Product feedback, overall customer experience reviews, and help center comments all offer a unique lens through which you can improve your products and help your customers better.
Here’s how I approach analyzing different types of customer feedback to find and take advantage of opportunities.
Three main categories of feedback
To simplify, I first break down feedback into three main categories: product feedback, overall support feedback, and help center feedback.
- Product feedback. This type of feedback provides direct insights into how well your product meets customer expectations and where it falls short.
- Overall support feedback. This type of feedback often highlights systemic issues or areas where the customer journey can be improved.
- Help Center Feedback. Feedback on help center resources can show how well your self-service options are performing and where they might need improvement.
Each type of feedback has one thing in common: it all comes directly from your customers, which makes it invaluable. Once the feedback is categorized, it’s time to analyze each type of feedback.
How to Analyze Customer Feedback Regarding a Product
Product feedback is so important that it’s a hallmark of pre-launch product research. But it doesn’t stop there. Product feedback continues to be key throughout the entire product lifecycle.
Here’s how I recommend you analyze product feedback.
Categorize feedback themes
Start by organizing feedback into categories such as features, usability, performance, and bugs. For example, if customers are saying that your product is difficult to use or crashes frequently, that can be categorized under “usability” or “bugs.” If your customers like your product but would love additional features, that feedback can go under the “features” category.
Feedback themes help you prioritize, because they help you understand the trends and opportunities.
Analyze customer sentiment
Pay attention to the sentiment behind the feedback. Positive comments can indicate which aspects of the product are working well, while negative comments reveal pain points. If you have a small dataset you regularly analyze, doing so manually is an option. However, I’d recommend using a tool or ChatGPT for sentiment analysis for larger datasets.
Companies like Qualtrics or Usersnap help collect the data and can analyze the sentiment for you. Whatever tool you already use may have sentiment analysis built in.
If you already have the data but need it analyzed, you can create a ChatGPT prompt for sentiment analysis. You’ll need to adjust it based on the format and specifics of your data, but this can start with something as simple as:
“I have a CSV file containing customer feedback from customer surveys, support tickets, and help center articles. Please analyze the sentiment of this feedback. Specifically, I would like you to:
- Identify the overall sentiment (positive, negative, or neutral) for each feedback entry.
- Provide a summary of the most common themes or issues mentioned in the feedback.
- Highlight any particularly strong positive or negative sentiments and explain what might be driving them.
- Provide any recommendations on how to improve customer satisfaction and loyalty based on the sentiment analysis
The CSV file contains columns such as ‘Feedback Source,’ ‘Customer Comment,’ ‘Date,’ and ‘Category.’ Please use this information to guide your analysis.”
Prioritize based on impact
Focus on issues that affect a large number of users or problems that significantly impact user experience. For example, a bug affecting the checkout process should be prioritized over minor feature requests.
You can also consider tackling multiple pieces of feedback with one product release. For example, a frequently requested feature enhancement release could also address a frequently complained-about bug. Prioritizing requests that could address multiple pieces of feedback will help you take quicker action for your customers.
And lastly, focusing on issues that have a direct relationship with retention or revenue can also have far-reaching impact.
Engage with customers
Product feedback can sometimes be vague or lack detail. When that’s the case, follow up with customers for more information. This direct engagement not only helps clarify issues, but also shows customers that their input is valued.
Email is usually the best option for reaching out to customers because you can add their responses to where you’re tracking the data. It also feels less invasive to your customers.
Here’s an example:
Hi [NAME],
Thanks so much for sharing your feedback about your experience with [PRODUCT]. I’m the manager of the support team at [COMPANY], and I saw that you had some ideas on how we could improve it, but I had a few questions and I want to make sure I was understanding your feedback correctly. [INSERT CLARIFYING OR FOLLOW UP QUESTIONS BASED ON ORIGINAL FEEDBACK]Your insights help us improve, so anything ELSE you can share would be super helpful. Feel free to reply here, or if you prefer, we can set up a quick call—whatever works best for you!
How to Analyze Customer Feedback
Your support team plays a key role in customer retention, with Zendesk’s research showing that 73% of customers will switch to a competitor after more than one bad experience. That means it’s important to take each piece of support feedback seriously because you may only have one chance to fix it.
Here are some tips on how to get it right.
Collect data at each touchpoint
Look at the entire customer journey and identify where feedback is collected, such as after a support interaction, following a purchase, or more proactive interactions like account management touchpoints. CSAT (customer satisfaction) and NPS (Net Promoter Score) surveys are two of the most popular methods to collect support feedback.
Analyze feedback at each touchpoint to pinpoint where the support experience might be lacking.
