EBay Finds a NICE Way to Save

The e-commerce marketing service eBay Enterprise processes roughly 17 million customer contacts per year across a full range of channels, including phone, chat, email, and social media, on behalf of its affiliated retailers. Its key focus is on lowering average handling times; delivering consistent, high-quality service; and enhancing consumer experiences. It is accomplishing these goals with the help of the Interaction Analytics solution from NICE Systems.

The company currently employs 2,400 agents across four contact centers in Eau Claire, Wis.; Brunswick, Ga.; Melbourne, Fla.; and Merritt Island, Fla. Many of the calls it fields have to do with customer questions about promotions on the retailers' own Web sites or on the main eBay site.

"As we started looking at ourselves, we kept asking what more we could do for our customers, and we were looking at how to improve the consumer experience and tie in the voice of the customer," says Robin Gomez, director of operational excellence at eBay Enterprise. "We saw an opportunity to run analytics on all the calls that we were recording but doing nothing with."

The company created its Intelligent Commerce Care model based on the analysis of multiple contact types. It used NICE to assess cost per contact, analyze volume patterns and trends, evaluate agent performance based on contact type, and correlate average call handling time, dissatisfaction levels, and repeat calls to identify areas that required attention.

And it was able to gain insight into how new hires performed against tenured agents to improve its "on-boarding" and training processes. By using analytics categories to focus on high periods of silence, the company could identify the reason for the silence and recognize where certain agents needed additional support.

Interaction Analytics has now become "a pretty important arrow in my quiver," Gomez says. "The biggest benefit is the ability to leverage the information that is captured. We take the raw data from the phone calls and leverage it to improve our operations and address the speed and quality of the interaction."

By implementing the NICE solution, eBay Enterprise has succeeded in redesigning process call flows for efficiency and dramatically improving overall efficiency and the customer experience. For one client in particular, eBay Enterprise cut average call handling time by 17 percent, improved first-call resolutions by 1.4 percent, increased customer satisfaction by 3 percent, increased the sales close rate by 32 percent, and saved $2.4 million. At the same time, it decreased the number of calls escalated to tier-two agents by 23 percent.

The benefits are very specific to each client, Gomez explains, but they are typical of the results eBay Enterprise achieves for its clients.

Due to the solution's success, eBay Enterprise has gone through five additional rollouts of NICE Interaction Analytics, and it's in the process of implementing text analytics for its chat and email interactions. Phone is by far the company's biggest customer channel, but it has seen strong growth in the email and chat channels.

"NICE is committed to helping organizations create perfect customer experiences by ensuring that employees are engaged, knowledgeable, and ready for every interaction," said Miki Migdal, president of the NICE Enterprise Product Group, in a statement. "This is a terrific example of a large contact center operation that has made significant improvements in how it manages handle times and prepares its agents so that it can deliver an effortless and consistent customer experience across multiple service channels and touch points." 

THE PAYOFF

Since deploying Nice Systems' Interaction Analytics in its contact centers, eBay Enterprises has been able to produce the following results for one retail client:

• cut average call handling time by 17 percent;
• improved first-call resolutions by 1.4 percent;
• increased customer satisfaction by 3 percent;
• increased the sales close rate by 32 percent;
• decreased the number of calls escalated to tier-two agents by 23 percent; and
• saved $2.4 million.