choice-based), as well as several probabilistic models for predicting market share. As for the rating conjoint, existing probabilistic models from the literature cannot be considered as reliable because they suffer from the Independence of Irrelevant Alternatives (IIA) property, in addition to depending on an arbitrary rating scale selected by the experimenter. In this article, after a brief overview of CA and of models used for market share predictions, we propose a new model for market share predictions, RFC-BOLSE, which avoids the IIA problem, yields convergent results for different rating scales, and outputs predictions that match regression reliability. The model is described in details and simulations and a case study on truck tyres will illustrate the reliability of RFC-BOLSE.
Incorporating demographics into discrete choice analyses
Robert E. Carter, International Journal of Market Research, Vol. 52, No. 3, 2010, pp. 393-406
Discrete choice experiments are analysed using multinomial logit models. One key trait of these models is that independent variables are usually based on alternative related characteristics, such as the price of different options or the commute time for different travel alternatives.
Discrete choice experiments are analysed using multinomial logit models. One key trait of these models is that independent variables are usually based on alternative related characteristics, such as the price of different options or the commute time for different travel alternatives. Respondent level characteristics, or demographics, are not typically included as independent variables or moderating constructs since these parameters do not vary across options in a choice set and, as such, do not impact the corresponding choice probabilities. To address this weakness, the objective of the current paper is to share a practical and usable approach to incorporate demographic variables as moderating constructs in discrete choice experiments and multinomial logit models. This approach requires the computation of a new variable representing the interaction between the focal demographic variable and an alternative related characteristic. For illustrative purposes, this procedure is applied to hypothetical transportation data.
The truth is out there! How external validity can lead to better marketing decisions
Greg Rogers and Didier Soopramanien, International Journal of Market Research, Vol. 51, No. 2, 2009, pp. 163-180
Marketing managers typically have to use and integrate many pieces of data and marketing intelligence when taking decisions such as whether to launch a product and, if so, at what price.
Marketing managers typically have to use and integrate many pieces of data and marketing intelligence when taking decisions such as whether to launch a product and, if so, at what price. Conjoint experiments and analysis remain popular marketing research tools with business practitioners to test and measure how the market will react to different actions. There is a growing body of work that focuses on, first, how to construct the experiments so that they better represent real market conditions and, second, the use of sophisticated model specifications that provide information on consumers’ responses. The market researcher typically uses internal validation for model validity – a comparison of model prediction and within-sample holdout data. We contend in this paper that customers and users of market research information need to adopt a different and wider meaning of validity, referred to as external validity, to facilitate improved decision making. In this research, a case study is used as an example to demonstrate how marketing managers can use the information from a choice-based conjoint derived choice model differently depending on the manner in which the model validation is carried out.
Rethinking data analysis - part two: some alternatives to frequentist approaches
Ray Kent, International Journal of Market Research, Vol. 51, No. 2, 2009, pp. 181-202
In ‘Rethinking data analysis – part one: the limitations of frequentist approaches’ (Kent 2009) it was argued that standard, frequentist statistics were developed for purposes entirely other than for the analysis of survey data; when applied in this context, the assumptions being made and the limitations of the statistical procedures are commonly ignored.
In ‘Rethinking data analysis – part one: the limitations of frequentist approaches’ (Kent 2009) it was argued that standard, frequentist statistics were developed for purposes entirely other than for the analysis of survey data; when applied in this context, the assumptions being made and the limitations of the statistical procedures are commonly ignored. This paper examines ways of approaching the analysis of data sets that can be seen as viable alternatives. It reviews Bayesian statistics, configurational and fuzzy set analysis, association rules in data mining, neural network analysis, chaos theory and the theory of the tipping point. Each of these approaches has its own limitations and not one of them can or should be seen as a total replacement for frequentist approaches. Rather, they are alternatives that should be considered when frequentist approaches are not appropriate or when they do not seem to be adequate to the task of finding patterns in a data set.
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