Quantifying Qualitative Data Used in Customer Analytics
Qualitative data is often helpful by itself to explain the “why” behind low satisfaction rates, higher sales, or high customer turnover rates. For example, if you see customers complaining that they don’t know what the total price of their order is in local currency or how to change the currency in a website shopping cart, you know what you can fix.
Comments provide immediate insight and potentially action (improve the currency display on checkout).
Always take the time to sort and count customer comments. Just because customer information is qualitative doesn’t mean you can’t use quantitative methods to interpret qualitative data to make better decisions. If you find that a significant portion of comments revolve around a specific issue (say 20% of the comments center around currency issues), you’ve just turned your qualitative data into quantitative data.
Quantifying the frequency of customer comments helps you understand how prevalent a certain attitude may be in your entire customer population. Some examples of open-ended responses (often called verbatim responses) are common for things such as:
Reasons why customers are not recommending your product
Observations from customers using a product at their workplace
Product complaints in customer service calls
Here are three steps you can follow to turn qualitative data into quantitative data to estimate the prevalence of responses:
Group similar comments and behaviors.
Customers will use their own words to describe how they feel. Group similar phrases, behaviors, or concepts together. Some comments will be virtually identical and grouped easily. Others will differ and require additional layers of grouping.
There can be a high amount of variability between people grouping items. If possible, consider having multiple people independently categorize comments.
Count the frequency.
Count the number that appear in a category and the total number. If 5 out of 50 comments are related to price, for example, then an estimate of how often price is a concern is 10% (5/50).
Estimate the frequency.
You can estimate how common an issue is with the entire customer base by using a confidence interval.
Collecting data from a sample of customers costs a lot less and takes a lot less time than measuring every customer. The level of precision you get from even a small sample is usually sufficient to make decisions from the data.