Go from Good to Great: The Value of Understanding Feedback Context

Noah Klausman
Sales
March 4, 2024

AI has changed the game of big data analysis in many spaces. Understanding the Voice of Customer is one space where the change is particularly monumental. Brands no longer need to rely on a manually created list of tags applied haphazardly (at worst) or unevenly (at best) to feedback records by resources with natural and unique biases.

Today, both off-the-shelf and custom Large Language Models can serve as the bulwark for tagging and categorizing feedback records, alleviating both the need for manual tagging and, more importantly, the inherent degradation of analysis quality. 

Unfortunately, while automating the process of manually tagging feedback has alleviated many problems, it has also led to blind spots in understanding and, ultimately a deep misunderstanding of what your customers are telling you in these valuable records.

Limits of analyzing feedback by keywords

In real-world conversation, our brains are programmed to recognize nuance. Unfortunately, the majority of feedback analytics tools are calibrated to organize records by keyword, often called a “topic.” 

For example, a client may share product feedback that looks like this:

"Love the overall UI of the app, but been struggling with the slow download speeds of albums. I also wish they would release a dark mode in the future for their Android app just like the web version"

In this case, a model may extract keywords like:

  • “UI of the app”
  • “slow download speeds”
  • “dark mode”
  • “Android app”
  • “web version”

Furthermore, because the record begins with praise, the sentiment level may be tagged as Positive, effectively overlooking the rest of the context of the record. 

If you are digging into the data, would any of those tagged Topics lead you to a conclusion short of further mining and manually digging? How would the topics “dark mode” or “web version” point you to a useful next step? For anyone reviewing this data as part of a larger data set, they would be highly likely to reach unsubstantiated conclusions.

Deeply understand feedback using context

The best platforms go deeper. Hidden behind the keywords is the most important element; the context of a specific feedback record. Without this qualification, keywords are not particularly useful. Enterpret’s adaptive custom ML models analyze every single sentence of each feedback record, and summarize each one into a Reason.

Reasons are a game-changer. They are semantic summaries of each element of each record. In the above case, the taxonomy would categorize that feedback record into the following Reasons:

  • “Happy with app UI”
  • “Difficulty with slow album download speeds”
  • “Feature request for dark mode in native app”

The model would also assign multiple categories to this record, likely Praise, complaint, and Feature Request, providing an unparalleled level of conclusive context.

This shifts the analysis paradigm; gleaning a depth of insights into the specific actionable next step that needs to be taken to address this issue. At scale, if you see 30% of feedback records noting difficulty with the login flow, you know there’s an issue, and you know what you need to fix.

Use AI for better granularity and quality for tagging feedback

"Ultimately, the biggest challenge in deploying machine learning isn't writing the code. It's in collecting and cleaning the training data. And more data beats a cleverer algorithm." - Andrew Ng, leading AI and Machine Learning professor at Stanford University 

Machine Learning has brought feedback analytics from 0 to 1.  While sophisticated algorithms are crucial, the value of machine learning ultimately hinges on the quality and quantity of the data used for training and, in the case of feedback, the level of granularity in tagging. 

In short, the context of the feedback is just as important as the topics (and in some cases, more). If you aren’t examining that context, you likely will have major blind spots.

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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