TL;DR:

• Precise data analytics is crucial in HR management.

• Calculating attrition helps identify trends and improve employee satisfaction.

• Weighted average headcount provides a more accurate calculation.

• The weighted average considers fluctuations throughout the year.

• Our people analytics tool offers advanced solutions for transformative insights.

In the dynamic HR management landscape, precise data analytics is crucial. Our expert data team and engineers step in, revolutionizing attrition calculation with advanced technology and methodologies.

Understanding the Impact of Employee Turnover on People Analytics

Employee turnover is a crucial metric for organizations. It refers to the rate at which employees leave a company. Calculating attrition helps businesses identify trends, assess retention strategies, and make informed decisions to improve employee satisfaction.

The calculation for attrition is:

(total terminations) / (average headcount over a time period)

Average headcount is typically calculated as:

The average number of employees at the start and end of the period.

For example, if a company had 100 employees at the end of the year (Dec 31st, 2022) and 160 employees as of June 30th, 2023, the average number of employees would be (100 + 160) / 2 = 130.

However, this does not account for fluctuations throughout the year. It averages the bookends but not the in-between months.

The Significance of Weighted Averages in Enhancing Accuracy in People Analytics

Changing from normal average to weighted average headcount moves us from calculating the bookends of a period to the individual months within the period.

It's more precise, accounting for employee tenures during the given period. This method reflects workforce dynamics more accurately.

Illustrating with an example

Consider our company: 100 employees at the start of 2023, and 150 as of June 30th, 2023. The normal average headcount is 130 by summing the bookends and dividing by 2. However, let's say within that time frame, we increased our headcount by 10 each month:

Dec '22: 100, Jan '23: 110, Feb '23 120, Mar '23 130, Apr '23 140, May '23 150, June '23 160

The weighted average headcount considers each month's fluctuations and sums the total headcount of every month, then divides it by the number of periods, as follows:

100 + 110 + 120 + 130 + 140 + 150 + 160 = 910

910 / 7 = 130

This gets us to the same average headcount, right?

But now, since we broke down the calculation by month, we can apply weights to the bookends of the period.

Applying the Weights

A period's start and end points are less critical than the internal months. Therefore, we assign weights of 0.5 to the edge points, while internal periods receive a weight of 1. This weighting system allows for a more balanced and realistic calculation.

We now recalculate:

(100 x 0.5) + (110 x 1) + (120 x 1) + (130 x 1) + (140 x 1) + (150 x 1) + (160 x 0.5) = 780

780 / 7 = 111780 divided by 7 equals 111

Weighted average headcount = 111

Normal average headcount = 130

Impact of Weighted Average Headcount

The attrition rate will vary depending on the company headcount on the bookends. However, there is a clear difference: the idea that weighted average headcount better accounts for fluctuations in the period.

If we had 20 terminations during the period, the difference in attrition would be:

Using weighted average headcount = 20 / 111 = 18%

Normal average headcount = 20 / 130 = 15.5%

Implementing "Small" Code Changes to Facilitate the Shift to a Weighted Attrition Calculation Mode

Three functional changes were made to our code to implement the above:

1. Adjust calendars only for average headcount to include the headcount as of 12/31/2022 when filtering from 1/1/2023 to 6/30/2023. Can think of this as being a 7-month period instead of 6 months.

   • Adjusted calendar would not be applied to total terminations, so only the headcount as of 12/31/2022 is considered.

   • Requires having two calendars, one for the numerator and one for the denominator.

2. Change the average headcount to use each date between the start and end of a period and apply the weights.

3. Use our custom semantic model to create CTEs, enabling us to implement the above requirements.

How the Shift to a Weighted Attrition Calculation Mode Benefits You

Our people analytics tool is a leap into the future of enterprise people analytics. By leveraging advanced technology and innovative methodologies, we provide superior insights. Our people analytics tool is designed to enhance accuracy and provide valuable insights into workforce trends and patterns. Experience the difference with our advanced solutions – your gateway to transformative insights.

Get in touch to see how we can innovate and change people analytics for your company.