Summary:
Employee turnover is one of the most major and troublesome challenges that an organization has to face, which directly influences its productivity and the morale of its staff. Thus, there are a variety of personal and organizational factors that cause employees to leave their companies. Personal factors can be defined as the characteristics of the workers, which involve the age of the individual, gender, and whether the person is married or not, which determine whether he or she is ready to remain with the organization or move to another company. These include; the job or position occupied by the employees and its classification, satisfaction, participation, organisational unit/department, distance travelled to work, flexibility between working time and family & social time, years of service with the organisation, and opportunities for training, respectively. Other drive factors at the organizational level are environment satisfaction, performance ratings, stock options, pay, extra working hours, and business travels, which contribute to an employee’s decision to exit. Thus, if there are different factors to be taken into consideration depending on a specific context, companies can use this information to enhance employee satisfaction and decrease turnover. In addition, the opportunity to apply the methods of AI for the analysis and prediction of employees’ behaviours could be considered a powerful intervention that helps mitigate attrition. AI can be used to identify employee satisfaction through trends analysis, amount of work done, customer satisfaction, recommendations to employees, work-life balance, employee’s wages, and better recruitment strategies for a better and more fulfilling employee experience.
Background and Motivation:
While employee turnover refers to the rate at which workers leave an organization, it is essential to understand that such action has implications for organizational productivity, employee morale, and profitability. It’s a problem that goes back in time and depends on the existing economic conditions of an organization, its culture, and the employee's satisfaction with his/her job. Other emergent factors that have introduced new variables include; recent changes such as technological advancement, for instance, and the recent disease COVID-19, among others. This is why it is essential to know the reasons why employees quit and have a clear goal of creating strategies for their retention. Promotions are often associated with advancements in career, pay, working conditions, satisfaction, and culture within organizations. People crave professional development and remuneration, work-life, satisfaction, and vision and missions that fit their organization. Therefore, it is important for organizations to try to address these factors so as to enhance the retention rates so that people feel valued and that they want to further please the organization.
Top Employee Attrition factors that affect Companies:
Lack of Career Advancement Opportunities: In other words, if employees feel that there is no potential for career advancement or skill enhancement in their present role or company, they are likely to look for other opportunities.
Inadequate Compensation and Benefits: The other key areas that should be considered to foster talent retention include compensation structures, medical covers, and incentives. If employees are not given what they need, or feel that they are not being paid enough, then they are more likely to move on.
Poor Work-Life Balance: It is evident, therefore, that checks and balances between working hours and the rest of one’s life are of critical concern to an employee. Such sources of conflict might mean that companies that lack flexibility or do not support personal needs and well-being, might experience high turnover rates.
Low Job Satisfaction and Engagement: All those individuals who do not enjoy the sense of herein engagement, job satisfaction and lack adequate organisational support may start seeking greener pastures elsewhere. Motivators like autonomy, resources, accomplishment, recognition, and intrinsic alternatives motivate the employees and thus make them satisfied with their job.
Negative Organisational Culture: Based on the information given above, it is very clear that organisational culture and value influence its employee retention rates. Work-related stress, neglect, or violation of equalization policies, and when employees’ conflicts and goals do not align with the company’s values, they may look for better opportunities.
Limited Training and Development Opportunities: Graduates regard training and development as important aspects to be enhanced. Owing to the reasons put down above, companies that fail to invest in their employees’ professional development may be likely to record high levels of attrition.
Job Insecurity: This is because workers cannot feel secure in their careers when their jobs are frequently threatened. Addressing them may entail explaining openly strategic plans of the company and prospects of the employees’ positions.
Unmanageable Workload: Long working hours put the staff and officers at risk of burnout and high turnover rates. Maintaining workload and offering proper assistance can play a role in retaining employees in a company.
9. Insufficient Employee Benefits: Selecting compensation based solely on wages and salaries may not cut it with employees who expect a well-rounded set of benefits that comprises medical coverage, pensions, and other employee benefits. Rewards are also known to present a challenge where adequate motivation is not provided such that employees feel encouraged to stay on.
Mismatch between Job and Person: Similarly, if an employee does not have proper fitment to the job role in terms of skills, interests, or career goals intended, the outcome is dissatisfaction and turnover. All in all, candidate selection and screening remain important in achieving retention as a company.
