You might remember HAL 9000, the Heuristically programmed ALgorithmic computer from Arthur C. Clarke’s novel 2001: A Space Odyssey. The system was a calm voice amid the chaos, and an all-seeing camera eye that captured everything around it. Yes, I know things went a little crazy for that mission, but a lot of enthusiasts on the web think HAL was doing exactly what he was asked to do.   He was asked to produce an outcome.  While I don’t think HAL would be a very good contact center coach or manager, I do see how he could be an incredible teammate for a command center.

The contact center world is changing. Business leaders are looking for ways to meet their key business objectives more effectively and efficiently. But with the ever-changing landscape of customers and resources, and the mountains of data generated every day, it’s a huge effort that can feel impossible to manage.

Contact centers are being called on to do more than just answer calls quickly and efficiently. More companies understand the tremendous opportunity a contact center has on their bottom line. Contact centers now look for metrics to show how they reduce costs, increase revenue and improve customer satisfaction for the company.

It used to be easy; customer interactions would come in and we could measure the speed of answer or service level to gauge our success. Well, money doesn’t buy happiness—and service levels don’t produce business outcomes. Now we are being asked to drive first contact resolution, Net Promoter Scores, revenue, and customer retention, among other business objectives, to measure our success. The problem is, these aren’t directly related to service levels. Just because you answered an interaction quickly doesn’t mean that you did it well enough to keep the customer from contacting you again, telling others how great you are or even remaining a customer.

So, you pull in disparate data sources to try and sort out the factors that impact your business. And you make assumptions about what drives your performance results. Customer needs, profiles and expectations are not all the same. Agent skills and behaviors are not all the same. No one can be good at everything, but everyone is good at something.

You need to match your customers with the agents that are good at what those customers want. And that is a big job. There is the manual effort that goes into analyzing big data for insights. More effort goes into building elaborate routing logic, rules and agent-skill profiles. Even after you’ve done all this work, the whole environment could change by the time you get it into production.

Channeling HAL and Artificial Intelligence to Connect With Customers

Let’s go back to our new teammate HAL. HAL was an early interpretation of Artificial Intelligence (AI).  We’ve come a long way since that 1967 film.  Now AI is real and ready to help us take on this big job.  AI loves to process huge amounts of data and then learn from that data to determine both customer and agent attributes that drive business outcomes. AI can leverage all that you know about customers and agents, and then use that data to determine what’s going on in the contact center now to connect each customer with the best agent—every time.

But AI shouldn’t be a prima donna, keeping all the data, learning and control to itself. AI should be a tool that provides visibility into data, relationships that drive performance and new opportunities for improvement.

Learn more about leveraging the power of AI to turn your data into actionable gold and discover ways to connect your customers to agents that will drive results. Genesys Predictive Routing: You don’t know how good you can be. Register for the June 27 Live Webinar: Technology Insights: How AI is Driving a New Era in Customer Engagement.

Charlie Godfrey

Charlie Godfrey

Charlie Godfrey is the Global Solution Services Director at Genesys. He has over 20 years of experience in contact center management and customer experience design. He has held positions of thought leadership in both technical and business organizations. Charlie was...