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Solid as a MROC: How text analytics helps to get solid results out of research communities
Istvan Hajnal, Jo Steyaert and Steven Deketelaere, ESOMAR, 3D Digital Dimensions, Amsterdam, November 2012
More and more research agencies offer Market Research Online Community (MROC) services alongside other methods, but efficiently coping with the huge amount of text that some MROCs produce often remains a challenge.
More and more research agencies offer Market Research Online Community (MROC) services alongside other methods, but efficiently coping with the huge amount of text that some MROCs produce often remains a challenge. This paper describes research looking at how text analytics can be a helpful tool for a community moderator and for a qualitative researcher in dealing with these problems. More specifically, it explores how Cluster Analysis, ConceptNet, WordNet and Latent Semantic Analysis (LSA) can be deployed in the context of Community Research. A case is presented in which these techniques were used to improve efficiency of deploying a MROC, both in terms of improving monitoring and in terms of supporting qualitative researchers in the reporting phase, leading to more solid results.
Point of view: Too late for techno phobia
Stan Sthanunanthan, Admap, November 2012, pp. 7-7
The role of technology in the insights industry over time can be separated into three phases: Enabler, Collector and Generator.
The role of technology in the insights industry over time can be separated into three phases: Enabler, Collector and Generator. The adoption of technology as an enabler allowed the industry to raise the bar in terms of analytics. It allowed the industry to retain its existing business model, which in turn helped it to grow rapidly as sample sizes and throughput increased. As the internet grew, the industry came to view it as a collector of information - a phase driven by the ability to ask people questions over the internet and get data faster and cheaper. This phase changed the game and resulted in the industry being forced to answer a lot of tough questions. The transformation of mobile phones and the growth in social media has led to technology playing a more central role as a generator of insights. This role is here to stay and the insights industry needs to embrace it fully.
Multilingual elite-interviews and software-based analysis: problems and solutions based on CAQDAS
Rudolf R. Sinkovics and Elfriede Penz, International Journal of Market Research, Vol. 53, No. 5, 2011, pp. 705-724
Qualitative international research is increasingly popular in marketing, management and business practice.
Qualitative international research is increasingly popular in marketing, management and business practice. Cultural dimensions, most importantly language, play a central role in this research context. The importance of language in the context of questionnaire design and international data gathering has long been stressed in various sources (Pike 1966; Brislin 1970; Piekkari & Welch 2004). However, the practice of qualitative data collection and analysis has not been addressed sufficiently, although new and innovative software-based tools are available to help these efforts. This paper deals with methodological and practical issues in analysing qualitative interviews with corporate elites. We illustrate conceptual challenges in setting up qualitative projects that build on interviewing corporate elites and address practical implementation issues in terms of multilingual coding, node creation and theory building by means of computer-assisted qualitative data analysis software (CAQDAS). To this end a specific empirical example will be used.
Statistical alchemy - the misuse of factor scores in linear regression
Cataldo Zuccaro, International Journal of Market Research, Vol. 52, No. 4, 2010, pp. 511-531
Linear regression and factor analysis are probably the most employed statistical techniques in market research.
Linear regression and factor analysis are probably the most employed statistical techniques in market research. During the last several decades these two techniques have been employed jointly by market researchers in modelling a wide spectrum of behavioural and psychological phenomena. More specifically, market researchers have employed factor scores as predictor (independent) variables to model the ‘variability’ of a variety of constructs (latent variables). Many of these studies can be classified as ‘modified psychometric investigations’ of the link between ‘supposedly latent structures’ and a wide variety of manifest and latent dependent (criterion) variables. Unfortunately, this standard market research practice is inappropriate and can lead to faulty analyses and recommendations; in addition, it is still employed today by academicians and professional market researchers. The objective of this paper is to demonstrate, on both mathematical and ontological grounds, the inappropriateness of the exercise and to recommend more robust practices in attempting to model the relationships between a set of latent variables measured through factor structures.