Why Attrition Affect Different Job Role:
In this case, exodus depends on specific job positions within a company and thus has varying effects on organisations. Some positions may entail a high turnover, and possible reasons include stress at work, working hours, minimal chances of promotion, and dissatisfaction with working conditions. For example administrative occupations that call for high customer contacts, for e. g. , the sales and customers services departments are most likely to post high turnover rates due to the strenuous nature of the job. There are changes in technology, and this means that there might be technical roles that experience some attrition due to technology changes which require technical skills, causing some gaps in specialisation which might make one have to go for training and more. It is also an applicable fact that leadership-level roles may have churn for the stress involved in coordinating individuals and units alongside delivering organisational results.
Attrition Rate by Education Level and Education Field:
Understanding how attrition rates vary based on educational background can provide valuable insights for developing tailored retention strategies. This analysis focuses on two dimensions: education level and education field.
Attrition Rates by Education Level: Several factors can hinder career advancement when human capital is undergoing diversification, including differing education levels of the employees and thus their expectations. For instance, while the highly educated employees, who have patrons with top-notch jobs, may look for more challenging positions based on their certifications of higher education, they are likely to leave in large numbers if their desired status is not attained. On the other hand, the organisation may also experience high turnover rates from employees with low education because they seek better remuneration and promotions to other organisations.
Attrition Rates by Education Field: Education as a field can also impact the number of students who drop out through attrition rates. Some industries may undergo fluctuations in demand within the economy, therefore making it easier for employees to seek other employment opportunities. First, assigning duties to correspond with job positions may determine employee satisfaction and turnover rates based on their area of education.
Employee Attrition Prediction Using Machine Models:
Logistic Regression:
Logistic Regression is a linear model used mainly for classification since it can provide the probability for answering multi-class problems. It estimates the likelihood of an object to be of a specific type by applying the logistic function. It is called logistic regression due to its capability to provide continuous output values in between 0 and 1, however, it is fundamentally a classification algorithm. It is preferred for its ease of use and easy interpretation: this is ideal for initial models or cases where the correlation between attributes and the objective variable is postulated to have a linear trend.
Support Vector Classifier:
SVC or SVM is another informative supervised learning algorithm for classification tasks. It tries to identify the hyperplane that separates classes in the feature space with high margin and at the same time has a maximum margin to the neighbouring points, thus it is less sensitive to noise. SVC has the ability to deal with both linear and non-linear patterns when classifying samples using kernel functions. High-dimensional space having lesser reliance on the training data as only the margin points or support vectors are needed to classify new data points.
K-Nearest Neighbors (KNN)
The K-Nearest Neighbors algorithm is an easy-to-implement method that belongs to non-parametric as well as the lazy learning algorithm as well. In specific, it partitions a dataset on the basis of the majority class of the k nearest neighbours in feature space. There is nothing special or different about the nature of linear or non-linear decision boundaries from the perspective of a KNN algorithm – as KNN do not impose any distribution of the data. KNN is easy to explain and easy to implement in some ways but it can be very slow for large number of cases in terms of its computation because it has to go through every case each time to calculate the distance.
Decision Tree:
Decision Tree is one of the powerful and more interpretable found supervised learning algorithms used in both classification and regression problems. It linearizes the feature space by repeatedly bisecting it according to the values of features. Here, every node on the left signifies a decision made by a feature and every node on the right is the class label or regression output. Decision Trees can manage both the numerical and nominal predictors, and they also determine which features are better to be used for splitting. But they are prone to overfitting especially when the tree depth is not properly regulated this leads to.
Random Forest:
Random forest is one of the types of decision tree algorithms that creates numerous trees during the training phase and makes the most frequent predicted class in the case of a classification problem or the mean predicted value in the case of a regression problem. It averages the results across several created decision trees to avoid issues of overfitting and increase the generality. As a modification of the concept of bagging, Random Forest adds randomness in the process of training data resampling and in selecting a number of features to take into account for each split with each single tree. It is less sensitive to noise as well as capable for working with high-dimensional features even with large training data. Random Forest has also proved to be very effective for classification techniques and is very accurate, scalable and easy to implement.
Comparison Accuracy Graph:
F1 Score & Mse Values:
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