Are you worried about employee attrition? If not, you're in the minority. After all, a stunning 52% of workers plan on looking for new jobs amidst the 2021 "turnover tsunami."
An organization is only as good as its employees, and stands to incur huge costs when they leave. Some costs like training and induction expenses are tangible, but the most important costs are intangible.
Often, a long-term employee leaving also means the loss of customer relationships, and the context and expertise they had with your industry and offerings. This article will show you how to deal with employee attrition using machine learning.
Employee attrition refers to the percentage of workers who leave an organization. They are either replaced by new employees, or sometimes the role could remain vacant or closed altogether.
The importance of employee retention can't be overstated. Indeed, the financial impact of losing top performers is staggering. According to the Work Institute’s Retention Report, the replacement cost for an employee earning $50,000 a year is a whopping $16,500.
With so much at stake, it's no wonder employers are devoting ever more resources and time to retaining top talent. This is especially true in technology companies where cutting-edge innovation often comes from talented individuals working alone or in small teams. Losing even one key employee can slow down product development cycles and hinder progress on important milestones for a long time after that person departs.
The good news is that there are plenty of ways to reduce your company's chances of losing valuable employees to other organizations or industries — and also ways to predict who might leave before they actually do so!
The bad news? Predicting these things is extremely difficult with traditional methods. Later in this article, we’ll explore how to keep it simple with no-code AI, but first, let’s look at the causes of employee attrition.
There are many factors that can contribute to employee attrition, but the three main causes of employee turnover are:
The lack of career development opportunities is perhaps the common cause of employee turnover. One survey showed that 68% of workers feel disengaged at their jobs. They may feel like they're not learning enough or growing fast enough. Often, these workers don't realize there are ways to improve their skills and grow professionally on their own time — often by taking online classes and working with mentors.
The reality is that many employees feel stuck in their current roles. Employees need a reason to stick around when faced with multiple offers from competitors as well as other opportunities outside your company's walls.
Further, managers who don't provide adequate training or supervision can also drive away top performers. Some people simply need more hands-on training and mentoring to feel prepared for the job, and they may not get it. This makes them disgruntled employees as well. And when those disgruntled employees see opportunities being offered by other employers, you've got a potentially serious problem on your hands.
Low compensation is a fairly obvious cause of attrition. If you make less than others in your type of role, it's probably time for a raise! Reducing turnover rates will directly lead to higher profits for your organization, especially if you can keep your top performers.
Some additional causes of employee turnover include:
If you lose too many of your top performers, your company will suffer from a talent shortage, which can lead to attrition in other areas.
Just ask anyone who has experienced an unfilled position in their department: it can cause serious disruption when crucial functions don't get performed well or at all. That's why it's critical for employers to understand what drives top talent away from their organization.
Similarly, a high rate of attrition drives up recruitment, hiring, and training costs, simply because you have to spend more money on replacing employees. Of course, this also means that onboarding becomes more difficult, increasing the odds that a new hire will leave before achieving his or her full potential.
After all, new hires see a high attrition rate as a red flag, and they may not want to join a company where so many people have left.
Another obvious impact of high employee attrition rates is that you’re losing out on the productivity of employees. If your top performers leave, it almost always means that you have to replace them with lower-performing individuals. After all, that performance is a consequence of the time spent and experience gained in the role.
While there are many contributing factors behind employee attrition, one thing is clear: companies that understand the issues surrounding leadership quality and turnover can make an important business case for retaining their highest performers.
Another drawback of high employee churn is the loss of expertise from your team. People who stay for a while develop deep knowledge. They see patterns and possibilities beyond what’s in your documentation and onboarding information.
It also takes time to build up expertise in new areas, which means it may not be possible to replace top performers if they leave. This can make it difficult to keep up with industry trends or meet the changing needs of customers or clients.
For example, many startups have found that their top talent understands the ins and outs of their products and has an innate ability to create innovative solutions for complex problems. Top performers who depart a company may not be able to be replaced at all — or at least, not quickly enough — which could cause serious damage if your startup depends on staying nimble in order to succeed.
Many organizations assume this is the cost of doing business. Every now and then, employees quit. The truth is, there are always ways to reduce employee attrition, saving costs, retaining valuable talent, and keeping up morale.
One of the most popular tools for preventing attrition is machine learning. By using algorithms and data, companies can identify patterns in their employees’ behavior that may indicate that they are about to leave or want to be reassigned elsewhere within the company.
This allows them to proactively stop departures from happening or nip potential problems in the bud before they become full-blown crises. Some companies have even gone so far as to build entire departments around preventing departures, or at least have empowered their HR teams to better prevent attrition, using AI.
Traditionally, implementing AI was a lengthy and complex process. The result often was an AI system that functioned properly but was hardly accessible or useful to the average user. This is because it required expertise in programming, data science, and deep domain knowledge to develop.
Indeed, the world is filled with highly complex machine learning tools: Xgboost, scikit-learn, and IBM Watson, just to name a few. These tools involve the use of coding languages like Python, and require knowledge in concepts like logistic regression, random forest, decision trees, KNNs, SVM (support vector machine), Naïve Bayes, and many other complex machine learning techniques and ideas.
