Forecast vs. Prediction in HR: What’s the Difference?

Setting goals and creating strategies to help you reach them is a natural path for any company. But what if you could forecast or predict the results you’ll get? Forecasting and prediction are tools that can help you understand where your business is headed, how likely you are to reach your goals, and what to do to get to your destination. 

What’s the difference between forecasting and prediction in HR? 

Forecasting and prediction are two terms you’ll often hear in HR, and many use them interchangeably. They refer to processes meant to help businesses understand their future workforce needs or mitigate risks. While they are similar terms, they are different concepts. Here’s how.

What is a forecast? 

A forecast is an estimation of future events or results based on historical data. Forecasts are objective and rely on data. They are based on statistical models and are helpful in estimating results across different sectors. Nobody can tell the future with 100% certainty, so errors are a possibility.

A forecast relies on calculation and estimation. That’s why it is best to have as much historical data as possible. The more information one can use in the calculation, the higher the chances of an accurate result.

In short, the forecasting process can be summarized as follows.

• Collect the data. To avoid errors, make sure your sources are trustworthy and always up-to-date.

• Analyze the data. See what happened in the past, what triggered certain events, and look for any patterns. Don’t forget to look at the most recent trends and the oldest ones to see the evolution.

• Draw conclusions and create the forecast. Based on your previous analysis and the trends you discovered, you should be able to estimate future events.

What is a prediction? 

A prediction estimates future events or results but uses subjective considerations like feedback or opinions. Others define a prediction as a statement of what someone thinks will happen in the future. Predictions can and often do use historical data, but they are based on intuition more than on an objective analysis of the data. 

For instance, if HR knows the last time they had a job opening, there were 100 applications they can predict that they will have another 100 applications if they were to open a similar position now. 

Their prediction isn’t based on any analysis and doesn’t consider the market or other factors that may have led 100 people to apply previously. Forecasting, on the other hand, would examine all factors, including application trends for other positions and changes over time. 

Examples of forecasting and prediction 

While similar in many ways, forecasting and prediction have varying levels of accuracy and different applications. Here are some examples of their use in the HR sector.

Forecasting in HR 

Forecasting uses data to identify patterns that lead to a certain outcome. This knowledge can help professionals understand how likely certain events are to happen again in the future.

Forecasting in HR can be applied as follows:

• Employee turnover forecasting. Analyzing past data and market trends can help HR predict future employee turnover. The patterns and trends in the data can show them why people choose to quit and the likelihood of that happening in the future. This, in turn, can help them create stronger retention strategies and succession plans, to minimize the impact of attrition on the company.

• Diversity forecasting. “How likely are our DE&I efforts to succeed?”, is a question most companies will ask themselves at one point or another. Through forecasting techniques, HR professionals can look at trends in the workforce composition and forecast its evolution over the coming months and years. This helps businesses understand if they’re on the right track, or if they need to make adjustments to reach their DE&I goals.

• Succession planning. A succession planning program is the best way to minimize disruptions when employees leave. The focus is on leadership and critical positions where the departure of one person could impact the entire company. Through forecasting, HR can get a good idea of the future talent needs, identify key positions, and suitable candidates to fill them.

Prediction in HR 

Unlike forecasts, predictions don’t always use quantitative or statistical data. Although more intuitive, they can still be useful in predicting possibilities in different sectors.

Predictions in HR can be applied as follows:

• Employee engagement prediction. When you invest in engagement strategies, you want to get an idea of how likely they are to work. But engagement as a metric is not purely quantitative. It includes feedback and surveys, all of which carry a degree of subjectivity. By using this data, HR can predict engagement levels and react accordingly.

• Cultural fit prediction. Making sure candidates fit into the company’s culture is a challenging, yet necessary part of talent acquisition. Unfortunately, no data can give you a precise forecast of the likelihood of cultural fit for a person. HR can look at past data regarding this issue while also discussing it with the candidates themselves. They can then predict how likely a person is to align with the company’s values.

• Employee performance prediction. An employee's performance depends on various factors, including their skills, their motivation, and more. Some factors are subjective, such as their health, so predictions can’t be 100% accurate. But HR can get a good idea of how well someone will perform by looking at past data on their performance, engagement levels, and future development plans.

HR forecasting techniques 

HR forecasting is useful in understanding future workforce demands, flight risk, and more. There are several techniques that can help you on this journey.

1. The Delphi technique 

The Delphi technique is based on the idea that forecasts or decisions made by a larger group of individuals will always be better than those made by a single person.

To use this technique, several people, with varying degrees of expertise, will be chosen to make a forecast regarding a certain issue. They will all receive the same data, but they will not work together as a group. Instead, they will be anonymous and have no face-to-face interactions.

An intermediary will assess their individual forecasts, draw conclusions, and provide individual feedback. The process can be repeated several times until those involved reach a common decision.

The Delphi technique eliminates the risk of biases and people influencing each other’s decisions. For instance, people might be more likely to agree with the most senior member of the group if they were going through this process face-to-face. Since they are unaware of each other's positions, they can speak openly and come to impartial conclusions.

2. Trend analysis 

Trend analysis is one of the most common forecasting techniques. It involves looking at historical data and trying to identify patterns and trends. Once HR finds these patterns, the next step is to forecast how likely they are to happen again.

For instance, you might look at data trends and find that productivity increased significantly during certain times each year. Upon a deeper analysis, you may find the time coincides with a merit increase or simply with a new project. With that in mind, you can forecast when those same conditions will be met so that productivity can increase again.

3. Scatter plot 

A scatter plot is a type of graph that shows the relationship between two variables. By looking at two sets of data, you can get a more accurate forecast and partly eliminate the subjectivity HR forecasting often comes with.

For example, when analyzing turnover risk, looking strictly at data related to attrition will limit your chances of an accurate forecast. But if you use a scatter plot, you can select another variable, such as the employee’s tenure. The scatter plot will show you the relationship between the two variables. 

Perhaps you’ll notice a critical moment for attrition is when employees hit the 3-year mark with the company. You can forecast that employees who worked for the company for almost 3 years are at the highest risk of quitting, so focus on keeping them with retention strategies. 

Finding the right tools for predictive HR analytics 

Despite their name, predictive HR analytics uses a mix of forecasts and predictions. You will almost always use historical data in your decision-making process, but there will also be a level of “intuition” involved. 

To maximize your predictive HR analytics journey, you need the correct information about your workforce. That’s why people analytics should always be your first step. Get a complete view of your workforce data and what influences their motivation and performance. 

Workforce planning will then guide you in creating realistic and adaptable strategies based on data and forecasts that will get you closer to reaching your business goals. Create strategies for retention, talent acquisition, and learning and development that help people perform their best, minimize skill gaps, and improve productivity.

On the Outsmart blog, we write about workforce-related topics like what makes a good manager, how to reduce employee turnover, and reskilling employees. We also report on trending topics like ESG and EU CSRD requirements and preparing for a recession, and advise on HR best practices like how to create a strategic compensation strategy, metrics every CHRO should track, and connecting people data to business data. But if you really want to know the bread and butter of Visier, read our post about the benefits of people analytics.