How to Analyze Customer Feedback
How to analyze customer feedback to understand the voice of the customer and identify actionable insights to build better products for them.
How to analyze customer feedback to understand the voice of the customer and identify actionable insights to build better products for them.
Everyone realises the need to understand the voice of their customers but it’s incredibly challenging to do it successfully. Challenges include collecting all feedback together, discovering what tags to apply, tagging feedback correctly, finding and sharing insights with your teammates.
We, at Enterpret, have been working on building analytics on top of customer feedback to make sure you are able to learn from your customers and the feedback they share with you.
In this blog, I’m going to elucidate the needs and challenges of analyzing customer feedback and how can you do successfully. In the second half of this blog, I'd be sharing how Enterpret helps you analyze customer feedback with ease, and what’s different about Enterpret’s approach.
Adam Nash (VP, Product at Dropbox), in this wonderful presentation, talks about the importance of listening to customers. Customers have a relationship with your product and share feedback on how that relationship can be improved. If their voice is not heard, the relationship is jeopardized.
Customer feedback is important for product development and product quality. Proper analysis is imperative to get a better view of what has to change and improve in the product to provide value to your customers.
There are two major aspects to analyzing customer feedback:
Above, you can see feedback from a customer of Notion. The feedback contains the following keywords:
Consider all of the different keywords contained within your product feedback. You could have hundreds.
In addition, different customers will share feedback for the same reason, but use different wording to describe it (e.g. I can’t renew my subscription, getting a payment error on resubscription, etc.). There could be infinite ways to describe a reason for feedback. Furthermore, these reasons for feedback themselves could very well range into the thousands.
To give feedback a structure, you need to accurately identify different keywords and reasons. This structure is called the Feedback Taxonomy.
Once the feedback is structured and accurately tagged, then you can answer questions like the ones listed below to both find and quantify relevant feedback:
Surfacing themes of feedback is helpful, but what makes feedback truly valuable is understanding the context: who the customer is, what was their behaviour, and where and when they shared the feedback. Tying the feedback they shared with the context of who they are is critical to unlocking insights with real business value, as opposed to just a generic list of the top 5 feedback themes in your user base.
For example:
At its core, Enterpret uses custom large language models to build an automatic feedback taxonomy customized to your product.
Enterpret’s unified feedback repository has native integrations that connect with feedback sources where natural language interaction happens between you and your customers. The feedback repository ingests feedback in any language, translates non-English to English.
Model training is automated by fetching historical feedback. After fetching all historical feedback, Enterpret removes spam and junk, since support channels can get a lot of spam.
Once the data is clean, Enterpret projects the feedback into a semantic space by leveraging the large language models I mentioned above. In the semantic space, all similar meaning text is clustered together. We then group these clusters, and give each a name — these are your feedback reasons. Let’s look at an example:
Let’s look at a real examples:
All three of these tweets are essentially talking about the same thing - “Switching From Evernote to Notion”.
After cleaning, all three pieces of text would be extremely close in the semantic space and would get clustered together - and can be named the same repeatable summary. Switching From Evernote to Notion would get recorded as a reason for the above feedback and any other similar feedback.
Similarly, tracked keywords like Evernote, Trello, Todoist, Web App, etc will get identified and tagged on the feedback.
Enterpret scans through all your feedback, historic and ongoing, and identifies the major reasons and entities within your feedback. This identification goes through multiple checks, including a human auditor, to ensure uniqueness (”switching from Evernote” and “moving over to Notion from Evernote” mean the same thing) and relevance (making sure the feedback is about your product).
We do a taxonomy refresh at regular intervals so that new reasons and keywords get created as your product evolves.
As a result, Enterpret is automatically able to identify all the thousands of reasons for feedback for your product and hundreds of keywords relevant to your product - through no effort on your end.
After the taxonomy is created, Enterpret then tags each piece of feedback ingested from the Unified Feedback Repository against the entire Taxonomy.
We train a custom model on your data to accurately tag the entire taxonomy on each feedback record to get optimum performance. These custom models are essential for analyzing customer feedback. While off-the-shelf models like GPT-3 can perform well on Internet data as that is what they are mostly trained on, they will perform poorly on a custom data set like your product’s customer feedback.
In addition, we have a team of human auditors who constantly check the performance of your model’s predictions to ensure nothing has gone astray.
Models are probabilistic by nature and will have a few incorrect predictions. We guarantee state-of-the-art performance, but incorrect predictions are bound to happen. Whenever you notice a mistake, you can report that feedback within Enterpret, and the model will update to ensure the same kind of mistake isn’t repeated.
Ingesting all feedback, creating a Taxonomy, and then applying the Taxonomy - creates your data of feedback records. Enterpret then provides you with an interface to perform analytical queries and search for feedback.
Some sample questions you could answer using Enterpret include:
Here are a few ways you can leverage the insights you identify in Enterpret in your day-to-day work:
Enterpret is differentiated from similar tools or generic models as it offers the following capabilities:
Hopefully, this post shed some light on how we’re approaching the tricky problem of analyzing customer feedback.
We’re working with some great product and product ops teams like Notion, Lambdatest and Airbase to help them identify actionable insights to build better products for their customers.
If you’d like to learn more about how your team can use Enterpret, please reach out!