Improving Contact Center Interactions with Artificial Intelligence

Of all the use cases for the myriad dimensions of Artificial Intelligence—including technologies both central and contiguous to AI—that for implementing intelligent contact centers is one of the most convincing.

And, according to Automation Anywhere CTO Prince Kohli, it’s also one that’s dire to the modern enterprise.

“One of the reasons it’s extremely important for companies to do a good job on contact centers is that’s a touch point for them with their customers,” Kohli explained. “So, when someone is calling in or interacting by direct phone, chat interface, or something else, that is how they judge a company: how quickly they get a response, what is the level of the response, how effective the agent was.”

An artful confluence of Robotic Process Automation, machine learning, taxonomies, and cloud computing can empower contact center agents with all the information they need to swiftly understand who customers are, grasp the reason for their interactions, and complete their requests in a speedy manner to improve customer satisfaction.

That they’re able to do so by automatically accessing any number of disparate systems and technologies on the backend only increases the ROI for investing in such a solution, making AI more ubiquitous across the contemporary business landscape.

Information Retrieval

RPA plays a critical role in the combination of the foregoing technologies for creating intelligent contact centers. Its dynamic bots are able to swiftly retrieve information from a diverse array of IT systems to give contact center agent all the relevant materials needed to satisfy customer demands. By simply identifying customers based on phone numbers, their login credentials, or other identifiers, bots deliver an initial level of automation by gathering customer data and presenting it to agents in a single pane of glass.

As soon as customers contact organizations, “a simple RPA interface will have all the information about that customer brought up without the agent having to access it,” Kohli mentioned. “Automation is running on your behalf without you having to pick it up.”

Relevant customer details retrieved from various systems of record include things like “who is this customer, what do they spend with me, what accounts do they have with me, do they have a ticket that’s currently open and if so, how long has it been open, what is the status, and many other things,” Kohli commented.

Automating this information retrieval step—and presenting it in a single pane of glass—conserves a significant amount of time that agents would otherwise devote to gathering knowledge about the customer from multiple systems, which delays time to action and is prohibitively labor-intensive.

Natural Language Processing

In addition to collocating relevant customer details for contact center agents, bots also work with a variety of NLP techniques for understanding the reason customers are interacting with them. Furthermore, they can then initiate workflows to fulfill those needs.

In some instances bots may utilize machine learning models to extract relevant entities to ascertain the intention behind a customer’s interaction. For example, if a customer calls to ask for a credit limit increase “at that point there are various AI systems that we can incorporate to extract the question from the question,” Kohli revealed. “There is a question in natural language, but using trained models we can extract the intent from the question.”

Organizations can utilize NLP solutions from the major cloud providers or their own bespoke text analytics options based on enterprise taxonomies to facilitate this part of the automation. Even better, once the customer’s needs have been identified, bots can engage with any number of systems to trigger the action needed to meet those needs.

For instance, if a customer wants a new credit card mailed out “the agent doesn’t have to spend time going to systems to do that,” Kohli noted. “He can just click a single button in his RPA interface and the automation, whether it’s local, remote, or has to go to a different set of systems, it kicks off and does that.”

Cloud Ready

By automating all the behind the scenes interfacing with numerous information technology systems, intelligent bots allow contact center agents to focus on ensuring customers needs are met—which may require comparing different service plans, listening to a story about a previous experience, or figuratively handholding them through a complex buying process. The cloud plays a pivotal role in this assemblage of AI technologies that includes NLP, machine learning, RPA, and machine reasoning.

As Kohli commented, it’s the ideal setting to provide these interactions because everything is simply an API call away. In some ways, whether accessing various machine learning models or other approaches for NLP, “it’s an API-driven thing,” Kohli admitted. “Since we can drive APIs, we can talk to APIs and let APIs talk to us; we can extract information; we can make those calls.”

And, the smart bots at the heart of RPA can help contact center agents—and their supporting organizations—perform better to deliver an incomparable customer experience.

Contributor

Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics.

Opinions expressed by contributors are their own.

About Jelani Harper

Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics.

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