We look at how brands are increasingly tapping into text analytics to improve customer experience delivery and understand their market better
One of the common applications for text analytics is for analysing customer sentiment, through determining the adjectives and other descriptive terms that consumers use when discussing brands and products.
Much of this capability was pioneered by Massachusetts-based company, Lexalytics. CEO, Jeff Catlin, believes his firm was the first to offer a sentiment analytics capability when it launched in 2004, thanks to its focus on analysing natural language.
“The first market that was a strong pick up was marketing, for what has become known as social listening, and particularly in sentiment analysis,” Catlin says. “Historically, we were a company that provided a lot of the backend technology to the social listening providers – the Sprinklrs and the HootSuites and all those guys.”
While the technology is constantly improving, Catlin and others readily concede there are many tasks where it can’t do the job as well as a human.
“I’m not sure we are ever going to be all the way there – it is a really hard problem,” he says. “And you know how hard the problem is when humans can’t do it.
“The way you do actually measure the accuracy of sentiment is what’s called inter-rater agreement, which means you have humans read something and you ask them if it is good or bad. If you can get them to agree nine out of 10 times, you are doing great. But the fact is well trained humans can’t agree more than nine out of 10 times.”
Where the machines do shine, however, is in the volume of words they can analyse – something no commercially-viable team of people can match. And on the accuracy front, companies such as Lexalytics have developed domain-specific variations for industries such as airlines, hotels and pharmaceuticals.
“That gets accuracy up within three to five points of humans,” Catlin says, adding that the stage of development will be around emotional measurement.
“In contact centre conversations, ’disappointed’ is not nearly the same thing as ‘angry’. But if they can figure out that you are angry, they can route the calls differently, because the likelihood of a churn is very high. That is more than sentiment, because you can measure impact from it.”
Those capabilities have been enhanced further through the use of machine learning, to allow the software to self-tune its accuracy based on feedback.
In recent years, Catlin has seen clients increasingly use the technology as an early warning system to determine when sentiment might be turning. The rise of social media has meant that bad news can travel fast – often spreading far and wide before the organisation affected is even aware of it.
“We’ve got customers like Microsoft that are very worried about missing things particularly racial or sexist, for instance,” he says. “There are a thousand ways to be racially insensitive. The technology is getting better at synonym resolution, and the software learns more and more of them.
“You can feed it data and try and make it be racially insensitive. And that is where machine learning really shines.”
Ultimately, Catlin believes the technology will expand range of factors a marketer can monitor.
“Marketers are currently very much built around knowing things they know they want to know,” Catlin says. “They don’t do so much ‘tell me what I need to know’, or unguided discovery. That technology is getting quite a bit better.”
While text analytics can reveal a treasure trove of information about what customers say, its power can be amplified further when combined with other data sources. According to SAS’s Frost, clients in the past 12 months have begun realising text only provides part of the client story.
“What we are seeing is organisations now using text as one of the assets and combining it with other information,” Frost says. “The CMO role is evolving and the CMO is now being asked to be the purveyor of all insights for customers. They are being forced more and more to have a holistic view of the customer and to provide these capabilities into the sales team.”
It is in interactions with the sales team where these capabilities of combining text and other forms of customer data might prove most useful, Frost added.
“When you rang that call centre, they already had a position on whether you are a churn risk or an upsell/cross sell opportunity, or somewhere in the middle,” he says. “They can combine that with what you are saying and what the agent is saying and look and see if the conversation is moving towards or away from a sale, or towards or away from a churn, and prompt the agents in real time.”
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