We’ve got it pretty good nowadays when it comes to customer service, particularly with the heightened competition—especially among consumer-facing companies, which try to claim the “best in customer satisfaction” crown. Much of this “arms race for customer service differentiation” is driven by access-from-anywhere devices and networks and made possible by technologies for tracking and improving customer engagement metrics.
The result: Just think about how much your customer service over the phone has improved in the last decade. The Benchmark Portal, a leading benchmarking firm that focuses on contact center operations metrics, found that the average contact center wait time in 2015 for a sample industry was down to 33, 30, and 29 seconds in the Americas, EMEA, and the Asia Pacific region, respectively.
Companies increasingly have to go above and beyond when it comes to customer service to stay ahead—and that extends to their call centers. Customers who have been kept waiting too long are unhappy customers, less likely to come back and more likely to shop around for alternatives. As the Benchmark Portal points out, call center operations are “one of the enterprise’s keys to the customer experience, satisfaction, and, ultimately, loyalty.” To avoid losing customers, a company needs to ensure its call center is appropriately staffed to meet demand—and it needs the proper tool to do so.
We recently launched the Anaplan Call Center Planning app to address this very issue. The modeling power of the Anaplan platform is leveraged to build optimization capabilities for large call center workforces. App features include:
- Planning staffing needs based on customized SLA assumptions for increased accuracy
- Generating accurate call center statistics, including SLA thresholds, arrival rates, answer times, and many others
- Importing call center data directly into the app to instantly generate a call center forecast
- Managing call center forecast using various statistical methods
The app uses the industry standard Erlang-B and Erlang-C traffic models to determine how many agents to staff at each call center with different call volumes, what the average wait time for a caller will be, and what the probability is that a caller will have to wait to get serviced.
Imagine using this functionality in correlation with other sources of information, such as customer satisfaction metrics, to more accurately predict a company’s revenue forecasts. Now that is powerful.