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Investigating the measures of relative importance in marketing research
Harvir S. Bansal and Philippe Duverger, International Journal of Market Research, Vol. 55, No. 5, 2013, pp. 675-694
Determining the relative importance of various predictors in a marketing research model is important for both theoretical and practical reasons.
Determining the relative importance of various predictors in a marketing research model is important for both theoretical and practical reasons. To date, the most commonly used methods to assess relative importance have involved examining either the regression coefficients or zero-order correlations of each predictor. Unfortunately, these indices are problematic when the predictors are correlated, as is the case with many of the drivers of service-provider switching, loyalty studies, satisfaction models and other marketing research. In this paper, we introduce Dominance Analysis to an audience of researchers in marketing research and empirically demonstrate its usefulness for assessing predictor relative importance. Using a Monte Carlo simulation, we first compare the accuracy of five traditional methods used in marketing research assessing relative importance and comparing them to Dominance Analysis. There are theoretical, as well as empirical, advantages to using Dominance Analysis over other methods, and these are discussed in the context of an empirical example using data drawn from a larger study of auto-repair service customers (n = 355).
Stop Flawed Marketing Mix Models From Stunting Growth
David Hoo and Michael von Gonten, ARF Experiential Learning, Audience Measurement 8.0, 2013
This paper argues that marketing mix models are not useful, and that the mix models currently in use, either the original format or the newer, VAR style, systematically understate the true effects of advertising.
This paper argues that marketing mix models are not useful, and that the mix models currently in use, either the original format or the newer, VAR style, systematically understate the true effects of advertising. Mix models are regression models and, as such, are incapable of providing truly causal evidence as to the effects of advertising and promotion. This leads marketers to undervalue the real effects of advertising and to reduce their advertising spending, reallocating those funds to price promotion. Such reductions in expenditure have the unintended effect of stunting sustainable growth and eroding the brand equity built by advertising. Moreover, reducing adspend to maximise efficiency is a vicious circle; on the other hand, effective advertising is an engine of growth.
The limits of prediction
Ian Durbach and Gillian Drewett, Admap, February 2013, pp. 40-41
Predicting trends is not an exact science and giving up the idea that the future can be planned with certainty is the key to planning.
Predicting trends is not an exact science and giving up the idea that the future can be planned with certainty is the key to planning. Recognising that there are fundamental limits as to how well future behaviour can be predicted can help marketers to direct resources more efficiently. This article uses examples of marketing failures by Honda and Mars and the successes of companies such as Burberry to show how gathering and modelling data more frequently can allow marketers to set more realistic goals.
Quantitative research: Storytelling with numbers
Ollie Willis, Admap, December 2012, pp. 38-39
Brand owners have access to more data than ever before and the sea of data is getting deeper and a guiding light is needed more than ever.
Brand owners have access to more data than ever before and the sea of data is getting deeper and a guiding light is needed more than ever. Quantitative research has to be part of the broader strategic conversation to deliver genuine value. To do this, quant researchers need to become storytellers as well as information providers. Telling a story with quant requires researchers to think about how to tell the story at every stage, from responding to the brief, to delivering the final output. This article explains the four key parts to telling a story with numbers.
Rediscovering the Master, John D. C. Little: A global approach to marketing ROI analytics
Lisa Wellington, ARF Experiential Learning, Re:think conference, 2012
Lisa Wellington of the Coca-Cola Company outlines the approach to marketing ROI analytics used by the soft drinks giant.
Lisa Wellington of the Coca-Cola Company outlines the approach to marketing ROI analytics used by the soft drinks giant. Specifically, the company uses a number of scientific models that quantify the volume of one or a set of marketing drivers, which are then strung together to provide a comprehensive view of the marketplace. The paper outlines the specific models used and how they fit together, covering the importance of increasing productivity, the media investment approach of the company and the scientific models used to estimate marketing ROI.
An improved, practical model of consumer choice
Len Marchant, Phil Prescott and Nic Jackson, International Journal of Market Research, Vol. 54, No. 1, 2012, pp. 71-92
This paper describes a framework for understanding and researching brand choice. The underlying model starts from the assumption that purchasers faced with alternative brands will select what in their judgement suits them best.
This paper describes a framework for understanding and researching brand choice. The underlying model starts from the assumption that purchasers faced with alternative brands will select what in their judgement suits them best. It develops the theory and the mathematics as simply as possible, and goes on to describe the marketing implications. It explains how to build a working model of the market using Monte Carlo techniques, and how this can be used to test the validity of the basic assumptions and to explore possible marketing strategies. It demonstrates, using real data from an actual study, how to interpret the market model in terms of purchasers' images of the brands. The paper will be of interest to both qualitative and quantitative researchers.
Understanding best practice in strategic futures work
The Futures Company Trends, Future Perspectives, December 2011
This report from The Futures Company was originally commissioned by the British Government and is now republished as part of the former's Future Perspectives series.
This report from The Futures Company was originally commissioned by the British Government and is now republished as part of the former's Future Perspectives series. With a focus on processes and systems, the report emphasises that for organisations to make effective use of strategic futures work it is necessary for them to accept the impossibility of predicting future outcomes. The objective is rather to introduce a new set of skills and perspectives that enable a quicker response to changed circumstances: one person described it as "a strategic fire drill". Best practice will vary by type of organisation and by country but the methods adopted must be credible to the organisation and key audiences. And ensuring the buy-in of both senior staff and those affected by the work will help gain acceptance for new thinking.
Online Data Analytics - From the consumer decision to the bigger picture
Louisa Middleton, Warc Exclusive, Datacentric, December 2011
Louisa Middleton, product marketing manager of EMEA Market Insights at Google UK gave a presentation on data and consumer decisions at the Warc Datacentric conference.
Louisa Middleton, product marketing manager of EMEA Market Insights at Google UK gave a presentation on data and consumer decisions at the Warc Datacentric conference. Middleton looked at what we know about consumer decisions based on available data sources, the context of consumer decisions and how to link consumer decisions to marketing decisions.
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.
Modeling the Real Return on Marketing Investments
Dr. Peter Cain, Marketing NPV, Volume 7, Issue 3, 2011, pp. 15-19
It can be challenging to decide how best to allocate an often limited marketing budget across a wide set of marketing activities.
It can be challenging to decide how best to allocate an often limited marketing budget across a wide set of marketing activities. Conventional models focus solely on incremental volume, often recommending a marketing budget allocation skewed towards promotional activity, which ignores the long-run view. To address this issue, the marketing mix model needs to be re-structured to quantify both short-run and long-run variation in the data, as demonstrated in this article. Not only does this provide more accurate short-run marketing results but, when combined with evolution in intermediate brand perception measures, allows an evaluation of the long-run impact of marketing activities.
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