Call centers have to constantly work to improve their key performance metrics. From average handle time, to call resolutions, to customer experience, call center managers have an enormous challenge before them to make continuous improvements.
When preparing to make improvements, call center managers need reliable information. Without the right information, a call centers management staff might as well try to work blindfolded.
This is where big data and predictive analytics come into play. Automated analytics software is necessary for the call center industry to analyze the billions of hours of audio files collectively generated each year.
Whats the significance of analyzing these massive amounts of data?
1: Assessing Overall Contact Center Performance
Theres an old saying that goes: those who fail to study the past are doomed to repeat it. This saying is particularly poignant for businesses, as analyses of past data allow companies to benchmark their progress.
Big data for overall performance metrics helps call centers measure their past and current performance so that reasonable and achievable performance goals can be set. Without a complete set of data, its impossible to establish which performance metrics are most in need of improvement.
2: Creating a Strategy to Achieve Increased Performance
Once you know what performance metrics are most in need of improvement, you can create a strategy for making those improvements.
However, creating such a strategy based on big data can be a daunting task. By definition, big data involves massive data sets that are difficult to manually process.
To help process the information in a large data set, such as the countless hours of recorded calls your contact center has, analytics programs are a must. These programs can help you parse an enormous data volume down into statistics that you can use to identify not just what metrics are most in need of improvement, but gain some insight into why those metrics are lagging.
For example, you could have a comprehensive list of call records and statistics from over the past year. With this information, you could easily see what your average handle time and positive call resolution rate is, but without some kind of deeper analysis, you wont know why some calls end in success while others end in failure.
Predictive Voice analytics programs can analyze a customer call automatically, studying the way that people speak to provide an assessment of their emotional behavior and tone. This can help you establish the emotional reasons behind specific call results, such as a phone agent using a condescending tone resulting in a dissatisfactory call result, or an agent demonstrating empathy and understanding of a customers situation leading to a more positive result.
Using this information, you can then create a strategy for improving future call results, such as by training underperforming agents in interpersonal skills so that customers dont find them abrasive to talk to.
3: For Projecting Future Call Results
One of the neat things about having a huge data set and getting it thoroughly analyzed is that you can start to spot trends in that data. With the right analytics program, this data can then be used to create a predictive model for future call results.
For example, predictive voice analytics software can examine a customers tone, pitch, and other key voice features to assess their emotional behavior and state of mind. This information can then be used to take an ambiguous response and evaluate the likelihood of a favorable outcome on a future call.
Applied to all of your calls, this predictive assessment can be used to rank customers by likelihood of a favorable response in the future. This, in turn, allows your call center to focus its efforts on the calls that will help move your business forward and minimize time wasted on dead-end contacts.
Without a large pool of data to draw on, and no analysis of that data to call out relevant information to you, such efficiency-enhancing projections of future call results cannot be made.
Big data is an important resource for call centers, but having the right tools to analyze that data is just as important.