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Bots Are Here to (Self-) Serve You
Thanks to machine learning and AI, bots today can usually give customers what they want.
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Instead of focusing on bots as substitutes for customer service reps—a discussion that often leads to anxiety over lost jobs—perhaps we could focus for a moment on bots as a substitute for traditional self-service. Self-service has gotten much better recently, but then again, it was pretty awful at its inception.

In my experience, old-school self-service was not much more than old service systems gussied up with a nice front end. There was always the implicit assumption that the user was someone who knew the process, which, admittedly, was a big ask. Quite often customers didn’t know the processes but they knew what they wanted and often found a service application standing in the way. As a result, about 10 years ago it became high season for vendor-bashing, and customers used newly available social media as their bashing tool. Because the internet is forever, you can still search for and find angry complaints.

That was then. The industry took some lumps but continued to innovate. Today, thanks to machine learning, artificial intelligence, and a few algorithms, customers can usually get what they want from a bot. The most successful places where bots are in common use involve straightforward and repetitive actions like buying movie tickets or visiting the ATM. We might still think of such activities more as self-service than bot-assisted service, but the lines are blurring.

Moreover, and very interestingly, bots are not taking over jobs, unless you count the ones carried out by customers performing self-service. Instead, they’re developing new approaches. We’re seeing new service niches opening, places where there were no jobs before, in e-tail, for example—and the jobs are being filled by machines. That’s because bots let us work on an exceptions-only basis; we don’t have to put people into the majority of situations that can be adequately handled by bots, which frees agents up to wrestle with the thornier issues instead.

This is much like the Internet of Things in one respect. Machines with sensors can report looming problems that can make field service more efficient and effective by pinpointing issues so that we can act proactively. The sensors and systems that capture such data fill a long-standing need that could never be filled by people due to the expense involved.

On the trickier side is the question of customer engagement and bots. In my research, engagement is a result of an emotional attachment—a customer in some way identifies with a brand and that drives loyalty. But do bots risk reducing engagement? Would a customer—you, for instance—develop as much attachment to a bot-based service (or possibly sales) system as he would with a person?

Perhaps the question of bots and engagement is not such a big issue because engagement really should be between the customer and all things that stand for the brand, including service but especially the experience of interacting with a product and all of its adjacencies.

In the right situation, a bot can be a great fit. Bots have faster access to information than people do, and they can work 24/7. On the other hand, some of the places we see bots being deployed also represent situations that may be at risk of evaporating. For instance, we can off-load customer service to ATMs or ticket kiosks, but the functions you perform at those places are also rapidly moving to the handheld device. Additionally, people already know the interface for their devices, and they are often comfortable talking to them.

Perhaps all of this will help reduce the anxiety involved in watching new automation take over blue- and even white-collar jobs. Someday, no doubt, machines will be even more capable, and more jobs might be at stake. Then again, workers looking for the next thing to hold their interest might head for the exits early. One of the fastest-growing jobs in the United States is wind turbine maintenance technician. You don’t major in it, you learn on the job—and there are lots of jobs like that on the ­horizon.


Denis Pombriant is founder and managing principal of Beagle Research Group and the Bullpen Group. He is a widely published CRM analyst in the United States and Europe, and his latest research spans all areas of social CRM, cloud, and mobile computing. His latest book, Solve for the Customer, is available at Amazon.com.

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