Podcast: Comparative text analytics with Ben Hookway
Ben spoke with us about the possibilities and value that is sitting in our owned, qualitative text data, specifically when it comes to finding differences among various groups.
On this episode of Now that’s Significant, we were joined by CEO of Relative Insight, Ben Hookway. Relative Insight is a comparative text analytics software company that helps organizations generate actionable insights from text data - using AI-powered natural language processing (NLP) coupled with advanced comparative linguistics. Relative Insights can analyze any source of text data and drive enhanced contextual understandings of target audiences, competitors and trends. By comparing any amount of qualitative data, Relative Insight reveals differences and similarities in how people and brands speak, using methodology that enables users to glean unique insights in a fast and scalable way. Ben came on to talk about the value that is sitting in our owned, qualitative text data. Rather than analyzing all data that is common between two people groups (generally 90-95%), Relative Insight scrupulously digs into all that is different between the two groups (normally 5-10%).
Ben says many companies already have massive amounts of text data, such as open-end survey responses, product and company reviews, social media, customer experience data, call transcripts. The problem is that they don’t have a way to extract the full value from text data. He believes that text holds enormous potential for companies because it's one of the fundamental data sources that answers the question “why.” While some other data sources can tell you “what”, text data is a great resource for understanding the motivations and feelings behind what is happening.
Ben says we can mine the value of text data well beyond the typical “word cloud” or sentiment analysis. To illustrate this point, he dives into the way that Relative Insight came about – starting as an academic and government agency partnership that used the technology to identify child stalkers online by very small differences in the text from online conversations. The advanced algorithms were able to pinpoint the small differences between a “40-year-old man doing a very good impression of a 13 year old girl” and a real 13 year old girl. It is these subtle differences in language – sometimes as low as 4 or 5% - where the real value of the text data lies. Relative Insight began applying the tech in other situations, and now is also experiencing massive growth in the commercial sector.
He gives some fascinating examples of how real companies have used text data to better reach their target audiences – not by just looking at what the text data was saying overall but by examining the differences among what various audience groups were saying. One example was a makeup company seeking to reach a younger demographic and identifying that the desired audience used the word “wear” rather than “apply” (preferred by the older demographic groups) when referring to makeup. This caused the company to change the messaging in its ad campaign to better resonate with younger audiences. They were saying the wrong thing and didn’t even know it. He said, “it's only by undertaking a comparison that you discover the differences and get a lot more information.” He also mentions a campaign where text was analyzing pre and post ad campaign launch, to see how the ads affected perceptions.
After briefly covering the mechanics and ease-of-use of uncovering insights using Relative Insight’s platform, Ben talks more about how the measurable impacts of properly using the rich resource of text data. He says much of the ROI surrounding their particular offering is speed to insights, leaving manual coding of things like open-end survey responses, behind. This allows companies to include valuable open-end survey questions without the back-end bottleneck of traditional manual techniques. He also touches on the wide number of types of companies that are using text analytics to inform decisions, and the value that they are finding with the Relative Insight approach.
Some macro themes in the space are pointing toward an increased use of text analytics, as businesses are seeking to understand the changing marketplace and changing consumers. Increased reliance on data in decision-making will drive a need for this type of analysis. In addition, in the technology space we are seeing more and more cloud data – essentially making data available to be analyzed. Text data is being surfaced inside this environment, and there are big strides in technology to enable access to this data, as well as other sources.
In short, Ben says that text analytics is poised to become a bigger and bigger part of the business intelligence mix, now that technology is making that data more accessible. Companies will begin using text as a strategic data resource when they begin to realize the wealth of information held in the text data that they already have.