Using Analytics, Call Accounting and Data Mining to Match a Contact Center's Assets to Its Customers' Needs
January 25, 2016
By Tracey E. Schelmetic
TMCnet Contributor
Even back in the days when call centers handled only calls, they didn’t have it easy. It was always difficult to determine how many agents were required on the phone to meet predicted volume. High turnover often meant making do with lightly trained agents, and customers have always been prone to becoming ornery. Balancing the call volume over available telephony resources, and keeping track of those resources, was an extra tricky task. Today, of course, the complexity and the challenges are much greater. Call accounting, once a vital way to help keep track of how a company’s telecommunications assets are being used, is a much more complex subject than it once was, and it’s not only about calls.
Most contact centers today are engaged in building up additional channels of access for their customers, including Web chat, social media, mobile apps and even video. Getting the channels in place is the first step, and the next step is the optimized routing of all interactions. Since no one can juggle all those balls mentally, or even on a spreadsheet, technology has been stepped up to better manage and balance customer contact volumes, to track and analyze it, and to make the customer journey (as well as the agent journey!) more intuitive and responsive so that customers become loyal to the company’s brand and spread their positive experiences to friends, family and colleagues.
To succeed at these endeavors, companies must understand how to match their assets – available agents to handle certain types of media – to customer needs. This is a huge challenge, according to ISI (News - Alert) Telemanagement Inc.’s Darlene Jackson in a recent blog post. Analytics is the technology that will help contact centers achieve this feat.
“Call center analytics provide insights to call drivers that will assist in formalizing your strategy for the most optimized routing you can provide,” wrote Jackson. “Using interaction analytics such as voice traffic analysis, for example, you can discern when your contact center has the highest call volumes. If, during those times, your tech support center is subject to longer hold or handle times, your call center strategy might be to add Web chat. In this example, web chat would allow your customers to access tech support directly from numerous devices, and agents could potentially service several inbound requests at once.”
Essentially, good analytics can help you understand what your customers need, and allow you to adjust your existing technology (or add new technology) in order to modernize your customer support offerings. Part of this strategy, however, means learning as much about your customers and their needs as you can. Most companies assume they know what their customers want, but they’re surprised when they actually use analytics to attain this information. (After all, not all customers know what they really want, either!) Jackson recommends using data mining.
“While there are no cookie cutter solutions, data mining is one way to get the most information on your customers that you can,” she wrote. “Information on the ways they deal with you as well as your competitors, pulled from social media, surveys, comment and suggestion tracking, or other customer service channels, will be useful in choosing the best contact channel(s) for your customers. Once you have a grasp on your customers’ preferred interaction methods, you can provide them with the optimized point of contact.”
Knowing your customers and your contact center capabilities is the message here. But “knowing” takes a bit of effort, and really understanding is likely to take technology in the form of analytics and data mining. If you can’t understand how best to use your own assets, you’ll never be able to meet customer needs.
Edited by Stefania Viscusi