Call Center Analytics: It's not what you know, it's how you use the information

If you're in charge of a contact center or agent team, chances are you have very specific measures for successful performance. But how many of those metrics are just efficiency numbers, rather than true measurements of performance?

Since many contact centers rely upon data from multiple places but have no aggregated view into it, planning departments and management are often left to the task of manually aggregating and analyzing data with desktop tools with limited metrics such as average handling time, number of calls completed and number of dropped calls.

Many contact center managers are frustrated with the information put out in the "standard reports" and instead use these reports merely as data sources for their own analysis. Analytic applications, however, give you the ability to measure many factors of performance for a better understanding of contact center effectiveness.

How is effectiveness different from efficiency? Performance analysis is one area where contact center managers have been able to look at contact center effectiveness and help team managers point team members in the right direction. Once a problem is identified, team managers can drill down into the specific KPI to determine the root cause of the problem. For example, if an agent is not meeting their target in 'not ready' time, a team manager can drill down into the small time pieces to see if there is a particular time of day where he's not meeting target. That helps managers identify where to focus. In the past, managers might only look at problems such as average handling time without understanding why an agent wasn't meeting that requirement.

The ability to drill down is equally important for revenue generating activities. Team leaders are able to look at which products agents spend their time selling so they can determine if they are concentrating on products that are easier to sell vs. high value products that might be more difficult to sell. They might determine that an agent needs more training on the more difficult products. Looking only at efficiency measures such as number of products sold would not provide an adequate analysis of what makes an agent perform in certain areas vs. other areas.

This information on individual agents is then aggregated along with measurements from other systems in the enterprise to give a holistic view of overall contact center performance within an organization. The contact center is then measured on how it meets company goals - not focusing just on "the numbers."

"I'm using reports today - how can analysis help me improve?"

Reporting tools generate static reports - once the report is generated, it is in jeopardy of becoming out of date if the data behind the scenes changes. In addition, the pre-configured nature of static reports built with a toolset hides anything but the most overt trends. If a business user wants to dig into the behind the scenes goings on, many important details are hidden.

With analytics, you are able to look at many factors at the same time (such as products, campaigns and individual agent performance) so you can count on the same truth in root metrics being available to everyone in the same manner. Analytics allows you to uncover where the organization is most effective and where it is not.

Accurate and reliable history
Most organizations that rely on reports find that they are constantly crosschecking historical information to make sure they are comparing apples to apples. Team 1 at one moment in time is not the same as Team 1 at another moment in time. A classic example of this is the supervisor who complains that his rating isn't right on the weekly report because Agent ABC no longer works for him.

With analytics, you should be able to track the movement of agents across the organizational hierarchy. In addition, an analytic application would support dynamic organizational structures so that when your organization reporting structure changes, the data follows the relevant managers to their new roles.

Analytic applications can help pinpoint a cause and effect relationship in near real-time as well. Managers can analyze campaigns mid-stream, in real-time, watching trends and making changes to the campaign quickly and easily. Say for example, a select team is making contact calls to cross-sell customers the opportunity to transfer credit card balances at a rate of 1.7% APR. Analytics provide the manager with a detailed analysis showing that the trend, just hours into the campaign, is that customers with balances less than $1,000 are far less likely to take advantage of the offer. Management can make a quick decision to reduce the rate to 0% APR because they can run "what if?" scenarios based on current and past trends.

Applying Analytics to Key Performance Indicators
While most reports are able to provide efficiency measurements such as average handling time, number of calls taken, dropped calls, etc., analytics provide the opportunity to truly understand contact center performance by taking efficiency measures and analyzing them against other key performance indicators to determine how effective the contact center really is.

For example, analytics gives you the ability to look at things such as:

• The relationship between key performance indicators in their contact centers - If I lower my Average Handling Time Target, will it adversely affect my Sales Conversion Rate?
  
• The outlying contributors to aggregate Call Center key performance indicators - My lunchtime calls handled is dragging down my SLA compliance - which agents are dragging me down?
  
• Call center performance over time - how effective is my continual improvement effort?
  
• Personal Performance vs. peers and peer groups - How am I stacking up against other agents?