Your qualitative feedback is abstract and hard to quantify. Here’s how you fix it.

Raveesh Motlani
Founder's Office
November 14, 2022

You’re going through customer feedback to manually tag it. One customer tweets, “The music stops and cuts off every time I put my headphones on.” Another customer reviews your app and says, “Every time I hit play after updating my playlist, the app quits randomly and restarts.

Feedback like this is pouring in for all products from multiple channels. If you interpret or group customer feedback incorrectly, or you’re too slow to act on it, you risk user churn and revenue. That’s how high the stakes are.

Qualitative feedback is notoriously difficult for product teams to interpret and act on because it’s subjective, nuanced, and time consuming. To actively listen to what customers are telling you, you have to be able to aggregate their feedback across all relevant sources, unify your feedback taxonomy, and analyze it granularly to pull valuable insights. A process that’s too broad or manual will not only end up being a colossal waste of time, but also do more harm than good by signaling you to invest in the wrong areas.

What is qualitative feedback?

Qualitative customer feedback is made up of in-depth, open-ended responses, as opposed to relying on singular, static data points. Customers often expand on their user experience or motivations when they’re giving qualitative feedback, helping you dive deeper into user behavior and needs. 

Qualitative feedback can come in many forms, such as:

  • Text responses in Net Promoter Score (NPS) surveys
  • Conversations with support agents
  • Help requests on social media
  • App store reviews

Customers might say just a few words or write multiple paragraphs, but they’ll go beyond the what of quantitative data and explain the why. A quantitative CSAT score provides a numerical measure of customer satisfaction, but qualitative data explains why customers are giving you that score. 

There’s inherent value in making sense of qualitative feedback because you can make more informed decisions. In one experiment, using qualitative insights led to a 300% lift in conversion rates.

Unlike quantitative feedback, qualitative feedback is notoriously abstract and difficult to analyze

Quantitative feedback allows you to look into the numerical data and pinpoint exactly what’s driving user behaviors, like clicks, subscriptions, and customer attrition. But qualitative feedback is more subjective and situational. It can’t be wrangled so confidently. You have to understand what your customers’ feedback means collectively, not just what they’re saying syntactically.

Take these two examples of customer feedback. Both customers were having issues with Shopify’s global payments but used different language to explain the problem and request a new feature.

This makes the feedback highly subjective for teams to analyze, which introduces biases to the decision-making process. Product teams start thinking, “We got a lot of comments about dark mode last week. Let’s roll out an update.” But they might not realize they’re getting a higher volume of requests for global payment support because they all use different language. Instead of being able to objectively quantify requests over time, recency bias takes precedence.

Without proper validation or efficient tagging, teams can’t neutrally analyze the feedback they’re getting. Different team members or departments may interpret or prioritize them differently based on language and other factors. And if companies received hundreds of these types of feedback regularly, it would become nearly impossible to keep up.

Customer feedback is siloed across teams

Often, subjective interpretations are compounded because different teams are responsible for tagging customer feedback based on your feedback tagging taxonomy. 

While your feedback taxonomy technically includes the same categories, each team or person might tag them differently based on their own perceptions and customer interactions.

You might have dozens of people interpreting and categorizing responses. And they’re all broken across departments, each with their own ideas about what drives up customer satisfaction and product adoption.

For instance, your customer support team handles live chats and calls to resolve customer issues. Your social media team monitors comments and mentions across platforms. Your product marketing team handles feedback directly related to NPS surveys and from sales teams.

Take the global payment request above. Your support team might tag that as a payment issue, but your product team might view it as a support issue for international customers.

Qualitative feedback is hard to scale

You not only have many team members with their own subjectivity to worry about — you also can’t get through the sheer volume of qualitative data. The more feedback and data you’re dealing with, the harder it becomes to keep up and draw cohesive conclusions. 

Qualitative feedback’s biggest benefit is how rich and insightful it is, but even enterprise companies can’t go line by line reading every comment to understand customer behaviors and needs. It’s much more time intensive than scanning reports from your web analytics or quickly graphing the answers to multiple-choice questions on a satisfaction survey to find trends.

