How can you predict employee turnover using machine learning algorithms?

Unfortunately, from my experience, the HR data is extremely dirty most of the time. Many companies, especially the SMEs are not keeping the data up to date. One approach is enriching the data, like calculating the distance from home address to office address, or adding education and experience information with scores to the HR data. Another contribution might be, adding external data and enriching the data with financial analytics, sector based analytics. For example average developer salary on the country or region might be critical for developer positions. Finally, Machine learning models developed by including information such as gender, place of birth, and age are prone to discrimination and one should stay away from these data.

From what I've seen as a data scientist, the quality of the data used is very important for how well machine learning models work, especially in sensitive areas like employee turnover. In addition to basic information about people's backgrounds and jobs, elements like how the team works together, immediate feedback from a boss, or even outside economic factors can have an effect. While public datasets are a great place to start, they may be lacking in nuances specific to certain industries or corporate cultures. It's also important to think about ethics when collecting data, especially when it comes to employee data. Make sure that all data is anonymized and doesn't have any information that could be used to find out who it belongs to.

Based on the various interactions, as of now, the AI and AI-powered systems can effectively collect and process large volumes of structured and unstructured data such as emails, social media interactions, and performance reviews. Once the relevant data is collected, data analytic techniques such as machine learning algorithms can be applied to identify patterns and correlations within the data. These algorithms can uncover hidden factors that contribute to turnover such as job dissatisfaction, lack of growth opportunities, or poor work-life balance. This analysis can go along way indent identifying for a company the ways it should adopt so as to limit Employee turn over

Data Collection: Gather data on employee demographics, performance, satisfaction levels, and tenure. Preprocessing: Clean and normalize data, handle missing values outliers and so on. Feature Selection: Choose relevant features that might influence turnover. You also need expert view on this, empirical methods maybe enough. Model Selection: Use algorithms like logistic regression, random forests, or gradient boosting. Training: Train the model using historical data, i.e., the data you have collected. Evaluation: Assess model accuracy using metrics like precision, recall, and F1-score, AUC and so on. Deployment: Implement the model to predict future turnover trends. The key is continuous model refinement with updated data.

Look out for unstructured & semi-structured data as well, sources like employee surveys floated in the past, sentiment analysis on textual reviews by the employee & for the employee. Survey results may need to be corrected for biases and then encoded appropriately to be fed into the model. Integrating external data sources which capture macro economic factors is paramount (factors like COVID, "great resignation", mass layoff events)

"If you know the enemy and know yourself, you need not fear the result of a hundred battles," said Sun Tzu. EDA helps you thoroughly "know" the data before the real analytical battle begins! With EDA, you can understand employee demographics, quick descriptive patterns, and start digging into likely indicators of turnover. By mapping out correlations and discerning hidden insights, EDA helps prioritize factors and build models.

I believe that exploratory data analysis (EDA) is useful not only for figuring out how the data is distributed and how it is related to other data, but also for testing initial assumptions and finding new insights that were not expected. There have been times when EDA has made it clear that factors that were thought to be less important at the time actually play a bigger role in employee turnover. This phase also allows for the detection of anomalies or patterns that may indicate external influences or biases in the data collection process.

The first step is to put something small into practice and validate it. If it's okay, then it's time to move forward. However, before taking this first step, you must understand the problem's fundamentals. Due to its time-consuming nature, it's a good chance for many to cut corners. Unfortunately, it leads nowhere. Understanding the problem's fundamentals means asking "why" as often as required. Sometimes, it sounds very irritating, but you do it to ensure every step is flawless, logical, and comprehensive. And the next step must stem from the previous one.

Many hidden variables are to be discovered at the data pre-processing stage, eg: in an attrition model for a Fortune 50 company, we discovered that frequency of long leaves taken by the employee correlate highly with tendency to leave the company. Try to devise a robust strategy for creating combined/extracted variables, this should not only be based on data science principles but also on observations made by the end users. Eg: the HR department found out that providing spot bonuses to employees (even if they are small in quantum) leads to positive sentiment & hence a reduction in probability of attrition. Creating such independent variables from raw data will translate into high model accuracy.

From my hands-on experience, while tools are pivotal in Exploratory Data Analysis (EDA), industry intuition is invaluable. EDA is about examining the main characteristics of a dataset, often visually. At times, subtle patterns might be overlooked by just crunching numbers. I've seen how understanding the broader company context can explain data spikes that initially seemed alarming. A department's high turnover might simply reflect recent company changes. So, while EDA gives us the 'what,' it's essential to dig into the 'why,' ensuring our insights match the real-world pulse.

From what I've seen, the art of feature engineering is where model accuracy is often won or lost. It's frequently possible to extract subtle patterns from the data by constructing composite features or identifying non-linear relationships. For example, individual factors like commute time or average working hours may not have a strong link to turnover. However, a composite feature that measures work-life imbalance may have a stronger link. During this phase, regular collaboration with HR experts can aid in the creation of meaningful and impactful features.

You can explore market salary comparisons to see if underpayment is a factor or seasoning. The key is to dig into the data and find those meaningful predictors

Feature engineering is both an art and a science. It wasn't that long ago that selecting features was a largely manual process, and sometimes you stumbled into (or stumbled past) important features to prioritize in your model. Automated feature engineering is much more prevalent and there are numerous tools available to support that process (e.g., Featuretools, TSFresh, Featurewiz). Even so, it's important to have domain knowledge, so the resulting features make sense. I've seen situations where the mathematical calculations are valid, but the features are so disconnected with real-world experience or reflect dynamics beyond the organization's capability to influence that they are unhelpful.

I don't really know the specific ML paradigms for this, but here is how I would approach it. A shotgun blast of a library of selected statistical analyses running autonomously over the datasets to see what features pop up organically, unsupervised. Sift through these and investigate their relevancy. Couple these with specific features you might have had beforehand and see what the difference is. This will be the difference between your expectations and naturally occurring patterns, which can inform your lines of investigation.

Feature selection is most critical step in Machine Learning (not really relevant in case of Deep Learning) as it decides quality, size and efficacy of the model. Better ie more relevant the features, the model becomes closer to reality. Some of the most common ones include: - Demographic data - Job characteristics - Performance metrics - Tenure - Employee engagement - Managerial factors - Work environment - Career development - Compensation and benefits - Industry and market conditions - Turnover in the team - Time of year - Employee sentiment

Not sure who in this collaborative worm has said we can build a tenure prediction model too rather than classification. Use information from CV's for features like average tenure, experience, expertise, projects among other features like salary, role, department, recognition and awards , demographic variables, market related ones etc already mentioned here and predict likely tenure. Aggregate over all employees each month to generate report.

Specifically in HR analytics projects, prioritising the practical implementation of a model over its accuracy is crucial. Consider an attrition model with 86% accuracy, impressive from a data science perspective. However, the HR Head rejected it because some variables were too complex to translate into actionable policies, & the model's 15 variables were overwhelming. Simplifying the model to 7 interpretable variables resulted in a 12% drop in accuracy, but enabled HR to implement a bonus policy for high-performing employees with a high attrition probability, effectively retaining them. Opt for models with outputs that are easily understandable. It's crucial to align with end users on result usability at the model selection phase itself.

Building any of these ML models to address employee turnover can be challenging due to two common factors: Imbalanced Dataset: Employee turnover data often has a significant class imbalance, with fewer employees leaving compared to those staying. This imbalanced data can bias any ML model. To address it, techniques like over-sampling and under-sampling i.e. SMOTE are used to balance the data before model building. Time Considerations: ML models can predict the likelihood of an employee leaving, but can't answer "when it will happen". To address this crucial timing aspect, advanced methods like survival analysis can be applied. Survival analysis not only predicts the likelihood of an event but also estimates when it is likely to occur.

I'm not certain which member of this collaborative effort proposed the idea, but it seems we're considering the development of a tenure prediction model instead of a classification model. We can leverage data from CVs to extract various features, such as average tenure, experience, expertise, projects, as well as factors like salary, role, department, recognition, awards, demographic variables, and market-related indicators, which have been previously mentioned. With these inputs, we can predict the probable duration of employment for individuals. To create monthly reports, we'll aggregate this predictive model's results for all employees.

Many binary classification algorithms are available. The choice depends on the underlying structure of the data such as linear, non-linear etc. Although some statistical tests can be done to discover that and then chose the ML algorithm, another practical way is to try all possible algorithm and see which one works best for the given dataset. Care has to be taken to do this investigation in cross-validation way (k-fold) to get relatively non overfitting. situation.

