This vendor-written tech primer has been edited to eliminate product promotion, but readers should note it will likely favor the submitter’s approach.
Artificial intelligence (AI) – when computers behave like humans – is no longer science fiction. Machines are getting smarter and companies across the globe are beginning to realize how they can leverage AI to improve consumer engagement and customer experience.
Gartner research indicates that in a few years 89% percent of businesses will compete mainly on customer experience. Within five years consumers will manage 85% of their relationships with an enterprise without interacting with a human – moving to the “DIY” customer service concept.
That’s why more companies are using social and digital platforms to empower customers and enhancing their contact centers with new AI technology. Interactive voice-response systems that enable agents to target and personalize communications with customers is one such example. Agents can now be armed with intelligence about why a customer is calling before even picking up the phone. The added layer of personalization and customization brings back an element of humanity that has gone missing, and it is occurring without driving up costs.
Surprisingly, consumers are even willing to pay a bit extra for such service, if need be. A 2015 poll of over 2,000 U.S. adults by Harris found that 70% said they would be willing to pay more for a brand with a good customer service reputation. Even more of them, 86%, said they would very likely switch brands after a bad customer service experience.
In many respects, AI is like a freight train racing down the tracks. Steady advances in hardware and software are sparking immense progress in how machines help interact with customers.
Google’s voice recognition technology, for instance, improved to 98% in 2014 from 84% just two years earlier. Facebook’s DeepFace technology now recognizes faces with 97% accuracy. As for IBM’s Watson, its technology is 2,400% “smarter” today than when it achieved its Jeopardy victory in 2011. Voice recognition systems themselves now perform tens of millions of online searches every month.
Consider the innovative developments in real-time speech analytics.
It’s possible, for instance, to feed a machine learning engine with all the attributes of the customer (ZIP code, product type, length of relationship, age, gender, etc.), along with the attributes of all your call center agents (level of schooling, hometown, birthdate, etc.) to determine who would serve that customer best. Then, each interaction can be quantified using any preferred metric – customer satisfaction, up-sale amount, etc.
From there, it’s relatively simple for the engine to “learn” what combination of customers and agents yields the best outcome. This method also can be used for outbound customer interactions. And over time, AI will be used to spot patterns and automatically generate alerts when an agent may be doing something that weakens the interaction, such as interrupt too much.