4 ways your product feedback loop is broken

Jack Divita
Customer Success Manager
June 30, 2022

Since joining Enterpret I have had the opportunity to speak with product leaders across multiple industries about how they manage and make use of their customer feedback. Each of these teams recognizes that feedback from their customers is paramount to the success of their products, but they have taken a wide range of approaches to learning from their customer feedback—to varying degrees of success.

As the world of SaaS products moves more and more to product-led sales motions it is more important than ever that product teams listen to, and learn from, their customer feedback. In this article I’ll discuss the 4 most common mistakes that I see product teams making when it comes to learning from their customer feedback.

  1. Your customer feedback is all over the place
  2. You are not quantifying your qualitative feedback
  3. You consider all customer feedback equal
  4. You’re wasting your time on manually intensive solutions

Your customer feedback is all over the place

Its 2024 and your customers have 6+ ways of getting in touch with you:

  • Social media
  • App store reviews
  • Support tickets
  • Sales calls
  • In-app surveys
  • Email
  • Consumer rating services
  • Customer and industry communities

At most tech companies each of these channels is managed and supported by different teams. If you are only looking at one, or a few, of these sources then your thinking will inherently be biased towards those users and you’re missing out on valuable information. Even if you have set up a solution to forward these conversations to a shared Slack channel, you are still going to have biases based on what you read and when - are you really taking the time to read through every single ticket?

The best teams take a holistic view of all their different feedback sources so they can have a complete understanding of their customers. By having a centralized system of customer feedback, they know that they are not missing any important trends or giving in to biases by only listening to certain types of customers.

For most teams, customer feedback is siloed in different systems. The best teams get a holistic view so that they can quantify what customers are saying across all their feedback channels.

You are not quantifying your qualitative feedback

Many product teams succumb to building products for their loudest detractors, or the most recent bug report, and do not take a data-driven approach to understanding their customer feedback. If you are not quantifying your qualitative feedback you are letting your biases make your decisions for you.

By taking a data-driven approach you can focus on building what will have the largest impact on your business by knowing, not guessing, what is most important to your customers.

Without good data on your customer feedback, it’s easy for recency bias or confirmation bias to sway product decisions. You need a way to quantify feedback and put that in the context of all the issues your customers are facing.

You consider all customer feedback equal

Was that random tweet from a user at a million-dollar customer who is at risk to churn? Was that scathing support ticket from one of your customers who just upsold?

Once you have a holistic view of everything your customers are saying and how often they are saying it, it is critical to understand who is saying it as well. This can be done through traditional cohorts (e.g. paid vs. unpaid and enterprise vs. mid-market), but truly data driven teams also do this by combining your customer feedback trends with your product analytics, which allows you to see:

  • What your power users are saying and what's holding back your casual users from reaching that level.
  • What your churned users were saying before they left. Do you have active customers struggling with similar things?
  • If you want to increase expansion into a new market, what do your existing customers there love about your product?

By understanding what's most important to different groups of user you can make informed decisions to best meet their unique needs.

Rather than looking at all of your product feedback in aggregate, it’s important to be able to segment by different user segments or behaviours so that you can identify what’s most important for your business.

You’re wasting your time on manually intensive solutions

Even the best teams with committed customer feedback strategies often rely on manual tagging to categorize and aggregate what their customers are saying.

Is your support team compensated for how accurately they tag a ticket? Or is it on how quickly and effectively they help your customers? What happens when you add a new tag; do you go back and re-tag every piece of feedback you’ve ever gotten? What if a new feature request is only seen once by each support agent?

For your product and engineering teams, how much time do you spend reading through customer support tickets? Is that a high leverage activity for your time?

By automating the time that would otherwise be spent triaging support tickets, reading through community conversations, listening to recorded customer call, and then aggregating everything you saw in a spreadsheet, you open up hours every week for your team to spend on their core, value-adding, jobs.

Modern machine learning algorithms are capable of fully understanding topics, intent, and sentiment of written text at levels that match, and often exceed, human capabilities. Taking advantage of machine learning can help create a robust understanding of what your customers are saying in a fraction of the time it would take a human to review a similar-sized dataset.

Efforts to manually tag often break down at scale. Lack of accuracy erodes trust, and tags are not granular enough to be actionable. Instead, natural language models can help identify the meaning behind pieces of feedback and surface themes humans might otherwise miss.

What makes an excellent strategy for customer feedback analysis?

Every product team wants to be customer-centric, but it’s easier said than done. By following these 4 principles you can feel confident that your customer feedback strategy is going to help you do just that.

  1. You have a single source of truth for all customer feedback that allows you to see trends and insights no matter where they are coming from.
  2. You take a data-driven approach to confidently know what your highest impact issues and opportunities are.
  3. You know what is important to every different type of customer and can make decisions based on business impact.
  4. You have an automated solution that gives you the highest possible degree of confidence and allows your team to focus on high-leverage activities.
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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
CPO, Apollo.io
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
CPO, Apollo.io
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, Apollo.io
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, Apollo.io