Voice recognition and machine learning make service bots better

We are on the cusp of a technological revolution whereby increasingly sophisticated tasks can be handed over from humans to machines. Organizations are embracing advancements in artificial intelligence, robotics, and natural language technology to adopt platforms that can “learn” from experience and actually interact with users. The next wave of these chatbots will have enhanced real-time data analytics and automation capabilities and the ability to integrate intelligence across multiple digital channels to engage customers in natural conversations using voice or text.

So what will the integration of this technology mean for you as a customer? When you have a question about a product or service, you will be presented with the best agent, who possesses the entire company’s collective experience and a huge wealth of knowledge to address your issue.

Please hold the line

Think about what happens today when you call your bank or the help desk of an ecommerce site. In most cases, you reach a recorded message asking you to select the reason for your call and maybe prompting you to solve basic issues by going to the website instead.

This approach may help companies cut customer service costs, but most people I know find navigating these menus quite frustrating, leading them to hit 0 to just get through to a human operator. That doesn’t necessarily help the situation, because it often leads to long hold times waiting for an operator to become available.

And it’s not uncommon for the long wait to be followed by a painful conversation with an agent whom you can’t understand because they have a headset problem or who puts you on hold to transfer you to another operator. You get the point.

Wanted: Robot with customer service skills

Now imagine your call is answered immediately by a chatbot with a name and a voice you recognize. After a few security checks to confirm your identity — maybe performed invisibly using voice recognition — the bot knows everything about all your interactions with the company, including orders, failed orders, searches on the website, past transactions, previous calls, and anything you’ve shared on this call without you having to wrack your brain, trawl through your inbox, or repeat yourself.

The bot addresses your concerns using the collected wisdom of the company, including the absolute latest data on issues other customers face and how to solve them. It is able to take action to rectify most problems without calling in specialists or requesting approvals, and it can do so without errors. Later the bot follows up with you to make sure everything went OK using the medium of your choice: email, messaging apps, a call back to your preferred number, a notification to your account on the website, or any combination of these — again, without errors.

The information generated throughout the entire process can then be leveraged in myriad ways, from improving the experience for the next caller, to refining products and services, to personalizing future interactions with you. If properly implemented, the process becomes a virtuous circle.

More often than not, your issue can get resolved in the first interaction. This type of machine-led interaction can increase customer satisfaction and inspire customer loyalty if well executed. If customers get to choose the medium they prefer, if the service they get is consistently faster and more effective, and if the bot has the best characteristics of a human agent (and none of the worst ones), customers could actually find the experience superior to dealing with a human. This is especially true for the generations of users who have grown up seeing smartphones, social media, and chat as an extension of themselves.

Coming to a call center near you

Facebook Messenger chatbots are emerging as a popular customer engagement platform for retailers. For example, eBay has a ShopBot that makes searching, comparing, and buying items quicker and more seamless. The machine learning capabilities of this bot mean that it can make more targeted recommendations to users based on the items they have shopped or searched before. At the higher end of the scale is Burberry — a brand that is well-known as a digital trailblazer in the fashion business. The brand’s chatbot has a wealth of data, imagery, and links to the company’s latest collections to help customers put together their desired look.

As chatbots become more commonplace in customer service, certain jobs will be replaced by machines and humans will be free to deal with more complicated or sensitive tasks faster. This shift will enable humans to offer augmented, enhanced service for the most valuable customers and open the door to hybrid service models, wherein machine learning-enabled technologies help humans even more. These hybrid models are already implemented, and widespread use of chatbots will happen faster than you might think. Given that it can take a long time to retrain and reskill employees, it is imperative that companies begin planning for these transitions now.

The implications for the human workforce that are created as a result of advancements in chatbot technology are likely to be profound. As the battle between humans and machines intensifies, there are bound to be both winners and losers. The winners will be the individuals and organizations that look most aggressively at how they can leverage these latest digital technologies for a competitive advantage.

Peter Quinlan is vice president of unified communications and collaboration product management at Tata Communications, a global provider of telecommunications solutions and services.