If you’re gathering customer feedback, you know that the trouble is not in gathering feedback, but rather, getting actionable insight from the feedback you’ve gathered. Customer feedback analysis, to be precise. Even if you feel like you’re sitting on a goldmine for understanding your customer base, getting value out of your feedback can seem like a monumental task.

You might find yourself frustrated by:

  • Feedback from different channels being locked in data silos
  • The enormous time investment it takes to review every customer comment
  • Trying to understand overall trends and themes in your open feedback

This is especially true when it involves large amounts of qualitative feedback. After all, while Customer Experience metrics like Net Promoter Score (NPS), Customer Satisfaction (CSAT), or Customer Effort Score (CES) are important indicators of customer sentiment and account health, the why behind these scores are the true value for CX.

But there’s no need to dread the task of analysis. Machine learning algorithms can make unlocking insight from qualitative feedback happen in a matter of minutes.

Machine learning is the hero we need

Machine learning algorithms have come a long way. They can now parse customer comments for sentiment, themes, and business insight with similar accuracy to humans in a fraction of the time. They don’t get bleary-eyed after reading too many comments. When you correct them, they incorporate those corrections immediately into all future analysis, as well as going back to correct their previous analysis!

With machine learning algorithms doing the tedious work, you can get to following-up with customers and acting on the insight you’ve gained faster than ever before.

No more trapped customer feedback

Having all of your qualitative feedback in one place makes it easier for you to understand the big picture when it comes to your customers. It means that your holistic reports on your overall customer base will be complete and accurate. The story you tell with your data can only reflect reality if you have all of it together.

AI-powered customer feedback analysis software is the aggregator you need to overcome data silos for customer experience.

How machine learning sorts and organizes your feedback

Customer feedback software unlocks insight at a speed and level of analysis that humans can’t achieve. Our brains aren’t built to handle huge quantities of data too quickly. We get tired, but machines are consistent, tireless, and fast.

Machine learning algorithms scan each comment that you feed them for overall sentiment and themes that they organize using tags. These tags have two functional purposes: routing comments to relevant stakeholders and creating structure for qualitative data to be analyzed quantitatively.

For example, let’s say you receive a comment that says, “I liked how quick the delivery to me was, but returning my order was a pain and I never received my refund.” The customer feedback software can analyze this and tell you that, overall, the sentiment is mixed. It would tag this comment with the themes of “Returns,” “Delivery,” and “Payments.” Each of these tags will be assigned their individual sentiment as well, with green being positive, blue being neutral, and red being negative.

Image of analyzing customer feedback using natural language processing

These tags are searchable, so you can find all of the comments tagged “Payments,” quickly see the ones with negative sentiment, and send them to accounting to address them immediately.

Alternatively, you can keep an eye out for bottlenecks and see what customers are saying by going through all the comments tagged “Delivery” or “Returns.” All of this can happen for thousands of comments in a matter of minutes, giving you more time to follow-up with customers and take care of higher-level business improvement.

Get the highlights and what’s trending

After machine learning algorithms have organized all of your qualitative data, you get to dive in and understand the why behind the feedback your receive. What part of the customer journey is driving customers to leave specific feedback? And is there something you can do to make the experience better?

Regardless of which platform you use to sort your feedback, here are some of the things you want to have on your radar:

  • If you are using CX metrics like NPS, CSAT, or CES, flag accounts with decreasing scores and any feedback where there are mismatches in sentiment and score (that is, an overall positive sentiment CSAT comment paired with a score of 2).

Image of Wootric watchlist, which automatically displays key trends in customer feedback to help companies learn and respond

  • Use filters and segmentation to see trending tags and topics for different parts of your customer base. For example, you may want to see what your paid pro customers are happy with and what they want improved. You can also see how many customers have put in a particular feature request. The possibilities are nearly endless.

Stop Reading Endless Comments, Start Acting on Insight

Whether you feel like you’re drowning in customer feedback, or feel like getting useful insight out of qualitative data is like pulling teeth, machine learning is here to make your life easier. Improve your customer experience, increase customer loyalty, and be a leader in your industry by adopting machine learning for customer feedback analysis.