For example, do customers like your chatbot? Or are they frustrated because they’re finding it too hard to reach a human team member when necessary? The only way to find this out is if you collect and analyze the data at each touchpoint.
Look for patterns in support interactions
Pay attention to recurring comments about the quality of support your customers receive, such as response times, the helpfulness of agents, and the ease of resolving issues. These trends will help you identify where improvements are needed, where you can simplify support workflows, and where your team could use additional training.
At the same time, if you’re not seeing many comments in your CSATs, optimize your surveys to make sure they’re short and include a comment box to increase engagement.
Benchmark against industry standards
Compare your feedback metrics with your industry’s benchmarks to gauge how your support processes stack up. If your support team takes longer to resolve issues than competitors, determine what needs to happen to increase resolution times.
Similarly, if customers frequently mention longer wait times compared to competitors, it may be time to reassess your support strategy or the structure of your team.
My support team was structured to segment the type of work each smaller team was doing. The intention was to decrease turnaround times, but when we analyzed the customer feedback on the support experience, we found it had the opposite effect. It helped reimagine the structure of the entire team, and our SLAs improved within two weeks of restructuring the team.
Implement feedback loops
A feedback loop is a tactic where you not only receive customer feedback, but you respond to it to improve the customer experience. Create a feedback loop where customers are regularly asked for their input on the support process. This ongoing dialogue helps ensure that you’re continuously improving and adapting to customer needs.
A simple method we use in my support department is making sure we reach out to every customer who isn’t satisfied with their support experience. By providing more detail about their experience, they feel like they matter. A feedback loop is especially helpful if you’re introducing a new process for reaching out to support, such as a new AI chatbot.
Analyzing help center feedback
Customer help centers are a great way to reduce tickets and get customers what they need faster and more efficiently. And Zendesk’s self-service research showed that “91% [of customers] say they would use a knowledge base if it met their needs.”
But to do that, you need to analyze the help center feedback data you’re getting from your customers effectively. Here are some tips for doing that.
Review help center analytics
Start by looking at help center metrics such as search queries, article views, and click-through rates. Identify which articles are frequently accessed and which ones are rarely visited. This data helps you understand what information customers are seeking and where gaps might exist. It also helps you prioritize the most popular articles for updating.
Using a tool to do this will make it much easier for you.
Swifteq’s Help Center Analytics app makes analyzing the performance of your Zendesk help center content super easy. You can track metrics like the helpfulness of each article, how many users create tickets after engaging with an article, and more.
If you have a Zendesk-hosted help center, you can enable a voting system on each article to collect visitor feedback. However, simply knowing whether an article was helpful or not isn’t enough to understand how to improve it.
Fortunately, the Help Center Analytics app provides a way to install a feedback form connected to Zendesk’s native voting system, allowing you to collect text feedback with each visitor vote on an article.
Form to collect feedback on articles from visitors
Assess user experience
Evaluate feedback related to the usability of your help center. Do your customers find it easy to navigate? Are there complaints about broken links or outdated information? Are articles broken up into chunks to make them easy to read?
It goes without saying that user experience is a key factor in the effectiveness of your help center. Use feedback related to the UX to make your help center more user-friendly.
Update based on feedback
This is where your help center analytics come to the rescue. Based on feedback and common search queries, identify the help center articles that will have the most impact and update them regularly.
If customers frequently search for information that isn’t available, create or revise articles to address these needs. Ensure that the content is clear, comprehensive, and easy to understand.
At my job, we have a system where we track when an article was last updated and how often it’s used in a spreadsheet. We use those two data points, along with user feedback, to determine what articles we need to update. We revisit these monthly to make sure our help center stays up to date.
Test and iterate
Outside of large impact updates like updating the most used help center articles, implement other changes to your help center through iteration and then test their impact.
Iterative improvements based on customer feedback helps with three things:
- It fine-tunes your self-service resources to better serve your customers so they can have a great experience across the board rather than just with the top few articles.
- It doesn’t take a huge amount of time investment to make continual iterative improvements compared to large help center initiatives from letting it sit out of date too long.
- It allows you to undo changes that aren’t well received quickly.
Turning feedback into actionable opportunities
Analyzing customer feedback is only the first step. The real value comes from translating these insights into actionable strategies. Analyzing customer feedback more effectively will help you uncover opportunities to enhance your products, improve support processes, and optimize help center resources.
And Swifteq can help with great tools to help you manage your knowledge base better, automate workflows, and overall increase customer satisfaction.
Tim is a Manager of Customer Support at Cars.com and a writer for Supported Content. When he’s not busy leading his team, you’ll find him spending time with his wife and two daughters, usually on some Disney-related activity. He also blogs about personal finance at Atypical Finance.
