How to Analyze Customer Feedback Data
Customers leave feedback more than ever before, and businesses collect that feedback for various purposes. But unfortunately, a majority of businesses don’t properly analyze the data from the feedback they collect, which makes the data practically useless.
Customer feedback analysis allows you to gain insight from the data you collected. But if you don’t know how to do it, at a first glance it could look like a difficult and time-consuming task. However, it’s a straightforward process once you learn how to analyze feedback data.
And this is why we gathered all of the information you need on this topic.
What Is Customer Feedback Data Analysis?
As you can probably guess from the name, customer feedback data analysis is the process of analyzing the data from the feedback you get from your customers.
But that’s only an oversimplified explanation. To understand customer feedback analysis, you need to know it happens in two stages: micro and macro.
In the micro stage, you figure out how to tag each data piece and perform the tagging as accurately as possible. And in the macro stage, you discover what all of that tagged customer feedback data means for your business.
Why Is Customer Feedback Analysis Important?
Organizations receive a vast amount of feedback daily, and transforming the data from that feedback into actionable insights can be overwhelming. Even with rich data, the analysis is still not compelling enough because companies don’t know how to analyze feedback data correctly.
If you’re dealing with this issue, there are a lot of things that might be going wrong. Your teams might be receiving poor data reports, you might not have a sound tagging system, or you’re simply focusing on the wrong area of customer feedback.
Whatever issue you’re dealing with, you need to find a way to solve it and learn how to evaluate client feedback properly. Only then you will understand your customers better. Your customers always tell you what they need and expect from you, and it’s up to you to listen.
The Most Effective Ways to Analyze Customer Feedback Data
You’re probably wondering how to analyze customer feedback to ensure you’re getting great benefits out of the customer feedback analysis. In this process, there are five stages to go through.
1. Collate the data in a single place
Before starting with any sort of analysis, you need to ensure all the data you collected is in a single place. And make sure to collect all of the feedback data you have access to, even if it’s incomplete.
2. Categorize the feedback
Now that all the feedback is in one place, it’s time to organize and divide it into categories and sub-categories. When you categorize the feedback, it’s easier to see the bigger picture and understand what’s going on.
You can categorize the feedback by type or theme. For instance, you can start by sorting feedback into positive, negative, and neutral, and then move on from there. Your sub-categories can be based on type or theme and include factors such as features, pricing, sentiment, and customer service, just to name a few.
3. Code the feedback data
The next step is to start reading through the data and carefully create feedback codes for each row of data. Learning how to analyze customer feedback also includes learning how to code that data. And the first thing you need to know is that each code relates to the product that received the feedback.
There are also some analysis codes for feature requests you could use, such as:
- Attaching complex HTML to some tasks
- Adding and removing teammates from screens
- Assigning one task to multiple clients
4. Scan for patterns
After you coded the data, it’s time to calculate how much feedback each code has. The easiest way to scan for patterns is to sort the feedback data based on the theme, type, and code, and then highlight the repeating patterns. When you do this, you’ll be able to identify the most common type of feedback and notice specific patterns.
5. Use data analysis automated tools
While it’s possible to learn how to evaluate client feedback without any tools, it’s still easier and much more efficient to use data analysis automated tools. These tools use machine learning for customer feedback analysis.
Here are a few you can try:
- Zoho Analytics – A tool that comes with multiple visualization options and a custom-themed dashboard.
- Adverity – A data-driven marketing analytics tool that eliminates manual data collection and reveals actionable insights.
- Looker – A cloud-based platform that comes with a role assignment, drag-and-drop for elements, and accurate charts that list the data in great detail.
In case you’re using online customer feedback surveys for gathering data, survey makers such as 123FormBuilder come with built-in reports and graphs. This makes it easier to unveil trends and high-level insights from your customers’ feedback without having to code anything.
What to Do with Customer Feedback Data Analysis Results?
Now that you know how to analyze feedback data, it’s time to learn what you can do with the results of your analysis.
There are three essential stages:
- Finding the root cause. Finding a root cause isn’t necessary for positive reviews, but it is for neutral and negative ones. Look for repeating patterns in those reviews and see if customers are complaining about similar issues.
- Planning actions. Once you find the root cause, come up with a plan to resolve the issues your customers are complaining about and turn the negative feedback into positive experiences.
- Alerting your teams. Once you know the actions you want to take, talk to your teams about the new procedures you’re planning to implement and why it’s important to do so.
Dealing with customer feedback analysis can be difficult at first, especially if you don’t have any prior experience or an analyst that can help. However, creating an analysis plan is crucial to your long-term customer feedback strategy as it will help you set clear goals and steps.
If you follow everything you read in this post, you can turn your customer feedback into a clear summary and use it to make informed decisions.Load more...