When Done Right, AI Can Turn the Contact Center into a Revenue Generator

When Done Right, AI Can Turn the Contact Center into a Revenue Generator

What if we can make contact center agents better, faster, smarter to more successfully create loyal, forever customers? This has been the goal of contact center software and services companies for the past three decades. Now in 2018, peruse any customer service site or search on the topic of “improving agent performance,” and the popular answers to this question are pointing in the direction of using chatbots, robots, Artificial Intelligence instead of human agents. I believe the answer is not to outright replace agents with machines, but rather to leverage this newer technology to make the actual contact centers better, faster, smarter.

The reality is that customers who engage with a contact center—via voice, chat, email, web, etc.—still want to know they can connect with a human if they want to or need to. In fact, in March 2018, an IDC survey reported that up to 45% of customers still want to talk to a human for emotional or complex interactions. In other words, replacing agents with machines is clearly not something customers want.

Maximizing the capabilities of AI and machine learning is what contact center innovators have to figure out sooner rather than later. There are a lot of really cool and interesting ideas around AI and the struggle of where to start is very real. One way to narrow the focus is to start by looking at what contact centers already do well, then determine how to augment it. For example, intelligent routing.

Intelligent routing at the very basic level is about matching the customer to the agent who is best skilled to serve him or her, and then routing the customer to connect with that best agent. The challenge with intelligent routing is that high-performing agents can easily end up with the majority of customers being routed to them. To transform the contact center into a revenue generator requires improving the performance of all agents, not just the top percentile.

What if we add AI and machine learning to the mix? Use these technologies to make intelligent routing more intelligent by leveraging data analytics to learn all the different behaviors of all the agents in an organization, as well as the behaviors of all the customers when they engage with that organization’s contact center. 

Behavior matching in the contact center is similar to what online dating apps are attempting to achieve. Each dating candidate completes a time-consuming online questionnaire which produces a significant amount of data about each candidate. Having access to all that information, the dating app then finds similarities between candidates to create ideal matches with the end result hopefully being a real human to human connection.

Companies in all industries want to provide that same connected experience. Customers also want the companies they do business with to know them. It seems like an easy match to make. And the logical place for the company-customer relationship to begin is in the contact center. But no customer wants to spend hours completing a questionnaire just to do business with a company. Thankfully they won’t have to.

With the variety of consumer information that currently exists across the Internet, as well as in multiple databases within a company any customer does business with, it is possible for AI to access analytics from a variety of sources. The inclusion of machine learning with AI means companies can now automate the process of “getting to know” a customer and “getting to know” an agent, in order to create a more connected customer-agent experience and lasting customer-company relationship.

Afiniti is leading the way in applying AI and machine learning to contact centers and utilizing behavioral matching to improve the agent-customer experience. It all starts with a customer’s automatic number identification or caller ID from the IVR system. Using the customer’s phone number as an initial data key, Afiniti’s Enterprise Behavioral Pairing™ (EBP) accesses information from more than 100 different data sources from around the Internet. All of this data is then fed into a neural network to compute customer types or customer segments in a matter of microseconds.

Based on the customer and agent data, Afiniti can determine how likely a particular agent is to meet the business objective (sell, subscribe, upsell, retain, etc.) successfully. EBP also takes into account contact center service level commitments and operating rules for a holistic approach to overall contact center performance. Since it is using machine learning, once the software has “learned” a particular customer, it continues to learn and improve, thereby continuing to improve customer experiences.

T-Mobile is highlighted on Afiniti’s website and credits the Afiniti behavioral matching software for giving them a higher conversion rate from prospect to paying customer. The wireless provider says Afiniti is responsible for $70 million in additional new sales annually. If that isn’t evidence of transforming the contact center into a revenue generator, I don’t know what is.

Afiniti recently joined Avaya’s A.I. Connect, an initiative that is dedicated to supporting and promoting the interoperability and value of artificial intelligence and machine learning within Avaya solutions. And we have now expanded into a strategic alliance, with Afiniti and Avaya working together to natively integrate Afiniti AI and machine learning technologies into Avaya Aura Elite by July 2018. Native integrations with Proactive Outreach Manager and Avaya Oceana will follow in the coming months. Avaya CEO Jim Chirico and Afiniti CEO Zia Chishti discuss the benefits of our alliance in this new video.

AI and machine learning are modern tools that many contact centers are struggling to wrap their arms around. While the opportunities for these technologies seem endless, the challenge when thinking about the contact center is to think first about the customer experience. Get that right, and the revenue will follow.

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