Feedback analysis is the process of figuring out what customers think about your product. It involves collecting customer feedback—primarily as qualitative data—so you can identify why your product works or doesn’t work for customers.
Customer feedback isn’t only negative. Customers will often provide complimentary feedback when:
Feedback can also be in the form of customer requests. Product management and customer support teams often find new product ideas or process improvements in customer requests.
Improving the user experience reduces friction for customers, helping you retain more of them. Businesses who conduct feedback analysis regularly are more likely to identify ways to improve the user experience than those who do not.
Customer feedback that's been structured and analyzed is also great for onboarding new hires to your organization.
To analyze customer feedback effectively you need to be able to collect it from various channels, organize it, categorize it—also known as tagging—and then interpret what it’s saying.
The most important thing about doing customer feedback analysis is doing it at scale. Relying only on anecdotes can bias your decision making. Look to get insights based on all your customer feedback and not just a few data points so your decisions are data-driven.
Customer feedback analysis typically involves the following steps:
You can analyze customer feedback manually or with the help of tools that automate some or most steps.
Manual customer feedback analysis is common especially at companies in the early stages of growth. It’s a good way to learn what it takes to make sense of what customers are telling you.
Once you understand the work involved in analyzing qualitative data, you then need to figure out how to inform your decisions with that knowledge. You might also develop an appreciation for customer analysis automation.
Automated customer feedback analysis eliminates manual, time-consuming sorting and tagging tasks. Many tools—such as text analytics software—will identify keywords or topics automatically and categorize feedback accordingly. You’ll get charts, graphs, and dashboards to track metrics per keyword or topic.
People with data analyst backgrounds can navigate most text analytics tools with skill. Others might find the learning curve too steep or the time requirements too onerous.
Advances in large language models have made it possible for new qualitative analysis tools to automate both theme analysis from customer feedback and the creation of readable reports. Read more about how this works thanks to models like GPT-3.
Such tools are better suited for decision makers who are tight on time but still value having direct access to the insights.
Customer feedback comes in many forms:
Any source of feedback from customers, particularly qualitative data, can help you understand your customers better so you can improve your product.
Your company is likely collecting customer feedback through various channels. We break down different ways to gather feedback from customers.
Customer surveys are a top method for gathering customer feedback. They allow you to ask specific questions about a product, workflow, or experience.
Our guide on how to analyze surveys details different types of surveys. Below are summaries of the most common ones.
Net promoter score surveys (NPS surveys)
NPS surveys measure how likely customers are to recommend your product or service. The metric is a rating, often from 1 to 10, and is typically accompanied by open-ended questions asking users to elaborate on their answer.
NPS surveys are used by product management, operations, and customer service teams to track how customers feel about the business in the long term.
Customer satisfaction surveys (CSAT surveys)
CSAT surveys measure shorter term engagement with customers, often after an interaction with customer service, a product purchase, or another event where the customer interacted with the company.
Many tools offer CSAT survey templates including helpdesk and customer relationship management (CRM) tools.
Customer effort score surveys (CES surveys)
Customer effort score questions can be included in other surveys, like CSAT surveys. Stand alone CES surveys are also common. They measure how easy it is for a customer to use or interact with your product or service.
Understanding the level of effort for customers helps product managers and UX designers improve the user experience.
Some examples of CES survey questions:
Product-market fit surveys
You can measure product-market fit (PMF) with the product-market fit survey method developed by the CEO and co-founder of Superhuman, Rahul Vohra.
Before becoming a qualitative AI platform, we at Viable offered an automated product-market fit survey product…and we were called Viable Fit. While we no longer offer the PMF survey (and dropped the Fit in our name), there are other companies offering the template as part of their solutions (Delighted and Doopoll are just two examples).
A great way to get feedback from customers is directly via your product. Asking for feedback while users are in your product can generate continuous feedback.
You can either build your own in-app feedback survey or use an existing solution like Pendo.
With a large selection of survey tools available, it’s easy to create custom surveys. You can ask customers, prospects, business partners, or employees for feedback on nearly any topic.
For instance, if you’ve created a waitlist prior to a highly anticipated product launch, you might want to take the opportunity to ask customers for feedback. It could help you make final tweaks prior to launch day.
For many companies the most common type of customer feedback comes through customer support tools (known also as helpdesks or communications platforms) including Zendesk, Intercom, Front, ServiceNow, Help Scout, FreshDesk, Salesforce Service Cloud, Jira Service Management, and others.
Support software often comes with some customer feedback analytics. Many will also integrate with other tools directly, which is useful for more in-depth qualitative analysis.
Product teams looking for a comprehensive view of what users say in mobile app reviews find that getting the data from the Apple App Store and Google Play Store can be straightforward.
Organizing and analyzing it is another matter. However, reviews can provide valuable insights for businesses looking to improve their apps quickly.
(Check out our report of the qualitative analysis we did on more than 50,000 mobile app reviews to see how the latest in natural language models handle large feedback datasets).
Review sites like G2, Capterra, and Trustpilot are great resources for anyone looking to assess solutions based on user testimonials. For businesses being reviewed, these sites contain some of the highest quality feedback.
Qualitative data analysis from these sites can be automated using Zapier. Here’s an example of a Zapier workflow using Viable.
Recorded conversations are more and more common thanks to the rise of software like Gong, Chorus, and others. Sales teams use these recordings to keep a detailed record of prospect conversations or develop on sales best practices.
Product teams, marketers, and user researchers can also learn a lot from sales call transcripts, including:
Large volumes of user research can also be organized efficiently if call transcripts are available.
Check out our article on how to analyze call transcripts.
Customer support analysis is not just for helpdesk tickets. If your company has a social media presence, you might engage with customers regularly—or even conduct customer support through Twitter or Facebook.
Feedback from these channels can be analyzed in a methodical way.
Here’s our Zapier Twitter template for analyzing Twitter data with Viable.
Customer research teams dedicate a lot of time to organizing their raw notes and research findings into shareable reports.
Sales, product, and customer support teams similarly invest time and effort into keeping track of customer and prospect notes.
To track notes in a way that allows for qualitative analysis, use CRM systems or productivity tools that facilitate note taking. Even if it’s a spreadsheet, you can build a good template that will make it easy to analyze qualitative data when you’re ready. See our guide for tracking qualitative data in CSVs for multiple use cases. It has a template you can copy.
Our team can help walk you through best practices for collecting, structuring, and analyzing qualitative data from existing sources in an automated way.