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IVR and chatbots: Super-agent or nightmare-agent?

IVR and chatbots: Super-agent or nightmare-agent?

Humans can be just as frustrating as a badly designed IVR, voice or chatbot.

When I recently called to sort out a problem with my broadband, I knew the person I was speaking with couldn’t help me, but he insisted on going through the troubleshooting process he’d been taught. Eventually, I got escalated to the tech team and we got it sorted, but it illustrated the problem with bots, and why you need a Bot Management System to keep them in check.

In the last blog in this series I explained the fundamental differences between web, mobile, and conversational AI. We established that customers expect bots to speak and listen like humans, and be smart, helpful and resourceful like humans are too.

But even the best AI doesn’t have the flexibility and robustness of human intelligence.

Human vs Bot

That doesn’t mean bots are useless, they’re just narrow. Like a rookie customer service agent who has only been trained on a single process, say processing the return of an unwanted item. Outside of that, they’re not going to be very helpful. If the rookie returns agent receives calls or chats about making a return all day, they’ll do a great job. But if someone starts asking about doing an exchange instead of a return. Or wants to know if there’s a larger jacket size in stock because the one they’re returning was too small, the rookie returns agent is going to struggle.

Now, a human rookie returns agent will use their general human intelligence to fix the problem. They’ll ask a manager for help or transfer the customer to a more experienced agent. But a returns bot won’t do that, they only know about returns. They don’t know how to tell their manager there’s a problem. And they only know how and when to get another agent to help if they’ve been given explicit instructions on how to identify when they need to get help, and they’re taught how and where to transfer a call or chat.

It’s actually even harder for bots. Imagine if our rookie returns agent isn’t a native speaker of the language most of the customers use. What if they only learned enough English to handle the returns process. They’re listening out for the word ‘return’, and they know if they hear it they need to say: “what’s the order number”. They don’t really know what the question means, but they know that most people respond with an 8-digit number that they can put into the system to process a return. If someone doesn’t respond with an 8-digit number, things will get awkward quickly.

Nightmare-agent

This is the bot’s predicament. They know just enough language and just enough of the business process to do very specific things. If well designed, they can do those specific things just as well as a human agent. Sometimes even better. Because they can use a voice that your customers respond best to, and that matches your brand. They can access data faster than a human agent. And they can help customers 24×7.

But outside of that narrow envelope of expertise, they quickly change from super-agent to ignorant, annoying, relationship-killing, brand-destroying nightmare-agent. Worse than that, they’re an ignorant, annoying, relationship-killing, brand-destroying nightmare agent who won’t complain to their manager, won’t ask for help, and won’t ever leave. They’ll just keep having thousands of terrible conversations. Your nightmare agent. The worst possible employee. Talking to your customers, at scale!

Super-agent

Hiring an employee is the final step in a process that started with a business strategy, departmental objectives, resource forecasting, job spec creation, advertising, interviewing, and objective testing. Once hired there will be a probation period, and the new employee will be set clear goals with measurable targets and outcomes. Training will be put in place, and the employee will meet with their line manager regularly. Before and during their 1-2-1 meetings the line manager will measure the employee’s performance and assess progress against the targets. They will discuss areas for improvement, and if targets are being met or exceeded, look for opportunities to help the employee develop their skills and deliver even more value in the business. This is line-management 101. You wouldn’t hire an employee without doing these things.

The Bot Management System

Now, as we saw above, from the perspective of the business, bots are very similar to a rookie human agent. With the right training and support, bots can do a human-like or even super-human-like job in a very narrowly defined area.

And they can do it at a lower cost. That’s ideal. A superhuman job for less than the cost of a human.

But outside of that narrow area, our super-agent becomes a nightmare agent. You can stop that happening with a Bot management system that ensures you hire the right bots for the right job and that you train them to do it brilliantly and measure their performance. A Bot management system lets you identify problems with your bot workforce and fix them. And find opportunities to enhance the impact your bots are having.

The real problem with bots today is not a tech issue, or a design issue. That’s not to say that better tech and better design aren’t really important. They are. They expand the capabilities of bots and the value they can deliver. But without a Bot Management System you can’t expect a decent return on your investments in IVR, voice and chatbot applications.

I’ll be continuing this series with a look at how to build a Bot Strategy  That’s the first step in creating a bot management system. If you missed them, check out my previous blogs in this series: What’s the best Conversational AI platform and Why your IVR, voice & chat bot need HR for AI.

Did you have a super-agent or a nightmare-agent experience recently?

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