IntelliResponse Joins [24]7 for Smarter Virtual Agents

[24]7, a provider of customer support technology, recently acquired IntelliResponse, a top vendor of virtual agent and other web self-service solutions. I did a brief email-based interview with the IntelliResponse team after the acquisition to learn more about how joining forces with [24]7 will strengthen their brand.

IntelliResponse [24]7Prior to the IntelliResponse acquisition, [24]7 didn’t have a virtual agent product. They did have a well-rounded suite of customer support solutions, including dashboards and tools to assist live agents supporting customer interactions via web and mobile chat, voice, and social media. They also had predictive analytics that track customer data and flag patterns to alert call agents about potential reasons customers might be calling. These predictive services are also linked into smart interactive voice response systems to provide customers with a tailored support experience right from their smartphone.

The [24]7 product portfolio, prior to the IntelliResponse acquisition, seemed to be focused on helping live agents and automated systems provide customers with the best support possible. The IntelliResponse virtual agent technology adds a strong self-service component to the [24]7 portfolio. It became clear from our email exchange that the IntelliResponse team sees huge potential in joining their virtual agent capabilities with the predictive analytics that already enable [24]7 solutions to excel at personalized, smart customer support.

So how do predictive customer analytics work? The system gathers information about the customer and uses the data to anticipate what the customer might be calling about and the type of support they need. There’s an online video on the [24]7 website showcasing predictive analytics using the following example:

A traveling consultant who just returned from a foreign country notices that his wireless bill is much higher than normal. When he calls the support line, the system has already flagged this anomaly in the consultant’s account. The system can make an educated guess that the customer is probably calling to get information about these recent high charges to his account.

When the consultant calls, he’s greeted by a pleasant, automated female voice that asks if he’s calling about billing. The automated solution has voice recognition and can understand the customer’s responses. It texts a link to the customer’s smartphone and he can access the link to see details about the charges to his account. He can clearly see roaming charges in a foreign country caused the unusually high amount of his current bill. A live agent can seamlessly engage with him and assist him in adding an international plan to his account.

Predictive analytics seem to be a great match for self-service virtual agent applications. The top goal of self-service systems is to infer the intent of the customer and quickly give them the most accurate answer or set of instructions as possible. Web interactions are complicated. It’s not always easy to understand what the customer really wants when they start typing queries into a search box or a virtual agent’s user interface. But what if you knew where the customer had already been on the website, what actions they’d performed, and you had background information about previous purchases or other account information? And what if you had an analytic engine that could connect the dots to figure out what the customer might be looking for? You could use that information to make the virtual agent look really smart. The days when virtual agents ask “how can I help you?” might soon be a thing of the past. With systems like [24]7 IntelliResponse, they’ll already know the answer.