Call Center Analytics and Metrics
“Analytics” is one of those terms that many business people use, even while they may disagree on its exact definition. In fact, one Gartner survey asked users what it means and they received a range of answers in response, from “online analytical processing” to monitoring call centers, to data mining.
For the purposes of this paper, we will define analytics this way: It is the discovery and communication of meaningful patterns in data. It also refers to the drawing of conclusions from the examination of that data. The objective of analytics is to describe business performance, and both predict and improve future performance.
Even amidst any lingering uncertainty over what is precisely meant by the term, it is indisputable that data analytics now plays a significant role in a wide range of industries, and call centers are certainly no exception. Every call center manager has a wealth of raw data at his or her disposal that can be used to identify patterns and make better business decisions.
Analytics can take many forms. The three that are most prominent for contact centers are Performance Analytics, Speech Analytics and Desktop Analytics.
Performance analytics focuses on call center personnel. How well agents perform their jobs, and how well they adhere to company policies and procedures has a direct impact on KPIs and other critical metrics. Managers should always be seeking out ideas to turn average agents into leaders, and great agents into superstars.
A performance analytics solution works by delivering insight into every encounter between an agent and a customer, through recorded phone calls and captured agent activity across all platforms. With this data, and the monitoring of KPIs from adherence and average handle time to labor costs and shrinkage, managers can ascertain whether personnel, processes and systems are aligning to meet the call center’s performance goals.
Since staffing accounts for as much as 70% of most call center budgets, performance analytics provide actionable intelligence that can impact forecasting and scheduling. And by providing this information in real-time, call centers can become proactive, rather than reactive, to help managers make better, faster decisions.
Speech analytics, like performance analytics, offers insight into employee performance. But it also provides insight into a call center’s customers, which can lead to changes in policy and staffing to serve them better.
Sometimes called audio mining, speech analytics refers to the analysis of information from recorded customer phone calls. It is a more in-depth procedure that merely reviewing calls and categorizing them by outcome, as it also provides insight into why customers are calling, and how their needs can be better serviced.
The focus is on capturing the “voice of the customer” – demeanor, personality, words/phrases used most often – to extract recurring themes. Calls are then categorized accordingly, and analyzed for any recurring patterns. These patterns can help to identify the root causes of why some KPIs are not moving in the right direction.
For instance, if first call resolution is a problem, speech analytics may uncover the motivation for why some customers are compelled to call back – if the same issue is happening with numerous callers, there may be a way to address it before it becomes a issue, which should lower similar occurrences going forward.
In addition, speech analytics can help boost sales (by identifying which approaches are having the greatest success) and boosting customer retention.
Large call centers have employed these techniques for years, but the practice is now becoming more commonplace at smaller and midsized centers, a result of improved technology and lower costs for top-tier quality monitoring software. Now, the kind of advanced marketing intelligence that used to cost hundreds of thousands of dollars is not only more accessible and affordable through speech analytics in the cloud, it is also easier to set up and use.
As technology has become more sophisticated, agents now work with a number of desktop applications designed to streamline daily activities, improve job performance and boost customer service. But expecting every agent to prosper equally in this scenario is like handing tennis racquets to Serena Williams and Stephanie from accounting, and expecting both to hit a 100 mph serve.
How well does each agent navigate the functionality of the call center’s software? How quickly do they type? Are they taking advantage of built-in shortcuts or getting to the same place through more roundabout means? More importantly, how are these delays affecting average handle time and other metrics?
Desktop analytics helps to pinpoint any knowledge or performance gaps, which can be corrected through additional agent training. At the same time, it can also identify any deficiencies in call center software that may be contributing to the problem. Error messages and slow page rendering can be just as detrimental to average handle time as a poorly training agent. These types of problems may go undetected for weeks or months, unless an agent describes the situation to his or her superiors. A few extra seconds’ delay caused by a technical issue, multiplied by the number of times that occurs per shift, per agent, per day, adds up to a serious problem.
With desktop analytics, seemingly minor but relevant problems are identified, as every event that occurs on the desktop is captured. A little fine-tuning can yield dramatic results.
Choosing an Analytics Provider
As there is some crossover in the data that is collected by performance analytics, speech analytics and desktop analytics, it makes sense to incorporate all of these functions into one workforce optimization (WFO) solution, combined with workforce management and quality monitoring. That also insures that the analytics data is highly actionable. For example, you should be able to change forecasts, schedules and other parameters based on analytic insights you get. Check out these videos to see call center metrics in action.
To avoid the substantial upfront investment that would typically be required to add these capabilities, contact centers should consider a cloud-based service. This also eliminates the need for a complicated and time-consuming hardware and software implementation. With a cloud-based WFO platform, a contact center can begin to benefit from analytics in as little as 30 days.
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