Before we begin, it’s important to keep in mind that HR forecasting is a complex process that varies depending on several factors. So, it’s best to treat these steps as guidelines and to adjust your forecasting strategy according to your organization’s specific nuances.
Now, let’s get to it.
Determine company goals and impact
The purpose of human resource forecasts is to help fulfill your organization’s talent supply. And so it is logical to start your forecasting process by first understanding your company’s short and long-term needs.
The simplest example of a forecast based on company goals is to consider revenue needs. Let’s say your organization relies on sales development representatives (SDRs) to bring in revenue, and that your yearly revenue should increase by X amount.
Now, you can determine how much each SDR brings in on average, which will help you determine how many representatives you’ll need to increase your revenue by X amount. If there is a discrepancy, you’ll have identified a potential hiring need.
Of course, this example is not practical because we’ve ignored several influencing factors and considerations. The purpose of sharing this instance is simply to communicate how company goals can drive hiring needs.
You can extend the same principles to a need to hire developers to meet product needs, or to recruit more PR professionals to expand your outreach. Determine the impact that each professional can potentially make and how far they’ll help you in reaching key objectives.
History is a great teacher
Provided your company isn’t new, your hiring history and previous human resource needs can help you forecast demand or at least give you an indication. So in your analysis, don’t overlook historical data.
However, a word of caution – if your organization’s needs have evolved rapidly, such as due to expansion, make sure you scale the insights accordingly. Previous hiring data won’t always translate linearly into future demand needs.
Collect and monitor both internal and external data
Restricting your HR analytics and forecasting to internal data can significantly harm the accuracy of your predictions. This is because, as we discussed earlier, several external factors can also influence your human resource needs.
So while collecting data for your forecast, don’t forget to include labor market insights, policy changes, and even demographic information.
Account for employee turnover
No organization has perfected employee retention, although some are better at it than others. When forecasting your company’s human resource demand, it’s important to account for turnover and attrition – you’ll need to fill the now open positions.
In general, it’s best to leverage historical data to determine past turnover, helping you make more accurate predictions. You should also pay attention to churn by department. Is Sales losing more people annually than, say, Products?
Engage your people
Human resource is about your…human resources. And yes, that’s painfully vague, but I’ll elaborate.
Hiring managers are on the ground. They’re actively engaged with departments and they can anticipate hiring needs. So when preparing your forecasts, engage hiring managers and ask them about their needs and future predictions.
You don’t need to stop engaging hiring managers, either. You can potentially increase the HR forecast’s accuracy by surveying employees to better predict turnover.
Choose your forecasting model
There are various models for HR forecasting, including ones that use expert opinions and others that use statistical modeling. We will not cover these models in this article, as that would invite a tangent.
However, we advise you to explore different methodologies, such as the Delphi model, regression analytics, supply forecasting, and ratio analysis. Then, apply whichever forecasting model best suits your organization’s human resource structure.
Sometimes, applying a combination of different forecasting methods can yield more accurate results.
Monitor and optimize
HR forecasting is not an exact science. The accuracy of your forecast largely depends on the reliability and quality of the input data, and on your selection of an appropriate model(s).
So after completing your forecast, monitor its accuracy by observing how well it stands the test of time. Do your predictions prepare you to anticipate and get ahead of human resource demand?