Unify and automate your process for analyzing customer feedback

Qualitative feedback is much easier to understand and analyze with automation and natural language processing (NLP) tools. Ditch manual tagging for one automated feedback platform so you can pull insights that are clear, quantifiable, and, most importantly, actionable. 

A tool like Enterpret collects, organizes, and analyzes sophisticated qualitative feedback using customizable NLP based on interactions between your team and customers.

Collect qualitative feedback across channels and inputs

Start by implementing an automated feedback repository that can pull from multiple data sets, channels, and languages to get the full picture of customer behavior and perspectives.

Say half your user base uses an iPhone, and the other half uses Androids. You can’t exclude feedback from one device — you’d be ignoring half your customers. You want to collect all of your customer’s feedback without losing out on the rich context of where and when they shared it.

Use a tool that can capture historical and real-time user feedback in a unified repository. Natural language processing is a must, and so are native integrations with feedback sources like support tickets, app reviews, customer survey responses, and even customer calls. And not every customer will give feedback in the same language. Aim for a solution that can process and translate comments in multiple languages — again, helping you keep your feedback repository standardized.

Unify and standardize your feedback taxonomy

Using NLP tools, generate an automated feedback taxonomy that groups together responses and pulls valuable insights for your product managers. Your feedback taxonomy needs to include topics and keywords that are relevant across your customers’ comments and that get at the heart of what they mean semantically. Otherwise, you’ll be assigning flawed reasons and drawing incorrect conclusions from your data. 

An automated feedback taxonomy can group together comments related to the same topics and experiences, even if your customers used completely different words in their feedback. But your taxonomy needs to be relevant to your product and customers. Don’t just plug in GPT-3 or a low-code NLP tool. Instead, make sure you’re using machine learning that’s fine-tuned to your product experience.

And as you’re setting up keywords in your taxonomy, avoid being too broad. Don’t settle for a low number of tags or reasons. Get down to granular and actionable tags. Sure, you can see that 500 users complained about “UX issues”, but this is useless knowledge. You need to see a granular breakdown of what those issues were, and have the ability to quantify them so you can take action.

An automated feedback taxonomy that breaks down granular reasons for customer feedback. The taxonomy is split into three columns: Tracked Keywords, Reason, and Feedback Records.

Analyze through AI and NLP to avoid biases

Don’t tag and review feedback manually and look for trends yourself. Use AI to pull in every comment, interpret your customers’ voices, and analyze for quantifiable insights so you can draw conclusions.

AI and machine learning can sort through qualitative feedback and accurately report on metrics, so you can make decisions while avoiding some of the human biases that come into play with subjective data. Importantly, this technology can scale up to any volume — one of the things that makes qualitative data so tricky to draw objective conclusions from. And it makes text analytics quantifiable, identifying clear trends, so you know which product feature to focus on next.

Use an automated feedback system that can segment based on certain customer types, such as “Enterprise plan” or “Android users”. This type of audience and data segmentation provides key context to the raw data after analysis — did you see a certain percentage increase or decrease among Android users after a new update? Are customers in one market asking for refunds at a higher rate?

Surface verifiable insights to address customer needs — not mistaken perceptions 

Often, product teams and leaders have preconceived ideas about why adoption is low or what features they should roll out next. But it’s costly to develop, iterate, and launch updates that end up falling flat when you have no real insight into the problem.

Take a step back and go through your qualitative feedback. When you get to the heart of qualitative feedback, you can prioritize efficiency along with your customer experience.

Schedule a demo to see how we've automated this process at Enterpret.

