For instance, service level and average speed of answer are maintained because we believe long wait times lead to customer dissatisfaction. Abandons are a great proxy for customer satisfaction, too, because a customer who hangs up is almost always not happy with their wait time. And agent quality scores are maintained as the mechanism to ensure a consistently excellent interaction with customers.
Different flavors of experience metrics have similarities
Customer satisfaction, first call resolution, Net Promoter Score, agent quality score, and others count among many customer experience metrics. Internally, companies focus on experience scores that can vary from other business units that focus on customer scoring. But even if the scores are called the same thing, they are almost always calculated using different algorithms.
This type of scoring makes perfect sense as different customers – calling the same company – are contacting the contact center for different purposes. The experience must therefore be attuned to the purpose of the contact, although it doesn’t mean that different ways of measuring a customer’s experience don’t share similarities. An example is that customer experience metrics have seasonality much like most other contact center metrics. As customers call for different purposes at different times of the year, their patience and expectations are likely to change. In the same way, an agent can be more or less motivated seasonally, and will score differently week over week.
Experience metrics are also differentiated by contact type, location, or staff group. A sales-oriented group and a service-oriented group will score differently, for example, even if they’re taking the same type of call. Geographical centers, likewise, often score differently because they have different management. Ultimately, though, experience scores exhibit trends. As a workgroup improves or declines, or as company performance changes, focused training can positively affect the trajectory of an experience score.
How can planners use customer experience metrics?
Similar to contact volumes, handle times, attrition, and shrinkage – time-series data that planning analysts typically work with – customer experience scores exhibit seasonality, trends, and differences across contact centers. When center and staff group forecast customer experience scores, planners can use these new forecasts in a host of ways. First, they can draw out the week-over-week customer experience trends to view where the business is heading. By applying a forecasting technique (like Holt-Winters) to a customer experience metric, planners can measure where their math expects customer experience to be weeks and months into the future.
Importantly, these forecasts act to set executive-level expectations. Planners can watch to see that actual expectations are met, and if expectations are trending in the wrong direction, it shows that the specified path should be adjusted. In effect, this time-series experience data acts as an early warning device.
Another great use of a customer experience forecast is as a point of comparison. The best companies view all of their forecasts (volumes, handle times, attrition, shrink, and so on) as a baseline for variance analyses. As weekly performance data is tallied, it can be compared to the forecast. Any differences between forecasted and actual performance implies that something has changed. If forecasting and tracking customer experience scores, any deviation must be noted, explained, and potentially acted upon. For this sort of analyses to have any meaning, it must be compared to seasonally adjusted customer experience forecasts.
Plan better by using customer service forecasts
If improving customer satisfaction is important to your company, then it makes perfect sense to include customer experience forecasts in your staff planning process and decision making. It’s simple. If you are using a hiring and extra time optimizer for long-term planning, you can instruct it to hire in such a fashion as to maximize customer experience scores. If you are developing hiring plans by hand, it should be straightforward to move new classes toward those centers with the better scores.
By developing customer experience time-series data, using this data to forecast expected performance, and applying this forecast to variance analyses and staff planning, you can greatly improve your customer’s experience.
Download the complete whitepaper to learn more: Customer Experience, Trends, and Staff Planning www.inin.com/whitepapers