Introducing Empirika Retain. A Machine Learning Solution for Employee Turnover Prediction
Empirika is an analytics consulting firm that specializes in delivering data-driven insights and solutions to help businesses grow and thrive. Empirika Retain is our newest product that aims to implement machine learning models to predict employee turnover (or churn) with high accuracy.
Employee turnover is a major challenge for many organizations, especially medium to large size companies that are spending a large amount of money and resources in hiring new employees, onboarding them, and training them, and the loss of productivity they face when employees leave.
By using Empirika Retain, you can leverage the power of machine learning to identify the factors that influence employee retention, the patterns and trends of employee behavior, and the likelihood of employees leaving your company in the near future.
How does Empirika Retain work?
Empirika Retain can help you reduce employee turnover, improve employee engagement and satisfaction, optimize your human resources strategies, and increase your return on investment. Here is how it works:
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Data collection: Empirika will need access to some of your human resources data, including time spent at the company, satisfaction level, average monthly hours worked, number of projects, previous performance evaluations, salary, work-related accidents or issues, previous or prospective promotions, current roles (e.g. sales, support, IT, analytics, technical, marketing, accounting, c-suite, etc.), among others. Empirika is not interested in collecting any personal identifiable information such as names, SSNs, phone numbers, or addresses. We will only use the data that is relevant and necessary for building the machine learning models.
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Data cleaning: Empirika will perform data cleaning to ensure the quality and consistency of the data. This includes removing duplicates, addressing outliers, missing values, errors, and anomalies. Empirika will also perform data transformation to convert the data into a suitable format for analysis and modeling.
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Data analysis: Empirika will perform data analysis to explore and understand the data. This includes descriptive statistics, correlation analysis, distribution analysis, hypothesis testing, and visualization. Empirika will also perform feature engineering to create new variables or modify existing ones that can enhance the predictive power of the models.
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Model building: Empirika will build machine learning models using various algorithms and techniques such as logistic regression, decision trees, random forests, support vector machines, neural networks, etc. Empirika will use cross-validation and grid search to optimize the hyperparameters of the models and select the best performing ones.
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Model testing: Empirika will test the models using unseen data to evaluate their performance and accuracy. Empirika will use various metrics such as accuracy, precision, recall, f1-score, roc curve, auc score, confusion matrix, etc. to measure how well the models can predict employee turnover.
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Model improvement: Empirika will improve the models by fine-tuning them based on the results of the testing phase. Empirika will also perform feature selection to reduce the dimensionality of the data and eliminate irrelevant or redundant features. Empirika will also perform model interpretation to explain how the models make predictions and what are the most important features that influence employee retention.
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Report generation: Empirika will generate a comprehensive and concise report that summarizes the findings and insights from the data and the models. The report will include visualizations such as charts, graphs, tables, etc. that illustrate the patterns and trends of employee behavior and turnover. The report will also include recommendations and suggestions on how to improve employee retention and engagement based on the data insights.
Do you want to learn more?
Get in touch so we can start the conversation.
