Decision makers in any sales organization know that forecasting correctly is both art and science. To predict the unforeseen successfully, you must use every piece of logic and reasoning available to help you make your predictions reality.
When developing a strategic forecasting approach, your business needs to take a number of items into consideration simultaneously — and that is no easy feat. Call centers, for example, must have a thorough understanding of seasonality and industry market trends to identify anomalies and increase accuracy.
Precise forecasting and scheduling depend on historical data, potential future fluctuations and current market conditions.
Enhancing and refining your forecasts with the help of new technology and techniques is key to minimizing costs and driving more revenue into your organization.
How to Approach Forecasting
Companies are always looking for ways to be more efficient. You want to provide excellent service to your customers, of course, while still cutting down on your operational expenses. Every call center must figure out the right balance: Overstaffing increases overhead, while understaffing leads to missed revenue or a bad customer experience.
At many companies, revenue and call volume forecasting are done in isolation rather than collaboratively. This strategy, unsurprisingly, fails to deliver the expected results when business development or marketing teams are predicting new growth and revenue opportunities, but operations teams are forecasting on historicals.
When forecasts are separated and siloed, the call center often is left playing catch-up — and with competitive labor markets, companies can experience significant opportunity costs.
A better approach is to ensure that company forecasts are made in collaboration. Decision makers from different areas of your organization — from marketing to account management — should come together to be part of the dialogue of forecasting together.
This way, everyone is on the same page and knows where the company is headed. Developing interdepartmental feedback loops and getting cross-functional team members to create collaborative forecasts will improve accuracy and also create a sense of interdependence.
Looking at historical data to determine seasonality is important, but historical data is not enough to develop a robust forecasting strategy in today’s environment. Markets and products are evolving rapidly to keep up with technology, and forecasting today requires analyzing innovation trends. Flexibility is crucial.
Accurate forecasting also has serious implications for the employees handling calls. Underutilized call centers, especially sales centers with commission structures, experience low employee morale and engagement in periods of overstaffing. Agent availability, flexibility, talent and training also need to be taken into consideration when developing your forecasting strategy to ensure that volume volatility doesn’t diminish employee engagement.
Automation Tools in Forecasting
Forecasting always has relied on data. In the past, the majority of companies used manual methods of forecasting (and quite a few still do today). With technology evolving, however, data science teams can analyze different scenarios constantly. These teams can run multiple forecasts at the same time in order to determine which one will produce the most accurate results. Data scientists also have built the framework for machine learning forecasts (or automation tools).
Data science teams provide access to information, reports and data tables that you can use to aid decision-making. When you have access to robust information daily, you can analyze the data in different ways, from different angles, and at different times.
This approach makes it possible to incorporate data outside of call volume, rather than relying on just a few pieces of information and then jumping to (often inaccurate) conclusions. This is especially important in a sales environment with many variables that affect outcomes.
It can be a struggle for any organization to forecast call volume accurately. Some companies may not have the ability (or desire) to analyze and manage all their data in-house in an efficient way. Instead, they may choose to outsource their data science needs, and have automation tools created that can help enhance and fine-tune their forecasting strategies.
Machine learning forecasts used in sync with scheduling platforms can determine when staffing issues might arise. If you know that you are going to be understaffed, the system can push out overtime to agents automatically.
On the other hand, if calls are not coming in as anticipated, the system can offer voluntary time-off as needed. This approach effectively automates processes, instead of requiring manual input from a workforce management team. Automatically predicting call flow and adjusting as needed, without involving a live person, cuts operating expenses and results in better outcomes.
Automation tools can help when you know agents will not be in due to illness, vacation or other leave. An ML-based system can look for suitable alternatives automatically, or throttle call volume based on current staffing availability. It can connect callers with the specific agents best equipped to handle particular situations as well.
Machine learning forecasts constantly are reiterated based on data. Technology has advanced to the point that it’s possible to have forecasts running independently and correcting themselves automatically. This development means managers no longer have to rely solely on algorithms and formulas when developing their forecasts. A call center that utilizes data science appropriately is better equipped to make well-informed decisions.
Enhancing Your Forecasting Approach
Forecasting for your company does not mean creating one general overall plan. Instead, strategic plans are broken down into short-, mid- and long-term forecasts that evolve continuously over time. Unforeseeable situations can develop very rapidly, so the best scheduling plans leave room for flexibility.
Smart forecasters always assume forecast variances are inevitable. You have to create levers to pull when forecasts do not come in as expected, because very rarely does everything happen exactly how you think it’s going to happen. Examples of levers can include overtime for agents when call volumes are higher than normal, reprioritizing calls so that the most cost-effective calls are at the top of the queue, or changing the call to action on a site to increase chat, email or lead forms, and decrease calls.
The ability to control call volume and quality through ML is unique. Automation tools consider how many calls are anticipated, how many agents are available, and how many calls are actually in the queue. These tools then are able to make decisions based on that information — for example, turning off the least-profitable paid search campaign to maximize opportunity, while eliminating sunk marketing spend.
Another way to become more flexible is through alternative calls to action, which increase the ways for customers to make contact if their calls can’t be handled quickly. One idea being considered is showing the customer a chat option instead of a phone number when agents are busy. This way they can still get the assistance they need without the frustration of sitting in a queue for a long time. Through automation, you can customize machine learning to enhance your customers’ experiences by offering more consumer choice.
Automation tools help balance ever-changing priorities. Automation can help conserve resources when you unexpectedly are understaffed, and help prioritize the calls that have the greatest potential to convert. That can improve your bottom line. The customer experience also improves when you are able to move customers through the queue more effectively.
Your clients also can save money by not paying for inefficient campaigns. Even though there are always ways to improve, data automation can make your forecasting more accurate than ever.
The Future of Forecasting
It is impossible to forecast the future based only on what has happened in the past. You also need to forecast based on the potential of what could have happened, and data science and machine learning (automation tools) can help with that.
You will be better positioned to serve your customers and increase your partners’ profitability if you utilize available technical tools to their full capacities, connecting all your systems, data and teams.
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