Why Coding Customer Feedback Is Essential and How It Works
For marketers, online reviews and mentions on social media are essential. Not only is word-of-mouth marketing one of the most effective strategies for getting new customers (some marketers believe it is the most effective!), these sources are also invaluable for collecting real-life, freeform customer feedback about how your business is succeeding, and more importantly, areas that need to be improved.
On the surface, it sounds pretty simple. Comb through online review sites and comments on your social media pages, or even send out a customer feedback survey, and make improvements based on what people are saying.
The tricky part is when you have to analyze this type of data on a large scale, which, most of the time, is going to be the case. No business owner is going to make fundamental changes to their strategies based on just a few negative reviews.
Think about it; how can you accurately compare different open-ended responses like “The product’s customer support team was excellent, just wish I didn’t have to call in the first place” to “This product works well enough for me but it’s a bit pricey”? The answer is to take this qualitative customer feedback and turn it into quantitative data. By doing this, you can make sure that you’re getting a full and accurate picture of your customer data and make the right decisions for your business.
What Is Qualitative Data Coding?
Don’t worry, this isn’t the computer programming type of coding. Coding qualitative data just means assign categories and numerical tags to each piece of data, so that it can be more easily analyzed. If your data analysts (or even just you and a spreadsheet) are trying to find out what a certain demographic thinks about your new product, ranking responses on a scale of 1-5 instead of reading each and every review will allow for deeper data manipulation and analysis.
Where It All Starts: Inductive vs Deductive Coding
When you and your team are starting to analyze a set of customer feedback, you’ll need to decide whether you want to take an inductive or deductive approach. If this is your very first time analyzing qualitative data, you’ll likely want to take an inductive approach. This means that you don’t already have set measurements for the data, and are going to figure this out as you go.
On the other side is deductive coding. This method is more useful once you’re on to subsequent rounds of data coding. Deductive coding uses predetermined measurements to sort the data, measurements that you probably decide on in your first round of inductive coding.
For example, say you send out a customer feedback survey asking about your new chatbot. The first time you go through this data, you determine that a scale of 1-10 measuring overall customer satisfaction will work best for your purposes. Then, if you send out another survey the next month to new customers, you can continue to use this scale to make sure the data stays uniform.
3 Steps for Coding Qualitative Data
For these three steps, let’s say that this is your first time working on this type of project so you don’t already have a set of measurements to use.
Step 1: Assign Categories
What exactly about your business are you looking to analyze feedback on? For a product-based company, your categories could be customer service satisfaction, product quality, and product price. Or if you have several similar products, your categories could look something like product No. 1 quality, product No. 1 price, product No. 2 quality, product No. 2 price, and so on. Choose several overarching categories that make the most sense for your company.
Step 2: Assign Sentiments
For the purpose of this article, let’s pretend that you’re doing your data coding in a spreadsheet like Google Sheets or Excel. Once you’ve created tabs for each of your categories and moved each piece of feedback, you’ll want to create a second column next to each of them to assign sentiments. This is where you’ll start to utilize measurements like a scale of 1-5 or unsatisfied, neutral, satisfied to tag each response.
Step 3: Put Them Both Together
After you’ve assigned categories and sentiments to each customer review, you’ll need to create another set of tabs to break them up further. These should each be labeled “Category + Sentiment,” for example “Customer Service + ⅖.” Move each of the reviews into the corresponding new tabs.
From here, you’ll have a clear, numerical view of your data and your team can analyze it more accurately. Maybe you notice that out of 1,000 survey responses, 60% fall into the “negative or very negative” tabs. Or if there is a larger-than-expected number of responses in another category, you could dig into the demographic data to see if there are certain customer ages or locations that are having a particular issue.
Using AI for Qualitative Data Coding
After reading through these basic steps, it’s clear that coding your customer feedback is a necessary yet time-consuming process. Because of this, many companies are turning to AI-powered tools to conduct this process automatically. Using these types of software, businesses can have bots comb through and sort data instead of employees, based on either predetermined measurements or by allowing the bots to come up with their own metrics as they go.
Whichever way you decide to go about it, coding qualitative customer feedback is an essential process for making smart, data-backed decisions for your business.
For helpful visual examples of this process along with extra pointers, take a look at the graphic below: