In any contact center, forecasting stands as the cornerstone of operational efficiency and precise reporting. An effective forecast touches every facet of the contact center's function, guiding budgeting, staffing, and customer experience strategies.
A critical yet often neglected step in developing a robust forecasting model is the analysis of all relevant data. Even the best artificial intelligence model can have trouble determining why that person did or did not pick up the phone, so it is essential to align past data with future expectations and cleanse any anomalies. Here are a few ways to do this:
- Compare actual call volume against forecasts. Large deviations (more than 15 percent) warrant investigation.
- Identify and exclude anomalies and unexpected events, such as unplanned promotions or system outages, from forecasts.
- Segregate predictable events (e.g., Black Friday, bill issue dates) in a separate forecast.
- Ignoring anomalies can lead to significant inaccuracies (between 20 percent and 40 percent) in forecasts. Recognizing and adjusting for these discrepancies is crucial.
Enhancing forecasting models is a key role of cross-departmental communication. Regular dialogues with marketing, sales, operations, and others provide invaluable insights into upcoming initiatives that could impact call volume. No one wants to scramble to handle an unexpected jump in call volume, only learning after the fact about a marketing promotion. Instead, collaboration enables departments and forecasters to anticipate and prepare for shifts in customer contact patterns, fostering a more robust and adaptive forecasting process.
Reviewing historical data from similar events helps teams prepare and learn. Taking stock of history, collaborating, and effectively communicating about potential stressors will ultimately bolster customer experience.
The selection of historical data is pivotal to effective workforce management and forecasting. There is no one-size-fits-all approach; relevant data could range from a week to years, depending on the organization's context. Data following significant changes (like new routing strategies) might be more relevant than older information. The key is selecting data periods that best predict future conditions. When forecasting for September, look at data from previous Septembers. Or, if the forecast is for a specific day, look for previous days where volume would be similar.
The data's relevance depends on the organization's specific context. Significant changes in operational elements, such as call routing, could render older data less relevant. Thus, the careful selection of data that closely aligns with expected conditions becomes critical to forecasting accuracy. This process often balances statistical methods with intuitive judgment.
The length of a forecast period also exerts an influence on the choice of data. Longer-term forecasts necessitate a more extensive data set to account for evolving trends and seasonality. Conversely, shorter-term forecasts, such as those for specific weeks or days, demand a more detailed and relevant historical data set to ensure precision.
Choosing the appropriate software can also impact forecasting accuracy. Ideal software should do the following:
- Allow selection of various date ranges.
- Present data in an easily adjustable format.
- Provide accurate staffing estimates based on forecasts.
- Offer robust reporting tools.
For most contact centers, the main goal is to balance customer service and service level targets while minimizing staffing costs, which typically make up between 70 percent and 80 percent of the budget. Accurate forecasting can prove essential to efficient scheduling and help avoid both understaffing and overstaffing.
Advanced workforce management (WFM) tools enhance forecasting methods. They help prepare budgets, meet service levels, and ensure effective staffing. Such software not only improves company reputation and customer experiences but also helps in learning from past events and refining predictions.
To sum up, while achieving accurate forecasts requires commitment, leveraging advanced WFM tools and strategies can significantly enhance the forecasting process.
David Hoekstra is product evangelist at Calabrio.