Creating the perfect product or service is nigh-on impossible—you're bound to have customer complaints pop up from time to time. It's how you deal with these complaints that matters.
Ensuring you're meeting customer expectations is key for business success, but collecting customer complaint data is a big ask. You've first got to collect customer complaints from all over—be it from social media, comment sections, or review pages. You then have to manually revise each and every complaint to ensure it reaches the right team member for speedy resolution.
It's time-consuming and slow, and it reduces the amount of time your team has to focus on the actual problem: customer experience and success.
If only there was an easier way...
No prizes for guessing where we're going with this.
In this article, you'll learn how you can use AI to speed up customer complaint analysis from start to finish.
Shall we?
What is customer complaint analysis?
Customer complaint analysis consists of tracking, collecting, and categorizing customer complaints. This data is then used to adequately identify and handle customer concerns and ensure customer needs are met.
Customer complaint analysis enables businesses to use data from all over the web to understand what customers like and dislike about a product or service. This can be used alongside data collected from internal sources, such as customer service conversations and customer surveys.
Why it's important to analyze customer complaints
There are a number of reasons you should analyze customer complaints received and even more reasons to do it quickly. Take a look.
Highlights problems with your product or service
Customer complaints aren't always directed at your company—they're often in the form of comments or reviews. These can come from social media comment sections, review pages, or any other place your users are talking about your product.
The point is that they often contain information you wouldn't otherwise get when only looking at customer service requests or complaints. When users are dissatisfied with your product or service, they'll often turn to social media—either instead of or as well as—turning to your support team.
Customer complaint analysis enables you to pick up on this dissatisfaction without it being directly communicated to you. By looking through customers' online complaints, you're able to identify issues—both big and small—that customers encounter when using your product.
You can then combine this information with your direct customer feedback to get a wider view of customer issues.
Retain customers if complaints are handled quickly
Another huge benefit of customer complaint analysis is that it enables you to act fast. Customers don't want to wait around for answers—they want their problems resolved as quickly as possible.
AI-powered customer complaint analysis speeds up the entire process. It automatically gathers, analyzes, and categorizes customer complaints—and then ensures they're forwarded to the right team member. For example, if it's an issue with your product's functionality—it's the product development team. If your customer is unsatisfied with the customer service on offer—that'll go to your customer service team.
Streamlining the customer complaint resolution process helps retain more customers as it enables you to proactively solve issues. Ensuring your users have easy access to support is key in keeping them happy.
Improve customer experience
Finally, analyzing complaints helps you improve the customer experience. The insights uncovered can help you identify areas for improvement and future expansion opportunities.
Customer complaint analysis isn't just about attending to issues occurring right now, it's also great for spotting complaints trends and ways in which you can improve the entire customer experience.
How to detect customer complaints with text analysis
Customer complaint analysis relies on text analysis to uncover insights in unstructured data. Using natural language processing (NLP), statistical pattern learning, and other Machine Learning techniques, text analysis considers text against learning data to understand the meaning and themes within the text.
Text analysis can be used for customer complaint management in a number of different ways—let's take a look at some examples.
Use text analysis to categorize support tickets
One of the ways your business can use text analysis in everyday operations is to categorize incoming support tickets based on who they correspond to. This enables you to attend to issues sooner by avoiding the tedious task of manually routing support tickets to the correct department or team member.
The text analysis model provides the categorization predictions, but businesses rely on powerful AI solutions to complete the puzzle. The AI solutions enable you not only to build AI models, but also to automate the process from start to finish.
We'll dig deeper into this process shortly—for now, let's look at how else organizations use AI-powered text analysis for customer complaints management.
Use text analysis for sentiment analysis
Another key way in which businesses harness the power of text analysis is by performing sentiment analysis on customer complaints. Using natural language processing (NLP) and Machine learning techniques, sentiment analysis analyzes text to identify positive, negative, and neutral sentiments within it.
Performing sentiment analysis on your structured and unstructured data enables you to act on it.
For example, you can use sentiment analysis to understand your customers feelings about a new product or service. These could be things they love about the new product, or they could be things they hate about it. You can then act accordingly—whether that's to update your feature onboarding, provide better self-serve support, or make changes to the product.
Use text analysis to categorize emails by content
Text analysis also comes in handy when categorizing incoming emails by content. Text analysis can read incoming messages to understand the contents in order to forward it to the right department or team.
This enables your organization to more swiftly handle and address incoming emails. Customers aren't left waiting whilst your team manually sorts through emails to understand the most adequate next steps. The text analysis model does all the heavy lifting so that the emails can be forwarded as needs be.
Use text analysis for social media listening
Last—but by no means least—we've got text analysis for social media listening. This is a key way businesses use AI to preempt customer issues and complaints.
Social listening involves monitoring the indirect ways customers communicate with and about your brand on social media. This could be as simple as a tweet singing your praises—I just love the sleek interface—or as developed as a full-on LinkedIn rant. Both relate to your organization, but neither is directly addressed to you.
Staying on top of what people are saying about your brand helps you develop insight-driven improvements.
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