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We are laser-focused on giving customers more than they expect through a hospitality-first, individualized approach to drive retention and loyalty. Enterpret has allowed us to stitch together a full picture of the customer, including feedback and reviews from multiple data points. We now can super-serve our loyal customers in a way that we have never been able to before.
Anna Esrov
Vice President of Customer Experience & Loyalty
Enterpret allowed us to listen to specific issues and come closer to our Members - prioritizing feedback which needed immediate attention, when it came to monitoring reception of new releases: Enterpret picked up insights for new updates and became the eyes of whether new systems and functionality were working well or not.
Louise Sellars
Analyst, Customer Insights
Enterpret is one of the most powerful tools in our toolkit. It's very Member-friendly. We've been able to share how other teams can modify and self-serve in Enterpret. It's bridged a gap to getting access to Member feedback, and I see all our teams finding ways to use Enterpret to answer Member-related questions.
Dina Mohammad-Laity
VP of Data
The big win-win is our VoC program enabled us to leverage our engineering resources to ship significantly awesome and valuable features while minimizing bug fixes and" keep the lights on" work. Magnifying and focusing on the 20% that causes the impact is like finding the needle in a haystack, especially when you have issues coming from all over the place
Abishek Viswanathan
Since launching our Voice of Customer program six months ago, our team has dropped our human inquiry rate by over 40%, improved customer satisfaction, and enabled our team to allocate resources to building features that increase LTV and revenue.
Abishek Viswanathan
Enterpret's Gong Integration is a game changer on so many levels. The automated labeling of feedback saves dozens of hours per week. This is essential in creating a customer feedback database for analytics.
Michael Bartimer
Revenue Operations Lead
Enterpret has made it so much easier to understand our customer feedback. Every month I put together a Voice of Customer report on feedback trends. Before Enterpret it would take me two weeks - with Enterpret I can get it done in 3 days.
Maya Bakir
Product Operations, Notion
The Enterpret platform is like the hero team of data analysts you always wanted - the ability to consolidate customer feedback from diverse touch points and identify both ongoing and emerging trends to ensure we focus on and build the right things has been amazing. We love the tools and support to help us train the results to our unique business and users and the Enterpret team is outstanding in every way.
Larisa Sheckler
COO, Samsung Food
Enterpret makes it easy to understand and prioritize the most important feedback themes. Having data organized in one place, make it easy to dig into the associated feedback to deeply understand the voice of customer so we can delight users, solve issues, and deliver on the most important requests.
Lauren Cunningham
Head of Support and Ops
With Enterpret powering Voice of Customer we're democratizing feedback and making it accessible for everyone across product, customer success, marketing, and leadership to provide evidence and add credibility to their strategies and roadmaps.
Michael Nguyen
Head of Research Ops and Insights, Figma
Boll & Branch takes pride in being a data driven company and Enterpret is helping us unlock an entirely new source of data. Enterpret quantifies our qualitative data while still keeping customer voice just a click away, adding valuable context and helping us get a more complete view of our customers.
Matheson Kuo
Senior Product Analyst, Boll & Branch
Enterpret has transformed our ability to use feedback to prioritize customers and drive product innovation. By using Enterpret to centralize our data, it saves us time, eliminates manual tagging, and boosts accuracy. We now gain near real-time insights, measure product success, and easily merge feedback categories. Enterpret's generative AI technology has streamlined our processes, improved decision-making, and elevated customer satisfaction
Nathan Yoon
Business Operations,
Enterpret helps us have a holistic view from our social media coverage, to our support tickets, to every single interaction that we're plugging into it. Beyond just keywords, we can actually understand: what are the broader sentiments? What are our users saying?
Emma Auscher
Global VP of Customer Experience, Notion
The advantage of Enterpret is that we’re not relying entirely on human categorization. Enterpret is like a second brain that is looking out for themes and trends that I might not be thinking about.
Misty Smith
Head of Product Operations, Notion
As a PM, I want to prioritize work that benefits as many of our customers as possible. It can be too easy to prioritize based on the loudest customer or the flavor of the moment. Because Enterpret is able to compress information across all of our qualitative feedback sources, I can make decisions that are more likely to result in positive outcomes for the customer and our business.
Duncan Stewart
Product Manager
We use Enterpret for our VoC & Root Cause Elimination Program - Solving the issues of aggregating disparate sources of feedback (often tens of thousands per month) and distilling it into specific reasons, with trends, so we can see if our product fixes are reducing reasons.
Nathan Yoon
Business Operations,