Often, AI is portrayed in the media as this ever-growing frenzy that will ultimately lead to robots stealing our jobs. And, how we should fear computers that are more intelligent than we are. Really?
But as 2018 comes to a close, it’s clear some businesses aren’t paying attention to Hollywood’s ominous depiction of artificial intelligence. Instead, they are embracing AI whole-heartedly with the adoption of AI text analysis.
What is AI Text Analytics and Natural Language Processing (NLP)?
AI text analysis is simply the process of extracting information from inside large amounts of text data. Natural Language Processing (NLP) is how a program actually understands this data and makes sense of it. For businesses, Natural Language Processing can help them automate the process of understanding customer comments on a large scale so they can make data driven decisions on how to improve the business.
AI in action: how Thematic’s AI analytics helped a media company
Thematic was recently featured in Stuff showcasing how our work for Sky TV is useful for businesses by applying AI and machine learning (ML) technology to make sense of customer feedback forms. As Richard Macmanus writes, “Unfortunately, that isn’t as sexy a headline as “Elon Musk’s Billion-Dollar Crusade to Stop the AI Apocalypse” (an actual headline last year from Vanity Fair).”
The feature tells the story of how Thematic has been noticed by the Silicon Valley elite and completed the exclusive US accelerator programme, YCombinator, finalizing a US$1.2 million funding round.
AI analytics helps interactively visualize themes in customer data
The benefit of using Thematic for businesses is manifold. The system analyses the free-text feedback submitted in customer survey forms, which has previously been difficult to analyze as companies spend time and resource and still struggle to do this manually.
As Thematic CEO Alyona Medelyan explains in the Business is Boring podcast ,”we automatically find themes” from the raw data and “then we visualize it using easy to understand charts, and let people interact with the data to understand their customers better”.
How do we do this? By using Natural Language Processing (NLP), which essentially analyzes language to identify recurrent patterns in what customers are saying. We also use sentiment analysis to assign a sentiment score capturing how positively or negatively customers feel.
Real-World AI Text Analytics Example: Sky TV
Sky TV has successfully been using Thematic to understand their subscriber’s feedback and get actionable insights, especially relating to metrics such as viewing experience and customer service NPS. Their weekly surveys help Sky TV understand how things such as price increases affect their customers and what they can do more of to increase customer retention.
Sky TV retrieved their survey responses “coded with one or several themes and sentiments, along with interactive visualizations for their employees to view on the company intranet.”
“Sometimes we need machines to help figure us out.”
Further, as the article explains, Sky TV “was able to quantify how customers felt about missing out on a popular sporting event. As I noted in a column last year, this may become a familiar feeling to all rugby fans in the coming years. Rugby rights are due to be renegotiated in 2020 and a global gorilla like Amazon could swoop in and buy them.
Perhaps to test how its customers might react to that, SkyTV used Thematic to analyze feedback about an unnamed “popular sporting event” it has already missed out on. The feedback from customers showed that this event was “very important to a subset of customers who were very vocal”. What’s more, this theme “spiked at the announcement but produced the biggest impact […] when customers expected the event to start”.
The future of AI text analysis in the business world
There is certainly a place for utilizing AI machine learning in the business world. As Richard writes “it’s heartening to see a New Zealand startup providing such a useful AI service to businesses. Understanding customers isn’t easy since we humans tend to be emotional. Sometimes we need machines to help figure us out.”
Read the full Stuff article, published here.