How to Build a Customer Feedback Analysis Process (with AI!)

The following is a guest post submission from Federico Pascual, co-founder and COO of MonkeyLearn.

Customer feedback doesn’t just come in through your site’s contact form – it’s everywhere.

You only have to search the Twitter handle of any product with more than a few hundred users to see that customers love to offer their opinion – positive and negative. It’s useful to be monitoring this and learning from it, but casually collecting feedback on an ad-hoc basis isn’t enough.

Startups thrive on feedback as their ‘North star’, and are constantly evolving based on what their customers request, break, and complain about. Enterprises also can’t overlook the fact that customers are what make any company tick, and must struggle harder than startups to stay relevant and innovate.

So, if you’re just collecting feedback ‘as and when’ it comes in, you’re missing out on data that’s just as important as page views or engagement. It’s like deciding not to bother setting up Google Analytics on your homepage, or not properly configuring your CRM; in the end, you’re deciding to not benefit from data that will have a transformative effect on your product strategy.

With a dataset of feedback – whether that’s from customer reviews, support tickets, or social media – you can dig into the words your customers are using to describe certain parts of your product and get insights into what they like, and what they don’t like. In this post, I’m going to show you how.

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