Historically, staffing a contact center has been a tricky proposition. More often than not, the outsourcing supplier overstaffs or understaffs the floor, since it is unable to predict incoming call and non-voice volumes accurately on a day-to-day basis. Yet proper staffing is crucial to building a successful operation that adheres to service level agreements and enhances the customer experience.
Most companies can predict call volumes monthly, but are hamstrung when it comes to daily visibility. In addition, most companies only use incoming volumes as an input to their forecasting techniques. However, I think that the call center must take into consideration extraneous factors that can radically affect call volume. For example, marketing efforts and special promotions that could cause a spike in call volume; or consider a large swell of calls that typically occur after a national holiday or when the system itself was down. All of these issues can create volatility in daily volumes.
The contact center must be prepared for the extra volumes if it is to serve the outsourcing buyer's customers properly. But, how do you predict accurate, granular volumes in order to staff properly?
Analytics and forecasting is the answer. To forecast incoming volumes, I think the past is a good indicator of the future. Time series regression models can be used for this analysis as a key step. The second part of the model accounts for the extraneous factors which act as a multiplier effect.
Forecasting has a number of applications. Today, every company relies on efficient and accurate forecasting. They need good visibility into the future. A good forecast is the key to efficient and profitable customer servicing. For example, it can provide insights for manufacturers. If a manufacturer can't predict the demand for a product, it may ship too many, which then sit in a warehouse, or too few, which means lost sales. Either way, this impacts the manufacturer's profitability. Forecasting and analytics can help predict volumes by category and geography so that manufacturers know when and how much to ship.
The granular information provided by forecasting creates tighter control because you gain better visibility and lower variances. Of course, this typically increases customer satisfaction as well.