Introducing Marketing Research to Data Scientists

Most people working in data science have probably heard of marketing research but may be confused about what it really is. Marketing research, also known as market research, has been in existence in one form or another for at least a century. Definitions of it vary and no marketing researcher I know has in-depth experience with all of it.

It’s just too big a field, with many specializations and sub-specializations. Traditionally, it has often been divided into three categories: desk or secondary research; qualitative research; and quantitative research.

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Much has changed in the world of marketing in the past decade or so and that alone is a big subject. Suffice it to say that technology and globalization have had a huge impact on marketing and marketing research.

Secondary research is now much easier with the World Wide Web than it used to be, and social media has added another dimension to this important but often overlooked area of marketing research. I am a user of qualitative research and have had academic training in qualitative research methods but am no longer a doer of it. Qualitative and quantitative research have both become much more sophisticated in recent years.

Marketing science – also defined in numerous ways! – is my specialization and this overlaps considerably with data science. In fact, some of my earliest work in marketing research would today be called data science. Put simply, I am a methodologist but also a practitioner.

I help design research and analyze and interpret the results, often with multivariate statistics or machine learning tools of assorted types. An Analytics Toolbox gives a snapshot of the sorts of techniques I use as does my company website.

As I’ve mentioned, marketing research (which I’ll henceforth abbreviate as MR) is a very big field and marketing researchers come from all sorts of backgrounds and often are quite specialized in what they do. In this short article I’ll give you a quick overview of the types of MR I know best, which apologies to the many areas I omit!

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  • In Consumer Segmentation consumers or customers are either 1) statistically profiled with respect to a key target variable such as purchase frequency or 2) divided into clusters (segments) based on how similar they are to each other in terms of wants and needs and/or behaviors. Segmentation can be used either for targeting purposes or simply to learn more about a product or service category. Data can include consumer surveys, customer records or both, though historically surveys have been the main source in MR.
  • Marketing Mix Modeling (aka Market Response Modeling) is quite complex, but in a nutshell, it tries to find out how much bang a client is getting for their marketing buck and identify the optimal marketing mix. It can also be extended to Demand Forecasting, in which future sales or market share are forecasted under various marketing and/or economic scenarios.
  • Pricing Analysis is especially useful for products that are not yet on the market or for which historical data are lacking. Surveys are the data main source in these cases. It also can be conducted using existing price and sales data, and price is typically a variable in marketing mix modeling.
  • Choice Modeling (aka “conjoint”) is a versatile tool often used in pricing research and new product development. For example, respondents in a consumer survey might be shown a series of choice tasks with product descriptions and asked which product, if any, they would choose. Their responses are statistically analyzed with latent class or Bayesian multinomial logit models and the importance (“utility”) of specific features estimated. What if? simulations can be conducted to see which combinations of features – i.e., which hypothetical new product – would garner the highest preference share. The utilities can also be used as input into segmentation.
  • Key Driver Analysis is similar to choice modeling in that one application of it is to learn more about consumer priorities. It serves many kinds of objectives and there are many ways to conduct it but, in essence, we are trying to unravel the “causes” of one or more target variables such as purchase interest in a new product, liking for a TV ad, customer satisfaction or brand equity. Causal analysis is an immensely complex topic, even for scholars, and in key driver analysis we really are picking out the independent (predictor) variables most strongly associated with our target (dependent) variable. Many analytic methods are used, one being structural equation modeling.
  • Image and Positioning Research does not receive the buzz it formerly did but remains a crucial part of marketing research. Typically, brand or user image data from consumer surveys are mapped with one of several methods such as correspondence analysis to help us understand how brands or users of brands are perceived by consumers. Mapping is frequently combined with key driver analysis to see which image attributes and image dimensions are the ones clients should focus on most.
  • Data Mining and Predictive Analytics are well-known to data scientists, of course. It therefore may come as a surprise to many of you that they were little-known to most marketing researchers until quite recently. In Data Science and Marketing Research and Data Science and Analytics Demystified I briefly touch on their history in MR and how they fit into the bigger MR picture.

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The foregoing is a tiny snapshot of my particular corner of MR, and there is much more to MR than I’ve been able to include in this brief post. Nevertheless, I hope you’ve found it interesting and useful!

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