Once qualitative data has been collected, the market researcher is typically faced with a large amount that has to be analyzed and interpreted for the end-users of the market research.
Qualitative data analysis is the collection and analysis of non-numerical information through observation, interviews, focus groups or analysis of documents. These data are analyzed for meaning as they are collected.
There are a number of methods for interpreting the data you have collected. Of these methods, two are the most practical for small business marketing purposes—the comparative method and analytical induction method.
Constant Comparative Method
This qualitative data analysis method (also referred to as grounded theory) is a structured process in which researchers compare each new bit of data with data that has already been examined in a study.
For instance, if you research consumer conversations about your product or services, you would be able to pick out any relevant sentiments or feelings. As you find and read more, you can begin creating lists and categories of each idea, pattern or theme that you find.
Develop some questions you would like to answer, such as "What do customers think about my new product?" You assign each sentiment a code that you develop for each sentiment and situation you find.
You'll work to identify the different categories and properties of each piece of information you separate from the content. Once you assign a code to each piece of information, you attempt to identify subcategories and relationships between the data.
Once you have worked through all of your information and assigned it to subcategories and developed the relationships between them, you develop categories for your data, e.g. someone purchased an item and feels unsatisfied.
Over time, as you continue to collect data, you compare new to old, again attempting to develop relationships between new and old data.
You continue this comparison until you have a good working theory of the meaning of the data. The meaning(s) you pull from the data are generally referred to as themes.
As you continue to develop the information you receive, you may be able to establish a correlation between some of the data, such as a theme of an increase in negative sentiments following the introduction of a new service.
The new service may not be the cause of the negative sentiment, however. Remember, correlation is not causation. Correlation simply means that there is in some way a relationship.
Analytical induction is a less intensive analysis than the constant comparative method. This method is similar to the way in which we interpret events on a daily basis.
If an event occurs, you hypothesize that it occurred for a certain reason.
If you come across another similar event, you adjust the hypothesis that discusses what happened. As you come across more similar events, you revise your hypothesis until eventually all the events are included in one hypothesis.
All the events, in this case, should be a similar occurrence. For instance, they should all revolve around one product or around one retail outlet that sells your product.
Presentation of Findings
The way that the data analysis findings or outcomes are presented can make the difference between research that is used or not. A rule of thumb is to present data in a way that will be understandable and usable to the least sophisticated people who will receive the data analysis findings.
Qualitative content analysis can be presented in tables and matrices. It can be presented as a simple sentence or a paper which analyzes the events and outlines the causes.
The constant comparative method presentation is focused on revealing the themes that have emerged from the data. While visual displays of data may be used, the findings are typically tied to specific excerpts from the data set, which explicitly illustrate the themes. These excerpts are including in the narrative discussion of the results section of the research manuscript and/or article.
The analysis induction method can also be used to determine themes, but it is not as specific as the comparative method for developing theories about events.
Choose Your Analysis Method to Fit the Data Collected
Tailoring the data analysis method to the data that has been collected and to the research questions and ultimate purpose results in stronger insights that can be used with the different methods of analysis.
If your surveys or research have resulted in close-ended questions with yes, no, maybe, or similar answers you might consider using a quantitative analysis approach. Qualitative methods are used to evaluate and create themes in otherwise immeasurable data.