Qualitative Marketing Research Analysis Methods

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  • 0:03 Turning Feelings into Numbers
  • 2:44 Coding to Analyze Data
  • 4:00 Interpreting Qualitative Data
  • 4:53 Lesson Summary
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Lesson Transcript
Instructor: David Whitsett

David has taught computer applications, computer fundamentals, computer networking, and marketing at the college level. He has a MBA in marketing.

When a company asks someone for an opinion on a product or an issue, taking those thoughts and feelings and turning them into actionable data can be a complex process. In this lesson, we'll examine analysis methods for qualitative marketing data.

Turning Feelings into Numbers

Qualitative marketing research refers to collecting the thoughts, feelings, and opinions of your customers regarding your existing products or a proposed product. You're looking for insights to understand the psychology of their behaviors and to get a better understanding of their needs. Qualitative data can be collected through interviews, focus groups, or observation, or watching customers interact with a product.

The problem is, how do you turn thoughts or feelings into data points that you can analyze with mathematics? Let's use a hypothetical focus group meeting as an example. You gather a panel of condo owners for a discussion about adding a nighttime security guard. Here are the steps you would go through.

During the Meeting

You can make note of common themes or patterns, big deviations or weird outliers, and any interesting stories that come out. You might also be able to take what you heard and come up with some new questions that you can ask in the next session. Making a video or audio recording of the session will allow you to capture non-verbal communication (expressions, gestures, posture, voice inflection), which will aid in the interpretation following the meeting.

Process & Record Quickly

After the session, record your thoughts and impressions as quickly as possible. It may help to develop a standard form that includes common data collection points. Immediately begin looking for themes and patterns (i.e. frequent use of the word 'secure'), and information that is usable and relevant.

Data Reduction

Qualitative research generates a lot of data, but not all of it is meaningful. For example, if our panel goes on a tangent and starts discussing landscaping, that's not useful. You have to sift through to find the nuggets of wisdom. Look at the recurring themes from the discussion and evaluate them against your original research questions.

Data Grouping

After you perform some data reduction, it's time to group your data. The core of analyzing the data is to group the themes and patterns. You can do this through content analysis and thematic analysis. One or both of these methods may be used depending on the nature of the questions asked and the type of data gathered.

Content analysis involves coding words or phrases that occur in the raw data and assigning them either a word or a symbol as a label. You then look for patterns and interpret the meaning. You can look at the data from a descriptive standpoint (what was said) and from an interpretive standpoint (what was meant by what was said). Thematic analysis is grouping themes and then comparing those groups against the objectives of the research question.

Coding to Analyze Data

Coding is the nucleus of the analysis; everything flows from it. When the coding is done, you can then begin to summarize and synthesize the data. The creation of the labels and the application of those labels to the data requires interpretation on the part of the researcher. Because of the way the process works, qualitative research is thought to use inductive reasoning, which generates a theory based on the data set.

The labels or codes that are used to categorize the data can be created in advance based on the framework of research questions. These are known as a priori codes. Some codes that you didn't account for may become apparent during the evaluation of the data - these are known as emergent codes.

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