AI offers an efficient, cost-effective way to unlock actionable insights from your reviews. Watch this VB Live event to learn how AI and machine learning can help you to deal with the negative reviews, resolve customer pain points, and build trust.
Consumer trust in information that’s available from businesses is dropping drastically. Almost 85% of millennials don’t trust traditional advertising, and 70% rely on reviews and recommendations from other customers, which of course includes online feedback.
A lot of businesses don’t like to look at their negative reviews, and don’t know how to deal with them or respond to them. But 82% of the top-performing companies report paying close attention to the human experience around digital and tech. That means keeping up-to-the-minute tabs on your reviews.
“One of the greatest challenges is trying to take advantage of this feedback and really use it to turn around the conversation,” says Ramin Vatanparast, chief product officer at Trustpilot. “For example, negative reviews are a great opportunity for businesses to reach out to unhappy customers and understand what the problem was with their experience and try to win back their respect, and build trust and credibility.”
Positive reviews also offer vital information to businesses — they’re not just pats on the back, but taken together are an incredibly accurate barometer for the success of your customer service and your products.
Whether it’s reviews, comments in a forum, support requests, or any of the feedback that you’re getting from customers, the challenge from a technical perspective is that it’s quite hard to scale all of that text — the things that people are writing and saying about your company — and understand the core themes, says Chris Hausler, senior data science manager at ZenDesk.
“If you’re a small company receiving 10 to 20 reviews or support requests a week, it’s easy for someone to individually sit down and read those and understand your customer’s perspective,” Hausler explains. “But when you scale that to tens of thousands, or even millions, it’s no longer feasible for an individual, or even a group of individuals, to read and understand all that text.”
That’s where AI comes in. Natural language processing helps identify patterns in that text, understand the core themes in what people are talking about, and give you a perspective that you aren’t able to get as an individual with that scale of feedback.
“The biggest opportunity isn’t just the star rating for those reviews,” Vatanparast says. “It’s looking at the context and being able to take advantage of the tools and technologies that let you understand the sentiment behind those star ratings, analyze your data, and improve your business.”
The other big piece is customer trust, he says.
“When we look to the Edelman trust barometer, you’ll notice that the overall trust level for online businesses, and even online digital platforms, is going down,” he says. “At the same time, fake reviews are creating a huge challenge for consumers, who now need to identify what’s real and what’s fake.”
AI also really has a role to play here, particularly at scale, Hausler says.
“On the fake review side, particularly when it’s coming from bots, there tend to be telltale signs in the ways they communicate that make them slightly different from the way that a human would write a review,” he explains. “Much in the way that we have something like spam detection in your Gmail account, you can train your AI to identify where you’re getting these bot reviews and then move them to the side so you and your customers are not being misled.
Fraud detection models, combined with offering customers the ability to flag suspicious content, lets you secure your customer feedback and make sure the reviews they see in the platform are as legitimate as possible.
“The biggest thing for any organization nowadays is to build strong principles and values around trust and what they stand for, to be able to openly talk about it and share their data and make sure the information they provide their consumers, including reviews, is as clear as possible and as trustworthy as possible,” he says.
For a deep dive into how an AI-powered review platform works to sort, flag, and capture sentiment from your reviews, how to turn negative reviews into opportunities to connect with your customers, and how to implement a fully secure review platform to capture customer sentiment, catch up on this VB Live event now!
- How to find themes in your customer reviews so that you can fix the real pain points instead of putting “band-aids” on each unhappy customer
- Steps to guide your business decisions around what your customers say they want, not what you think they want
- The importance of addressing your most common negative issues first to see an immediate change in your customer satisfaction level
- The concept of always improving your customer experience — searching for trends in your good reviews to turn them into great reviews
- Ramin Vatanparast, Chief Product Officer, Trustpilot
- Chris Hausler, Senior Data Science Manager, ZenDesk