Video: Analyze Customer Feedback With NLP to Get Actionable Insights

Most companies have more sources of customer feedback than they realize. Customer surveys and reviews aside, call logs, support tickets, and brand mentions on social media all are customer feedback, rich with insights.

Many of the world’s most successful enterprises have automated the entire process of gathering and analyzing their customer feedback using AI-driven Natural Language Processing (NLP) tools. It gives them an edge over the competition and allows them to extract actionable insights.

If you haven’t automated analyzing customer feedback yet, you’ll want to start doing this, ASAP. Going through customer feedback allows you to understand your target audience on a deeper, more intimate level. It enables you to improve upon your product/service to win over more customers.

In this guide, we walk you through all you need to know about analyzing customer feedback with NLP, including:

• Why it’s important to review customer feedback
• The difference between data and actionable insights
• How to transform your data into insights
• Analyzing customer feedback using NLP
• A step-by-step guide to collecting and analyzing customer feedback

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Let’s discuss how to analyze your data to come up with these insights.

If you’re looking at quantitative data, this is fairly straightforward. Say that you’re using the Customer Effort Score (CES) to benchmark your customer satisfaction levels, for instance. Because the CES is recorded on a numeric scale (from one to seven), it’s easy for you to track changes in your score, and come up with insights based on any fluctuations.

But with quantitative data, on the other hand, things aren’t as simple. Say you send out a survey, and within that survey, you have a free-text question asking your customers: What do you think CompanyName can improve upon? Obviously, the data that you collect can’t be textured translated into numeric results which are easier to analyze.

Now, if you intend to manually collate and read all the responses, then analyze the content to come up with insights, this will take a lot of time and effort. Here, a workaround is to use Natural Language Processing (NLP) and machine learning (ML) to do the heavy-lifting. These tools analyze qualitative data, and churn out insights that are derived from your customer feedback.

Take Wonderflow. Our tool utilizes NLP to mimic the human ability to comprehend texts. Once you feed your data into the tool, the tool will work to understand the data, and conduct sentiment analysis to gauge whether your customers are satisfied, neutral, or dissatisfied with your product and service.

At the same time, Wonderflow also generates automatic insights based on your customer feedback. You can receive the insights in your inbox in real-time, and these are also made available in Wonderflow’s reports, where the tool shares recommendations on how you can implement your learnings into your product life cycle.

Recently, Wonderflow was selected by independent research firm Aragon Research as one of the companies making an impact in document analytics. Check out the report here.