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Actionable analytics: Reexamining metrics for better data-driven decisions
Michael J McDermott, ANA Magazine, Autumn 2013
This article looks at how marketers can make their data analytics more effective, allowing them to develop real-time actionable models that integrate multichannel customer activities.
This article looks at how marketers can make their data analytics more effective, allowing them to develop real-time actionable models that integrate multichannel customer activities. There are many challenges, including handling large amounts of data, getting to grips with using cloud computing and creating a data-driven culture throughout the company. However, if these can be addressed, then data analytics can provide substantial benefits in customer experience, engagement, retention and loyalty, and marketing optimisation and performance. Best practice advice comes from MarketShare, a predictive analytics firm, Gilt Groupe, the online retailer, and SAS Institute, developers of analytics software.
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).
Beyond Big Data: How Big Data needs consumer insights in order to realise it's full potential
Colin Strong, ESOMAR, 3D Digital Dimensions, Boston, June 2013
This paper critiques the current Big Data agenda and argues that the market research industry will need to be instrumental in delivering new forms of analytics - one which brings together the strategic understanding of the researcher with the technology that enables the interrogation of Big Data.
This paper critiques the current Big Data agenda and argues that the market research industry will need to be instrumental in delivering new forms of analytics - one which brings together the strategic understanding of the researcher with the technology that enables the interrogation of Big Data. The paper describes the potential benefits of Big Data such as targeting, profiling and predictive techniques but also highlights the importance of understanding context, which, if overlooked can lead to bias and misinterpretation. Finding people with the right skillset is emphasised and the author concludes that the much sought after skill set to execute the Big Data agenda is present within the research industry.
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.
Strategic management of new products: ex-ante simulation and market segmentation
Jae Young Choi, Jungwoo Shin and Jongsu Lee, International Journal of Market Research, Vol. 55, No. 2, 2013, pp. 289-314
Among various methodologies for demand forecasting of new products, the random-coefficient discrete-choice model using stated preference data is considered to be effective because it reflects heterogeneity in consumer preference and enables the design of experiments in the absence of revealed-preference data.
Among various methodologies for demand forecasting of new products, the random-coefficient discrete-choice model using stated preference data is considered to be effective because it reflects heterogeneity in consumer preference and enables the design of experiments in the absence of revealed-preference data. Based on estimates drawn from consumer preference data by structural hierarchical Bayesian logit models, this study develops the overall, strategic, demand-side management for new products by combining market share simulation and a rigorous clustering methodology, the Gaussian mixture model. It then applies the process to the empirical case of electronic payment instruments.
What next: Preparing for the near future
David Smith, Warc Exclusive, MAP: Measuring Advertising Performance, March 2013
This future-themed presentation explores various "megatrends" including globalisation, the emerging middle class and an ageing population.
This future-themed presentation explores various "megatrends" including globalisation, the emerging middle class and an ageing population. It also identifies key technology and communication trends such as mobile devices and personalised engagement. 3D printing, behavioural science and big data are discussed. The presentation concludes by identifying some of the industries likely to be transformed by this new and so-called disruptive technology.
How Big Ideas trump Big Data at Fidelity
Geoffrey Precourt, Event Reports, IAB Annual Leadership Meeting, February 2013
In this article, Jim Speros, chief marketing officer at Fidelity, the investment group, talks about how the company balances Big Data and Big Ideas in its communications.
In this article, Jim Speros, chief marketing officer at Fidelity, the investment group, talks about how the company balances Big Data and Big Ideas in its communications. While noting the importance of the deluge of statistical information now available to brand owners thanks to the advent of various digital channels, Speros argues that intangibles and the importance of a strong narrative must not be ignored.
Nate Silver blows up Big Data at IAB Leadership confab
Geoffrey Precourt, Event Reports, IAB Annual Leadership Meeting, February 2013
In this report, Geoffrey Precourt, Warc's US Editor, discusses a keynote presentation given by Nate Silver, the celebrated statistician, blogger and author, at the Interactive Advertising Bureau's Thought Leadership conference.
In this report, Geoffrey Precourt, Warc's US Editor, discusses a keynote presentation given by Nate Silver, the celebrated statistician, blogger and author, at the Interactive Advertising Bureau's Thought Leadership conference. In his speech, Silver warned that big data "doesn't always produce big breakthroughs", but must be thoughtfully analysed and carefully measured against an organisation's internal beliefs and external realities. Otherwise, marketers may well be faced with a simple case of "garbage in, garbage out."
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.
Predicting elections: a 'Wisdom of Crowds' approach
Martin Boon, International Journal of Market Research, Vol. 54, No. 4, 2012, pp. 465-483
Opinion polls are the currency of politics. They are used by media organisations to evaluate the performance of governments, and by governments and political parties to test the policies that shape manifestos and reform agendas.
Opinion polls are the currency of politics. They are used by media organisations to evaluate the performance of governments, and by governments and political parties to test the policies that shape manifestos and reform agendas. But opinion polls all rely on one thing – asking people how they themselves intend to vote – and, too often, classical opinion research techniques fail to confront the issues that underpin inaccuracy. In the UK and in many other countries around the world, their performance over the past 20 years has ranged from excellent to disastrous. The ‘Wisdom of Crowds’ concept turns conventional predictions on their head. It assumes that any crowd that conforms to a core set of principles is capable of delivering a more accurate prediction than the smartest people within it. This paper tests this proposition within the context of actual elections in the UK, showing that the Wisdom of Crowds approach used by ICM Research at the 2010 general election would have produced the most accurate final pre-election prediction. It also shows that a Wisdom approach to regular vote intention tracking produces an interesting complement to classically conducted vote intention polls. Or, if one were to be bold, a competitor to them.
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