Customer Sentiment Is Becoming a New Imperative
Artificial intelligence has dramatically increased the ability of automated systems to recognize and analyze human emotions, and that has produced a real market opportunity for the analytics industry, according to a new report from research firm Tractica.
The sentiment and emotion analytics market is set to explode, from $123 million currently to $3.8 billion by 2025, representing nearly 3,000 percent growth in just seven years, Tractica predicts.
These colossal growth projections are due in large part to accelerated access to data (primarily social media feeds and digital video), cheaper computing power, and evolving deep learning capabilities combined with natural language processing (NLP) and computer vision.
The top three use cases for sentiment and emotion analysis will be customer experience, product and market research, and customer service, respectively, the research firm reported. Combined, these three areas will represent 71 percent of the total revenue opportunity, according to Mark Beccue, principal analyst at Tractica.
In customer service, the technology can “make live or automated agents better,” Beccue says.
In customer experience, it can “completely reshape how companies engage with customers,” providing not just insight but context to most interactions, he adds.
And in market and product research, emotion and sentiment analytics can prove more reliable than panels, surveys, and focus groups, where “people can often say what they think you want to hear,” Beccue explains. “Their faces often tell a different story than their words, and that’s where the analytics comes in.”
Also driving the continued emphasis on sentiment and emotion analytics is the need for companies to create unique customer experiences, as product and price are no longer differentiators. The ability for companies to come across as empathetic is a huge motivator, Beccue points out.
But, even with technology, correctly analyzing sentiment and emotion is not easy. Humans themselves often have difficulty reading other humans, so can technology really be expected to do it flawlessly?
“It’s still in the early days, for sure,” Beccue says, noting that emotion and sentiment analytics will only really start kicking into high gear around 2022, still four or five years away. By that point, he expects artificial intelligence and machine learning to have advanced far enough to make an analysis with a reasonable confidence level.
The one advantage that machines will have over humans when it comes to this type of analysis, though, is the ability to process data faster. But taking into consideration the necessary context is still a challenge, and it will be for some time to come.
Emotion and sentiment analysis is complex because emotion is complex and not very well understood. Emotion can be deceptive and expressed in multiple ways: in speech intonations, the text of words spoken or written, facial expressions, body postures, gestures, and more. And these factors can all vary among geographies and cultures, making it difficult to create computer models that can be broadly applied.
Also keeping back the technology today is the fact that many vendors are still very specialized, resulting in an industry that is fragmented, according to Beccue.
Going forward, Beccue expects customer service automation companies to really advance the science behind sentiment and emotion analytics, and for customer-first companies to be among the earliest adopters.
One of the innovative uses that Beccue singles out is the Cloverleaf ShelfPoint system, a retail display system that uses artificial intelligence and emotion detection technology from Affectiva to change up the content on in-store LCD displays in response to the emotions detected on the faces of shoppers walking the aisles.
Among other use cases, Tractica expects to see the most growth in the retail, advertising, business services, healthcare, education, automotive, manufacturing, and gaming segments. “A better understanding of human emotion will help AI technology create more empathetic customer and healthcare experiences, drive our cars, enhance teaching methods, and figure out ways to build better products that meet our needs,” Beccue concludes.