Market Research 101: Data Analysis
Data analysis in a market research project is the stage when qualitative data, quantitative data, or a mixture of both, is brought together and scrutinized in order to draw conclusions based on the data. The market research process consists of six steps. They are as follows:
Step 1 - Articulate the research problem and objectives: Market research begins with a definition of the problem to be solved or the question to be answered. Typically, there are several alternative approaches that can be used to conduct the market research.
Step 2 - Develop the overall research plan: The task of this stage is to determine the most efficacious way to collect the necessary information.
Step 3 – Collect the data or information: At this point, you have to consider how you're going to obtain the information (meaning, how participants are going to be contacted whether it's surveys, phone calls, one-on-one interviews, etc.).
Step 4 – Analyze the data or information: Collecting volumes of information can be overwhelming. At this stage, you need to organize the data and weed out what is not crucial.
Step 5 – Present or disseminate the findings: From knowing your audience to knowing what findings are actionable, before releasing your findings, you need to know which findings you want to disseminate.
Step 6 – Use the findings to make the decision: Because external consumers of market research may not use the findings accurately, appropriately, or completely, you need to consider the attributes of good market research.
Quantitative Market Research Decision Support Tool
The following statistical methods will help you get from A to Z in the research process.
- Multiple Regression - This statistical procedure is used to estimate the equation with the best fit for explaining how the value of an dependent variable changes as the values of a number of independent variables shifts. 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. In a situation where many variables are correlated, factor analysis identifies which relations are strongest. 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 just a few variables.
- Cluster Analysis - This statistical procedure is used to separate objects into specific groups that are mutually exclusive but also relatively homogeneous in constitution. This process is similar to what occurs in market segmentation when the market researcher is interested in the similarities that facilitate grouping consumers into segments and 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, the inferred utility functions of each attribute, and 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 the 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 the strength of roast, number of flavored and specialty versions, distribution channels, and packaging options.