How Uses Machine Learning to Turn Customer Feedback into Actionable Insights

Ashwin Singhania

Ashwin Singhania

5 minutes

We have covered in other posts why my co-founder Ryan and I started and why it’s important to analyze customer feedback in the first place. Today, I want to dive into how our platform works and the output. 

The sheer volume of customer feedback can be overwhelming and noisy, let alone finding the patterns and pulling important insights. That's where machine learning and artificial intelligence comes in. Machine learning (ML) is a subfield of artificial intelligence. 

We employ a particular machine learning technology called natural language processing (NLP) in a handful of distinct features so that can do the heavy lifting for you.

Automated aggregating & analyzing makes it easy to manage all your customer feedback in one place, no matter what channels it comes from or how often new data streams in. All you have to do is connect your public and private data sources through our integrations page. 

Once the data is integrated, our AI sifts through all of your feedback and automatically identifies the most actionable patterns of feedback so you don’t have to waste time filtering through it yourself.

That includes identifying patterns that indicate an issue or opportunity that needs addressing right away so you can prioritize what matters most and take decisive action as quickly as possible.

Search capabilities identify hard-to-find insights

So now that all of your customer feedback is in one place and our AI has sorted it into actionable patterns for you - delivering your top insights within minutes, what’s next? Aside from immediately applying those insights to your roadmap, search for specific or more granular insights. 

You can perform powerful searches that let you find those hard-to-find insights that would otherwise be hidden in a sea of customer feedback. 

Simply type in what you’re looking for - for example: ‘incorrect number of payments - and see all feedback about that issue almost instantly. You can then sift through the actual anecdotes, and also quantify how matching feedback has trended over time. 

This capability alone makes it easier than ever to uncover those “Aha!” moments that drive real progress for your organization by connecting the dots between feature improvements and product performance in a meaningful way.

Sentiment analysis gives you pulse on customer point of view

One last feature is our sentiment analysis. Our AI conducts an analysis on the sentiment of each piece of customer feedback - categorizing it as negative, positive, or neutral. 

You can filter your feedback by sentiment to quickly analyze the context of each category over a period of time.  

These three features and so many more allow you to make informed decisions about where best to invest your resources for maximum impact on user experience and satisfaction levels. does the Heavy Lifting for You 

We understand that customer feedback is essential for any successful business but also realize how daunting it can be dealing with all that data coming from multiple sources at once. Machine learning and natural language processing gives us the opportunity to make that process more efficient than ever before.

After all, what good is customer feedback if you can’t turn it into actionable insights that lead to tangible results? And while saving time & money. Sounds like a win-win to me. 

Ashwin Singhania

Ashwin is a Co-founder and the CPO of He brings 10 years of experience building consumer and enterprise software products. Before Unwrap, he led product teams building Amazon Alexa's question-and-answer experience and natural language AI technology. Despite Amazon's endless resources, Ashwin's teams struggled to efficiently translate distributed feedback from their millions of customers into data-driven insights, inspiring the solutions Unwrap delivers today. Prior to Amazon, Ashwin led product teams at a Santa Barbara-based technology startup, Graphiq, which was acquired by Amazon in 2017 to power Alexa. He holds a Bachelor's degree in Computer Science from UC Santa Barbara.

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