Apart from Accuracy as a measure, which is easy to understand, more refined metrics such as Precision, Recall, F1, ROC, etc. can be used in case of Binary Classification. They are more appropriate especially when data is imbalanced i.e. when entries in each output class are not comparable. Employee turnover is imbalanced as number that are leaving far less (less hope!!) than the ones staying. We can interpret to understand drivers and risks of employee turnover by using techniques like feature importance, partial dependence plots, or SHAP values which explain model predictions and feature contributions. This will help identify employees most likely to leave and segment them into different groups for targeted interventions.

The actionability of a predictive model lies not just in its accuracy but in its explainability to decision-makers. Without proper interpretation, statistical model outputs are indistinguishable to the users from the raw data. Ensure you translate the business implications of your model - what drives customer attrition? What worked for that division that had unusually high employee engagement?

Model Interpretation is like deciphering the research findings. You want to understand why the model makes certain predictions or decisions. It's like dissecting the results to uncover the underlying logic. Think of it as reading between the lines to grasp the "why" behind the "what" of your model's predictions. Model Application is like taking those insights and applying them in the real world. You use the knowledge gained from your model to make informed decisions or take actions. It's like taking the scientific discovery and using it to develop a new technology or treatment. You want to put your model's wisdom to practical use, just like turning research into a life-changing invention.

In addition to utilizing Accuracy as a straightforward metric, more nuanced evaluation criteria like Precision, Recall, F1-score, ROC, and others can prove valuable in the context of Binary Classification. These metrics are particularly useful when dealing with imbalanced data, where the number of entries in each output class is significantly unequal. Employee turnover serves as an example of such an imbalance, where the number of employees leaving is substantially less (and, unfortunately, less hopeful) than those who choose to stay.

From my experience, while the tech and algorithms play a pivotal role, the human touch can't be sidelined. Often, the numbers tell only half the story. Employees may be grappling with challenges beyond the immediate scope of collected data. By complementing our model findings with regular employee check-ins and fostering open communication channels, we can gain richer insights. I've seen cases where simple work-life balance initiatives or mentorship opportunities made the difference. It's a blend of art and science—using machine learning to give us direction but relying on our human intuition and empathy to truly understand and address the core issues.

Adding a privacy perspective here. It would be critical to consider the expectation of privacy in the workplace here and the importance of compliance and trust. Personally, when asked how I would sum up privacy in one sentence, I would say: people shouldn’t be surprised when they learn how their data is being used. Transparency and adequate notice have an important role to play in data protection. Furthermore, there are additional critical compliance steps in many jurisdictions for AI systems that process personal data, such as conducting a data protection impact assessment. All the above is my personal opinion and perspective and not legal advice.

I’ve previously seen scenarios like this be dubbed “flight risk prediction”. (The term itself is highly critical IMHO.) I’m certain there’s a lot of data and lots of variables you “could” consider: - Time in position - Time since last promotion - Performance review/ rating - High performer status - etc. However, the two questions to remember are: 1) Would that even be allowed? (Your HR and Legal expert can give much better advise here!) 2) And even if so, would those be reliable indicators? Let’s not forget: a model is just a representation of a subset of data. Your employees and the circumstances outside the AI lab are a lot more complex than a handful of variables.

He escuchado en un curso que dio un experto sobre datos en HR, que una forma de usar data analytics y ahora IA para predecir la rotación, es analizar la cantidad de emails que envían las personas en promedio. Y que cuando baja demasiado del promedio es indicador de desinterés y luego renuncia. También el lugar donde se sientan las personas en el comedor y con quiénes, puede predecir renuncias.

Building on what @andreas Welsch and others have said, even if you find the right data, and variables, a better question is whether this should even be done in the first place; even if it can be done, ethically, responsibly does not mean it should. If it cannot be explained to senior executives and leaders will not act on the insights gleaned, it would be better to use these resources elsewhere.

Well, can you make an educated guess on employment turnover using AI? Yes (given a sizeable responsibly handled dataset in ‘000s of validated datapoints) but this is completely useless unless you dig into “why are they going to leave” and “what can you do about it” Employee exit can be a sensitive topic and is usually a subjective decision based on variables that may or may not be quantified. Yes, quantifiable measures such as engagement, absenteeism , etc can be “believed” to be indicative, but what about key qualitative factors such as relationship with manager? Personal circumstances? Market shifts? Culture? Team dynamics? Personal ambitions? Recognition? Learning opportunities? Working conditions? AI can’t do that!