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In an ideal scenario, past data would always serve as the basis for any business forecasts. However, it is possible to create valuable forecasting even without such data. There are many cases in which a call center might lack sufficient data to provide a reliable forecast. These three situations include forecasting without historical data, with little or insufficient information, and through a new service or channel. Let’s look at the high-level and low-level strategies, which are the two key aspects of forecasting with limited data.
The High-Level and the Low-Level Forecast
To develop a forecast with limited data, divide the forecast into two parts: the high-level forecast and the low-level forecast strategy.
High-Level Strategy
This is the top-level forecast, which includes an estimate of the number of contacts a call center is likely to make each month throughout the period of the next year. Call centers need at least three years of data to generate a good forecast and at least five years to make a solid forecast.
Low-Level Strategy
This predicts the required manpower by splitting down a high-level forecast into weekly, daily, or hourly (or 15-minute) intervals. This usually takes place over a shorter time frame.
Forecasting without Historical Data
When it comes to a start-up organization’s hotline, forecasting with limited data follows critical stages. Which are:
Option 1 – Using a Financial Plan for Forecasting
A financial plan can help determine the contact demand in the case of uncertainty. The likelihood of receiving a call depends on the potential sell-out of products. This selling potential can be estimated by looking at sales projections from the larger corporation. This figure offers the revenue-to-contact ratio to estimate how many product/service sales will likely equate to one contact. The standard call center guidelines in the same industry can help figure out this ratio. The projected sales for the year need to be added further to this ratio. This overall revenue forecast will help call centers increase their call volumes accordingly.
Option 2 – Comparison-Based Forecasting
Contact volumes fluctuate on a day-to-day basis. Therefore, considering seasonality is the next step to getting a sense of when monthly, daily, and hourly contacts will arrive. Different industries have different peak times. With the help of call centers, companies can leverage older call arrival records to estimate seasonality. Companies can fetch a data set from call centers to get a monthly breakdown of the contacts’ percentage. Multiplying estimated yearly contact numbers with monthly estimations will provide a high-level forecast of contact volume.
Conclusion
There are various reasons why the contact center may not have sufficient data to generate forecasts. The two common reasons are- poor report quality from computers and poor data preservation skills of outsourcers. Long-standing contact centers may, however, have varying degrees of “insufficient data.” Using a financial plan for forecasting is the ideal way to forecasting with limited data. Companies can seek professional support from seasoned call centers to track monthly seasonality and estimate annual contact volume.
