Retaining talented employees is fundamental to building a thriving business. With the turnover analysis dashboard, Leapsome admins can access an overview of current and previous employee turnover data to equip managers with the information they can use to impact retention. The turnover analysis dashboard gives you first-hand information on how many employees might be expected to leave the company. It can therefore inform your hiring processes and further optimize engagement survey data. You can find the turnover analysis dashboard in the Surveys module sub-menu under ‘Turnover analysis’; it will only be visible to Admins.
Data and Insights
The dashboard summarizes your organization’s actual employee turnover over time. The first two graphs visualize past turnover rates. You can understand the quarterly trends of how many total employees joined the organization (presented in green), how many employees left the organization (shown in red), and the total employee number in blue. These trends can help indicate how your turnover rate changes over time.
You can also see the latest turnover data as a percentage, calculated by dividing the number of employees who departed by the total number of employees at the end of that quarter.
Please note: The turnover calculation only works if all employees, including deactivated users, have a start and a termination date in Leapsome. Deleted users are not calculated in turnover analytics.
The second graph below shows a turnover rate that peaked in Q4 2021 and then decreased again through the following quarters.
Use the predictive analysis to address problem areas.
Leapsome’s proprietary turnover prediction algorithm takes multiple engagement factors to calculate the turnover prediction score. For example, the trends in the engagement survey results — with higher weight given to recent data and engagement-focused questions — among various other factors. The algorithm keeps improving and evolving with time as it collects more engagement data.
Leapsome uses the anonymized data to predict turnover for the upcoming months. The turnover rate estimates employees that might leave the company in the future and is categorized based on teams, enabling you to address issues within the teams with the highest expected turnover.
In the top section above, you’ll find the teams with the highest expected turnover rates. The section below it shows the teams with the lowest expected turnover rates. The above example shows that the Tech Sales team has the highest expected turnover rate, followed by the Community SDR team.
Visibility and Access
The turnover analysis dashboard is accessible by default for every user with admin rights, including the survey module admin.
The data stored in the 'Turnover analysis' tab can be exported as an Excel file by super-admins or survey owners. To do that, navigate to Surveys > Turnover analysis > 'Actions'. The Excel file will contain 2 separate pages:
- Page 1 contains columns: Quarter; Total employee number at beginning of the quarter; Total employee number at the end of the quarter; Joined; Departed; Turnover rate.
- Page 2 contains: Column header showing team (for instance, Customer success, Sales etc.) and 1 row showing 'Turnover factor'.
Frequently Asked Questions
Where does the number in "latest turnover rate” come from?
The latest turnover rate indicates the turnover from the last quarter
How can I calculate the number of people who will potentially leave for each segment depicted in our dashboard?
The factor displayed for each segment gives you the likelihood relative to the overall “employee at risk” rate. If that rate is 5-10% and the factor for a segment is 0.5x, your expected rate would be 2.5-5%. You can multiply that number by the number of employees in the respective segment.
When we have a wider percentage range for “employees at risk” does this mean there is a discrepancy in the figures and the numbers are less reliable?
It indicates that the sample size Leapsome can use is smaller and, therefore, can have a less precise forecast.
Does the platform recalculate data in real-time?
Which users' data is included in the analysis?
We use data of all active and deactivated users, given they have a start date, and if applicable, termination date listed in their profile. Deleted users do not count in the analysis.