What is the Difference Between Interpolation and Extpolation in Recruitment Forecasting?

Interpolation and extrapolation are mathematical techniques used to predict unknown values, but in recruitment, interpolation is generally more reliable for internal workforce planning, while extrapolation helps forecast external hiring trends. Understanding when to apply each method can significantly improve the accuracy of your talent acquisition predictions, from filling skill gaps to anticipating future hiring needs.

What are Interpolation and Extrapolation in a Recruitment Context?

In recruitment analytics, interpolation and extrapolation are predictive methods. Interpolation involves estimating values within a known range of data. For example, predicting the likely salary for a mid-level role when you have data points for entry-level and senior positions uses interpolation. Extrapolation, conversely, estimates values outside the existing data range, such as forecasting next year's hiring volume based on the past three years' data. The core difference lies in the position of the predicted value relative to your existing data set. Based on our assessment experience, interpolation typically yields more reliable results in recruitment because it works within established parameters.

How Can You Use Interpolation for Internal Recruitment?

Interpolation is highly effective for internal workforce planning and talent mapping. It helps HR professionals make informed decisions with existing data. Common applications include:

• Salary Banding: If you know the salaries for a Grade 5 ($50,000) and a Grade 7 ($70,000) position, you can interpolate to determine a fair market rate for a Grade 6 role, ensuring internal equity.
• Skill Gap Analysis: By assessing the skills of junior and senior employees, you can interpolate the specific training or competencies needed for a mid-level employee to progress, creating a targeted development plan.
• Performance Prediction: Estimating the potential performance of an internal candidate for a promotion based on their current role and the documented performance of others who have been promoted.

This method reduces risk by relying on known, internal data points to make predictions about gaps within your current talent pool.

When Should You Apply Extrapolation for Talent Forecasting?

Extrapolation is best used for strategic, long-range talent forecasting where you must look beyond current data trends. This method is inherently riskier but essential for planning. Key recruitment applications are:

• Hiring Volume Projections: Analyzing hiring data from the last few years to predict how many new hires will be needed in the coming year or quarter.
• Attrition Rate Forecasting: Using historical turnover rates to estimate future attrition, allowing proactive recruitment to build a talent pipeline.
• Market Trend Analysis: Predicting future demand for specific skills in the job market based on emerging industry trends and past growth data.

It's crucial to use extrapolation cautiously. A sharp, unexpected change in the market can render extrapolated forecasts inaccurate. For instance, an economic downturn could drastically alter hiring projections based on years of growth.

What are the Practical Steps for Applying These Methods?

Integrating these techniques into your recruitment process requires a structured approach. Here is a comparison of the key considerations:

To implement these methods effectively, start by ensuring your historical data on roles, salaries, and turnover is clean and organized. Use simple linear models initially before moving to more complex ones.

To effectively leverage predictive techniques in recruitment: prioritize interpolation for reliable internal decisions like promotions and salary benchmarking, and use extrapolation cautiously for long-term strategic planning while acknowledging its inherent risks. The key to success lies in using high-quality, relevant data and continuously validating your predictions against actual outcomes to refine your models over time.