What's driving the rise of text analytics and its role in CX

When Norman Peledeau first began developing text analytics software 20 years ago, he could barely get a meeting with potential commercial clients. Now he finds himself at the helm of a company developing one of the essential tools of the social media age.

Peledeau had been tasked with analysing a large volume of text. But when he couldn’t find any simple tools to assist, he set about developing his own.

“I was doing that analysis by hand, and I thought there must be something better than this,” Peledeau tells CMO. “At the time, very few companies were interested in any kind of text analysis, but there were a lot of academic researchers in cultural science and communication doing that.”

Now his company, Montreal-based Provalis Research, has created text analytics software products used by more than 4000 organisations across 80 countries for tasks including media and survey analysis and market research.

The market Provalis plays in is now predicted to at a compound growth rate of 17 per cent until 2023, when it will be worth US$23 billion. And much larger players are incorporating text analytics into their offerings, including SAP, IBM, SAS Institute and OpenText Corporation, as well as a host of smaller established players and startups.

What’s driving take-up

According to Peledeau, one use case accounts for a large swathe of that growth: Social media monitoring.

“This is what has driven a lot of market research companies to look for text analytics tools,” Peledeau says. “When I started and was presenting our tool, people were looking at it and not seeing the point of wanting to analyse text data. And when they were doing surveys, they were not putting open-ended questions in because they know it was time consuming to analyse. Things have changed.”

Peledeau says analytics is now also commonly applied to transcripts of focus groups and contact centre interactions, with some brands now specifically encouraging clients to provide written commentary through blogs, comment pages and online communities.

The recent rapid uptake of text analytics has also been witnessed by SAS Institute.

“Organisations that we speak to now are asking us more and more about text,” says SAS director for presales, fraud and cloud for A/NZ, Dominic Frost. “I would say on just about every RFP response we have now there is now a section on text.”

Such is the demand for text analytics that for experience management specialist, Qualtrics, it is a core requirement alongside its traditional survey tools.

“We wound up with the responsibility for not just helping clients collect the data, but making sense of it,” Qualtrics product manager, Jamie Morningstar, says. “So how do you take the data that has been collected and make it really actionable and really powerful? And that is where a lot of our analytics products come in.

“Text analytics is all about making the text actionable… what text analytics delivers to those clients is the ability to quantify that text data so it can be really actionable and analysable, right alongside the quantitative data they are collecting.”

Morningstar says while the most common usage is to determine how an organisation is performing on the topics it cares about, increasingly analytics being used to find those topics they didn’t care about, but should. These might include emerging or latent issues that pop up too infrequently to be otherwise noticed.

She describes one example at a home Internet installer. “There was a small but powerful underlying theme when installers were smelling like cigarettes,” Morningstar says. “That was a really big driver of dissatisfaction for that very small number of clients reporting that. It’s not common, but it is something that is pretty easily affected by those installers if they know that it matters.”

Another example came about at a website hosting company, where ‘site’ was a commonly used term.

“But then another term that came up was ‘sight’,” Morningstar says. “Even though ‘sight’ and ‘site’ are not synonymous, these customers were mixing the words up. If our machine learning tools hadn’t surfaced this a whole body of related concepts would have never been seen.”

Up next: How text analytics is improving the way brands gauge customer sentiment, plus combining data sources