Examples Of Data Mining Vs. Traditional Marketing Research

Traditional marketing research often involves assessing the overall market for a good or service, surveying consumers about their likes and dislikes, and conducting focus groups to gauge consumer responses to a new product. The growth of information technology has transformed market research, with a growing number of analysts learning about consumer preferences and buying habits by mining massive sets of quantitative data and employing complex algorithms to uncover patterns and correlations that enable more effective marketing.

Data Mining Features
Data mining uses statistical techniques to discover correlations between different factors and variables in large data sets, according to Yale University Professor Ian Ayres, author of “Super Crunchers.” These data sets are often measured in terabytes, a terabyte being equivalent to 1,000 gigabytes. Data mining often gives businesses enormous amounts of information about their customers’ behaviors and buying habits, enabling them to more effectively market their goods.

Data Mining Examples
Ayres cited online retailer Amazon.com’s feature that tells a potential customer that people who like one particular product also like certain other items as an example of marketing through data mining. In another example, credit card issuer Capital One generates for its customer service representatives a list of products and services that a consumer is likely to buy based on characteristics of the customers’ credit card accounts.

Traditional Market Research
While data mining emphasizes extracting predictive information about customers and sales from large databases, traditional marketing research focuses on identifying factors that influence the buying decisions of households and organizations. Relevant data is then collected, often through sales data, surveys and focus groups, according to Professor Roger A. Kerin, author of the textbook “Marketing.” Traditional market researchers identify an opportunity, collect the needed information, then formulate an appropriate sales strategy. Data mining relies on information that is already available.

Considerations
Data mining has transformed the market research field, fueling growth in job opportunities in this profession. The U.S. Bureau of Labor Statistics has projected above average growth in market research job opportunities, with the best opportunities existing for men and women with advanced degrees and strong quantitative skills that enable them to extract meaning from data.

References (3)
“Super Crunchers”; Ian Ayres; 2007
“Marketing (8th ed.)”; Roger A. Kerin; 2006
Bureau of Labor Statistics: Market and Survey Researchers
Resources (1)
University of California, Los Angeles: Data Mining: What is Data Mining?
About the Author
Shane Hall is a writer and research analyst with more than 20 years of experience. His work has appeared in “Brookings Papers on Education Policy,” “Population and Development” and various Texas newspapers. Hall has a Doctor of Philosophy in political economy and is a former college instructor of economics and political science.

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6 thoughts on “Examples Of Data Mining Vs. Traditional Marketing Research

  1. One example I thought of when reading this blog about data mining versus traditional marketing research was network-based marketing. Network-based marketing is the marketing techniques utilising information about consumer to consumer interaction (Hill, Provost & Volinsky 2006). Hill, Provost and Volinsky (2006) completed an empirical study on how network-based marketing can improving on traditional marketing techniques. With the existence of mass online social networking, data mining must produce information about consumer to consumer interaction unlike anything marketers have ever had available to them before.

    Reference:
    Hill, S, Provost, F, Volinsky, C 2006, ‘Network-Based Marketing: Identifying Likely Adopters via Consumer Networks’, Statistical Science, vol. 21, iss. 2, pp. 256-276, retrieved 16 April 2015, .

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  2. Nice write up . The examples you have given is not a broadly discussed.. As there are many companies which do research and tracks customer buying behaviour and sell them to required companies. .
    Data mining and Traditional marketing research plays a important role in any marketing campaign or design of products etc.. Also Data mining is widely used nowadays ..

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  3. Good post. The move away from the traditional methods of marketing research such as focus groups to data mining is changing the way we view the customer. The data mining gives more up to date information and allows companies to react more readily to changing market conditions. The downside of this is that some of the more traditional methods allows questions and interactions with the end users (more emotional understanding), whereas the data mining is more of a representation of the “facts” (less emotional understanding).

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  4. Data mining is usually used in mass scale products where a pridictive pattern is expected of the goods and is usually a tiresome job, one of my friend works with major grocery supermarket in Australia and she told that the massive amount of data some time leads to doubtfull statistics and sometimes have to go through the ananlsis over-n-over again and still no conclusion can be made than the data is disregarded. Whereas traditional approach is more of quality reasearch and may lead to instant conclusions but it also has it cons, for example the person may not be honest about his preferrences or his statements when being interviewed and the information may be biased.

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  5. Useful blog.Before read your blog, I’m even don’t know what is data mining. So from your blog I understand that data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. The data is very useful and avoid bias or potential of error in research. Compared with traditional method that it’s more of quality and validity but it also has disadvantages.

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  6. I believe both data mining and traditional market research methods have their place. Without doubt there is an abundance of data available to organisations to determine customer preferences, trends and activities. This data tells an organisation the what, where and when. As we use more technology there will certainly be more opportunities for people to work in the field of data mining and analysis.

    Traditional market research methods such as surveys and focus groups have the added benefit of telling marketers the “why.” Sometimes the why might not be needed and therefore data mining may be enough. However I do not think data can answer all of the questions a market researcher needs to answer to make effective marketing decisions.

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