4 AI startups that analyze customer reviews
Already, as of 2010, a quarter of Americans (24 percent) had posted product reviews or comments online, and 78 percent of internet users had gone online for product research. But those are ancient stats. Numbers are higher now. More recently, BrightLocal found in 2016 that 91 percent of consumers regularly or occasionally read online reviews, with 47 percent taking sentiment of local-business reviews — the tonality of a review’s text — into account in purchasing decisions. Breaking out the figures, 74 percent of consumers say that positive reviews make them trust a local business more, and 60 percent say that negative reviews make them not want to use a business, according to BrightLocal.
So reviews are important, and the feelings expressed are key. To understand review content, including sentiment, at web and social scale and velocity, you need automated natural language processing (NLP) and other forms of AI.
Commercial review-management platforms — from Bazaarvoice, PowerReviews, Yotpo, and others — help brands and online commerce sites collect reviews and redeploy them to boost sales. That’s an important function: Bazaarvoice reports, “Our clients see 65% average lift in revenue per visit and 52% lift in conversion on product pages with ratings and reviews.” Not all platforms bake NLP into their products and services, however. It’s the companies that do that interest me, the ones that look at what’s actually said in reviews, beyond the star ratings. Let’s look at four, and then at do-it-yourself approaches to customer-review analysis.
Customer reviews contain several forms of salient information. First there’s the star rating, but ratings, even when broken out into categories — on Airbnb, for example, categories include accuracy, communication, cleanliness, location, check-in, and value — have zero explanatory power. So we have review text: free-form, voice-of-the-customer reactions. This text tells a story, and stories sell, so we need to know the aspects of a product or service discussed, the wording used to describe them, and the sentiment expressed.
Review text also reveals a lot about the reviewer, as Stanford University Prof. Dan Jurafsky explains in an exploration of review language, Natural Language Processing on Everyday Language. (Jurafsky’s data science study on how restaurants and reviewers talk about food — including the connection between menu wording and item price — is really illuminating. Also, NLP and AI can help in review moderation by identifying abusive language and detecting fraudulent reviews, but those are topics for another article.) Finally, reviewer identity is key: Demographic characteristics such as age, gender, and geographic location, as well as reviewer reputation or ratings and the reviewer’s social profile, come into play, as does review recency.
We’re describing a complex data scenario. A Bazaarvoice blog post will take you through some of the data science challenges, but it doesn’t cover solutions. The startups I will profile deploy analytics — NLP, machine learning, and other forms of AI — to respond to the challenges.
1. Revuze focuses on products and product attributes in addition to brand health, with a couple of differentiators. One is special attention to discovering different ways people talk about a given topic, and a second is the ability to identify sentiment in phrases that lack obvious clues like using words like “good,” “happy,” and “terrible.”
Revuze analyses aren’t limited to reviews; the company’s tech applies applies NLP for topic, keyword, and sentiment extraction from survey responses, call-center text, and social media too. Source text is analyzed against category taxonomies generated via semi-supervised machine learning.
