Turnover rate

Process for measuring turnover rate

A high turnover rate has significant negative effects over organizations as there are some important side effects:

Increment in costs associated with recruitment and training.
Loss of institutional knowledge as when experienced employees leave, they take valuable knowledge, skills and expertise with them.
Constant turnover can create a sense of instability within the organization, negatively affecting the morale of the remaining employees.
Disruption of team dynamics as building trust and strong relationships within teams takes time.
Companies with high turnover rates may gain a reputation as undesirable places to work, making it harder to attract top talent.

Turnover is often viewed negatively, however a moderate amount is natural and even beneficial for most organizations. The key is managing turnover in a way that reduces the negative impacts while capitalizing on its potential benefits: fresh perspectives, performance boost, etc.

1 What information does the Nailted forecasting turnover model use?

Nailted’s prediction algorithms uses machine learning to predict the likelihood of an employee leaving the company, offering an advanced tool for HR and management teams to proactively manage employee retention applying the required retention strategies.

The model is based on historical employee data, including both current employees and those who have left the company. Nailted forecasting turnover model uses a variety of demographic, professional and engagement-related variables to make its predictions. The type of variables used, which may include but are not limited to are the following:

Individual demographic information: Personal information about each employee that helps to understand the composition and diversity of the different employee groups is used to forecast employee turnover.

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In order to keep your data up to date, we recommend you to integrate your HR software with Nailted. See here the full list of supported HR softwares.

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Individual Professional Information: This type of attributes refer to the employee journey within the organization.
- Tenure of the person in the company: Amount of time that an employee has been working at the company.
- Turnover: The information used is the aggregate turnover of all the groups that the individual is part of.
Aggregated Team and group metrics: This information is gathered from the surveys that Nailted sends to employees.
- Participation of the groups of the person
- eNPS of the groups of the person
- Engagement of the groups of the person
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Nailted forecasting turnover model will never use individual participation, eNPS or engagement responses. The average of the group's values in which the employee is included will be used to protect individual privacy and prevent potential biases.

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It's important to note that the specific variables used may change over time to improve the model's accuracy and relevance.

2 What methodology does the Nailted turnover forecasting model use?

The methodology that Nailted turnover forecasting model uses is composed of 5 different phases:

Data collection: Historical data on employees is gathered, including those employees who have left the company and those who have remained as part of the organization. Individual demographic data is collected, while survey metrics are aggregated at the group level.
Data preprocessing: The data collected is cleaned, normalized, and prepared for model training. Special care is taken to ensure that no individual-level data is included.
Model training: Supervised machine learning techniques are used to train the model on the historical data. The model learns to associate patterns in the input variables with the likelihood of an employee leaving, based on demographic information and group-level metrics.
Model validation: The trained model is validated using a separate dataset to ensure its accuracy and generalizability.
Prediction: For current employees, the model uses their demographic information and the aggregate metrics of their groups to estimate the probability of them leaving the company.

3 Limitations and considerations

It is important to note that while the Nailted forecasting model can provide valuable insights, it should not be used as the sole basis for making decisions about individual employees. The predictions are probabilistic and based on historical patterns, which may not always apply to every individual case.

The model’s predictions should be used as one of many factors in developing employee engagement and retention strategies at the group or organization level.

4 Privacy and data protection

Nailted prioritizes employee privacy and data protection by using only aggregated group-level data for metrics, ensuring that individual employee responses remain confidential and are not used in the predictive model.

5 Ongoing improvement

Nailted is continuously monitoring the model’s performance and updating it with new data to ensure its accuracy and relevance.

Our team is committed to staying at the forefront of ethical AI and machine learning practices in HR analytics. Suggestions and insights from Nailted users and the broader HR community are welcome to help shape the future development of our attrition prediction model.