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Market Research 101 - Data Analysis

Step 4. Analyze the Data

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Consider Machine Learning for Data Analysis

Courtesy, Conrady Applied Science, LLC -- the North American Partner of Bayesia S.A.S. &copy:

The market research process consists of six discrete stages or steps. They are as follows:

 

  • Step 1 - Articulate the research problem and objectives
  • Step 2 - Develop the overall research plan
  • Step 3 – Collect the data or information
  • Step 4 – Analyze the data or information
  • Step 5 – Present or disseminate the findings
  • Step 6 – Use the findings to make the decision

 

In the market research process, the fourth step is: Analyze the Data or Information.

The amount of data that can be collected and assembled in a market research study can be astronomical. Data organization and data reduction are two very important aspects of data analysis that is seldom highlighted. Yet, these steps are crucial to the ability to make sense out of data and to the ability to make cogent and insightful data interpretation. An impressive array of methods for data organization and data reduction are available.

A market researcher may tabulate data or compile frequency distributions. The means or averages and other measures of dispersion are common ways of analyzing data for which frequency distributions are available. Very often, advanced statistics and decision models are used to maximize the information that can be extracted from research data. The following section provides a brief description of several commonly used statistical tools, decision support models, and optimization routines

 

Quantitative Market Research Decision Support Tools

Statistical Methods

  • Multiple Regression - This statistical procedure is used to estimate the equation with the best fit for explaining how the value of a dependent variable changes as the values of a number of independent variables shift. A simple market research example is the estimation of the best fit for advertising by looking at how sales revenue (the dependent variable) changes in relation to expenditures on advertising, placement of ads, and timing of ads.
  • Discriminant Analysis - This statistical technique is used to for classification of people, products, or other tangibles into two or more categories. Market research can make use of discriminant analyses in a number of ways. One simple example is to distinguish what advertising channels are most effective for different types of products.
  • Factor Analysis - This statistical method is used to determine which are the strongest underlying dimensions of a larger set of variables that are inter-correlated. Where many variables are correlated, factor analysis identifies which relations are strongest. A Using factor analysis, a market researcher who wants to know what combination of variables or factors are most appealing to a particular type of consumer can use factor analysis to reduce the data down to a few variables are most appealing to consumers.
  • Cluster Analysis - This statistical procedure is used to separate objects into a specific number of groups that are mutually exclusive but that are also relatively homogeneous in constitution. This process is similar to what occurs in market segmentation where the market researcher is interested in the similarities that facilitate grouping consumers into segments and is also interested in the attributes that make the market segments distinct.
  • Conjoint Analysis - This statistical method is used to unpack the preferences of consumers with regard to different marketing offers. Two dimensions are of interest to the market researcher in conjoint analysis: (1) The inferred utility functions of each attribute, and (2) the relative importance of the preferred attributes to the consumers.
  • Multidimensional Scaling - This category represents a constellation of techniques used to produce perceptual maps of competing brands or products. For instance, in multidimensional scaling, brands are shown in a space of attributes in which distance between the brands represents dissimilarity. An example of multidimensional scaling in market research would show the manufacturers of single serving coffee in the form of "K-cups." The different K-cup brands would be arrayed in the multidimensional space by attributes such as strength of roast, number of flavored and specialty versions, distribution channels, and packaging options.

 

You can explore more data reduction and decision support models that are used in step four of the market research process. Or you can move on to Step 5. Present the Findings.

Sources:

Kotler, P. (2003). Marketing Management (11th ed.). Upper Saddle River, NJ: Pearson Education, Inc., Prentice Hall.

Lehmann, D. R. Gupta, S., and Seckel, J. (1997). Market Research. Reading, MA: Addison-Wesley.

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