Businesses need to regularly analyze their processes, solutions, customer experience, and other metrics in order to improve profits and services. While some analyses are objective, such as numbers in a profit/loss statement, others are necessarily subjective. Qualitative analysis looks at non-quantitative data, like labor relations, customer feedback, industry cycles, research and development, and more.
Qualitative analysis is often used alongside quantitative analysis to determine a company’s investment potential. Here’s an overview of qualitative analysis, plus a step-by-step guide to conduct your own.
What is a qualitative analysis?
Qualitative analysis is a subjective determination based on non-quantifiable data, like behavior and company culture. Unlike quantitative analysis, it’s more difficult to collect and measure this kind of data. While not impossible, it’s far easier to write programs to analyze quantitative data than qualitative data.
The intangible data in a qualitative analysis can come through customer feedback, testimonials, panel groups, interviews, document analysis, and other common sources of public or internal sentiment. The data might deal with issues like customer satisfaction, perception of a brand, managerial effectiveness, competitive advantage, and other input not easily quantified. Qualitative analysis is often used by social scientists, but it can be a great boon to companies, too.
How to perform your own qualitative business analysis
While qualitative business analysis can be extremely complex, there are five basic steps to master. The key to success is knowing exactly what kind of question you’re researching, and the scope of the issue. This will help you refine your queries and classifications to get the precise answers you need.
If you’re curious about what a qualitative analysis example might look like, here’s an overview of how the process works.
Research and gather data
Like quantitative and sentiment analysis, the first step is to research and gather your data. Companies often use customer feedback, interviews, surveys, chatbot conversations, and focus groups to find out what their target audience thinks. Larger companies are more likely to have access to customer reviews, social media mentions, and other opinion sources. This data can often be exported to feedback analysis software or APIs.
Organize your data
After the data is gathered, you’ll need to organize it. This allows you to access all the data in one place and analyze it consistently. If you’re organizing feedback manually, spreadsheets can be an easy solution. However, that often leads to different teams collecting and organizing data in different spreadsheets, making it more difficult to review all feedback at once and import it into software. Storing the data in a central database helps avoid this problem.
There are also computer-assisted analytics available. It’s not as labor intensive as manual data organization, but you will still need to add the data, create themes, and perform your own analysis.
Finally, feedback repositories and feedback analytics platforms can automate these processes. Repositories help keep your data in one place, allowing you to tag it and search for it at will. Analytics platforms, in turn, automate sentiment analysis and qualitative analysis, based on themes. These can also be used with manually-created data spreadsheets.
Code the data
Once you’ve gathered and organized your qualitative data, the next step is to code the data. This is the process of labeling and organizing the data to discover broad themes—and the relationship between those themes. Typically, this is done by taking small samples of data to discover themes, then labeling them appropriately.
When that’s finished, you can move on to larger samples of data. Feedback analytics programs will do this process for you.
In practice, coding is simply assigning a theme to a particular set of key words or phrases. For example, a review stating “I love my new backpack” could be coded as “positive product feedback.” It could also be coded as part of a specific product line or model.
Whether you code manually or have a software platform automate the process, the themes you choose depend entirely on the question you’re trying to answer.
Next, it’s time to analyze the data you’ve gathered, organized, and coded. This will provide insight into the data you’ve accumulated. To do so, you’ll go through the codes in the data and create your own correlations. For example, if you have an inordinate amount of “poor customer service” feedback, you’ll notice that code has more data in it than other codes.
In some cases, your coding might be too broad. “Poor customer service” can encompass subcodes, such as “long phone wait times” or “disgruntled employees.” As you sort through your data, pay attention to whether each insight is discrete and whether you have sufficient data to support it.
While you can manually code your data, many integrations and platforms will provide insights for you. Some of them are capable of providing visualizations, which is key for the final step of qualitative analysis.
Report your data
Finally, you’ll need to take your data and report upon it. What narrative does the data convey, and which methods will make the most impact? Depending on the type of qualitative analysis you’re performing, and the story you want to tell, there are many methods to report data.
For instance, graphs, visualization software, PowerPoint, feedback analytics platforms, and even written analysis will help get the message across to investors, internal teams, and more.
Gather qualitative analysis data with Voiceform
Great analysis starts with good data. Voiceform is an innovative way to collect feedback and customer sentiment through audio and text responses. Customers who may be reluctant to write out their thoughts can provide feedback in more convenient ways.
Voiceform not only gathers multimedia feedback, but it also transcribes the information for easy exporting and analysis. When you’re ready, the platform provides rich quantitative and qualitative analyses to monitor your brand’s reception.