Perspectives on data mining
Niall M. Adams, International Journal of Market Research, Vol. 52, No. 1, 2010, pp. 11-20
As a data analysis technology, data mining has matured to the extent that there are now a number of sophisticated commercial software packages available.
As a data analysis technology, data mining has matured to the extent that there are now a number of sophisticated commercial software packages available. The purpose of this article is to explore what data mining has become, its relationship to statistics and its relevance in market research.
Technology Futures: Perspectives on How Technology Will Transform the Market Research of Tomorrow
Michael Denitto, Leon Walsh and Philip Martin (edited by Tim Macer), Market Research Society, Annual Conference, 2009
This paper forecasts how technology will affect market research in the future. The paper is in two parts, by different authors.
This paper forecasts how technology will affect market research in the future. The paper is in two parts, by different authors. 1) Software as a service (SaaS), which depends on high-powered computers in remote data centres and the internet; users do not buy the software but lease it and use on demand. Differences between SaaS and conventional desktop software are described, from the point of view of the end user and the service provider. Factors contributing to the growth of SaaS and its advantages for market research are discussed, also security and reliability issues. The difference between SaaS and ASP (Application Service Provider) is explained. Argues that SaaS solutions are by nature more efficient, more flexible, more adaptable, and more cost-effective than their traditional desktop equivalents. 2) Qualitative data analysis: there has been an explosion of unstructured data from multiple sources; new technologies are needed to handle this and ensure its quality. Discussed are: video analysis technologies (e.g. facial and emotion recognition); textual analysis technologies which sift and filter information from multiple sources (emails, blogs etc.); mobile and SMS research methods, including future applications such as WAP. Advantages to researchers of adopting these new technologies are fully covered.
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.
Score a bull’s-eye
Amy Syracuse, ANA Magazine, February 2007
Marketing has often been seen as the preserve of creatives working on instinct rather than calculation.
Marketing has often been seen as the preserve of creatives working on instinct rather than calculation. As the pressures on companies and brands increase, however, a more scientific approach may be required to gain an insight into consumers and establishing what their future desires may be. In this climate, the use of business intelligence software has grown, and it can aid marketers to understand their customers, campaigns and competitors.
Beyond The Research Repository - Using next generation web technology to maximize the return from your market research investment
Chris Forbes and Daryl Maloney McCall, ESOMAR, Asia Pacific Conference, Tokyo, March 2005
This paper draws on the authors’ experience in implementing client side, market research department (MRD) portals for more than 20 organizations around the globe with research budgets ranging from $1 million to $100 million.
This paper draws on the authors’ experience in implementing client side, market research department (MRD) portals for more than 20 organizations around the globe with research budgets ranging from $1 million to $100 million. It outlines the problems many organizations have faced in sustaining the use of their MRD portals, and how, by fully exploiting second-generation web technology, these problems can be overcome to help MRDs maximise the return from their investment in market research. The paper also includes pointers to the application of emerging web technologies that are likely to affect MRDs.
Juan Carlos Aguado, Monica Casabayo, Lluis Martinez-Ribes and Nuria Agell, ESOMAR, Marketing Transformation Congress, Rome, Sept 2001, pp. 449-467
Customers are increasingly demanding, and successful companies need to design and introduce new ways to offer customer value.
Customers are increasingly demanding, and successful companies need to design and introduce new ways to offer customer value. However, the process is not complete until they design control systems, provide a support decision tool able to identify and distinguish customer behaviour profiles according to their loyalty, and help marketers to readapt relationship marketing strategies in order to increase efficiency. LAMDA (Learning Algorithm Machine for Data Analysis), an artificial intelligence technique software tool enabling forecasting and identification of customer behaviour, is based on a self-learning classifying technique that relies on the generalizing power of Fuzzy Logic and the interpolation capability of logical hybrid connectives. This paper specifically examines the efficiency of the LAMDA classifier in identifying and distinguishing between the various degrees of customer loyalty. The study carried out in this project is based on data gathered from the customer loyalty cards of a Spanish grocer, Supermercats Pujol, S.A.
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