It doesn’t end there—human resource teams would often hire entire teams of data scientists, who would manually do data preprocessing, build machine learning algorithms, tweak model metrics, analyze feature importance for feature engineering, calculate f1-scores, and more.
Improving and analyzing models used to be a highly complex computer science task as well, as engineers would have to analyze performance on the test data set, and calculate metrics like the false negatives, while creating visualizations like the ROC curve, all manually. In short, the traditional approach to using artificial intelligence for employee attrition prediction was extremely complex and time-consuming.
This is now changing with the introduction of no-code AI, which enables businesses to tap into the power of machine learning on their own. With this approach, businesses can use Akkio’s easy-to-use, drag-and-drop interface that allows non-technical users to build powerful AI applications in minutes. Here’s how you can implement no-code AI into your business.
First, we’ll sign up to Akkio for free. Then, as with any machine learning task, we need relevant historical data. In this case, we need a dataset of employees with a column on whether or not they’ve quit.
In this case, we can use a Kaggle dataset on over 2,000 employees, including columns on the monthly income, department, business travel amount, education, overtime hours, job satisfaction, and more. In other words, we have a variety of potentially predictive categorical and numeric data. There’s also a column called “Attrition,” which is either “Yes” or “No.” That’s the column we want to predict.
We can simply upload this training data, and select that “Attrition” column, and Akkio will automatically build machine learning classifier models in the background. Things like handling missing values and creating a validation set are automatically handled through data mining techniques.
Once the model is trained, we get a simple overview of the model’s accuracy, sample predictions, and more.
Now, we can deploy our model anywhere we have employee data, whether it’s a Google Sheets file or an HR analytics tool. With Akkio, HR managers can use no-code tools like Zapier to integrate into thousands of applications, while more technical teams can use our custom API.
Let’s look at four tips to ensure that your predictions are accurate:
Following these tips will help you build a robust model to accurately predict and prevent employee attrition. Let’s explore each area in detail.
If your data is not tagged, you will need to tag it (manually or via an auto-tagging service) before you can start training the model. In order for an AI system to understand a specific feature of your data, it requires that data be labeled with that feature.
This step sounds easy enough but if you’ve ever tried to do so yourself, you know just how challenging and time-consuming it can be: You have to actually write down each label/tag so the computer knows what each value means! Simple data analysis will reveal how much data needs to be labeled, or, for example, how many rows don’t have a value for “attrition.”
Similarly, you’ll want to ensure that non-predictive columns like “employeecount” or “employeenumber” are not included. Further, you may want to re-consider the use of any private information like marital status.
Unlike humans, a machine learning model needs to "see" an example many times to learn patterns in the data. This is a fundamental difference between human and machine learning. Humans learn by doing (applying what we've learned to new situations). Machines learn by seeing examples of the target many times.
While it's possible to program a computer to solve any simple equation, computers don't have the ability – or the experience – to pick up on patterns for complex tasks with just a few examples, like humans do. So when training a model, it’s important to use large enough training sets, which often requires a longer training time.
The amount of data required depends on the complexity of the problem, and the results you’re trying to achieve. Typically, more data usually means better results.
To improve accuracy and avoid overfitting (where a machine learning system too strictly follows past data patterns), it’s recommended to use as much high-quality data as possible.
Once trained, your ML model will make predictions about new instances based on what it has learned from previous examples in your dataset. These predictions could be wrong if there’s not enough, or not broad enough, historical data. For example, if you build an attrition prediction model on data from just employees in your marketing department, and then apply it to employees in your IT department, your results may be inaccurate.
To prevent this from happening, we suggest testing predictions against real-world data. If your model is used on data that it wasn’t trained with, how does it perform? With these additional checks in place, we can ensure our predictions are accurate.
In many companies, employees who are flagged by AI models as at-risk of leaving their jobs may already be set on leaving. High performers typically have networks that they can leverage in their next roles. Other employees have simply been too good at what they do – they’ve outgrown their current role and are looking for the next step in their careers.
That said, there are many ways to win back at-risk employees, including better and timely HR and management support, promotions and better perks, and better recognition. Let's explore these areas in detail.
This is a no-brainer for any company that wants to attract top talent. Companies that treat their employees well will always have the advantage of having the best talent, whether it's better at attracting top talent or keeping them around once they've joined.
Companies that understand this concept invest in their employees – providing them with career development opportunities such as training and education as well as offering them more challenging work environments so that people stay engaged.
They also recognize the importance of good managers by providing additional management support to key performers who are willing to take on greater responsibility within their teams. This helps to keep key people around for the long haul.
Creating an attractive environment that attracts talented people is crucial if you want to retain your best people. So, you need to make sure when good people perform well, they get rewarded, whether it’s tangible compensation or non-monetary perks like recognition or access.
The key takeaway here is simple: People enjoy working for companies that give back – not just financially but also through greater job security or more interesting work environments.
With this type of employee retention strategy in place, organizations will be able to reduce attrition rates while retaining high-performing employees who ultimately contribute significantly towards business growth and success.
Using AI models to identify at-risk employees is a game-changer for HR departments and can help reduce attrition rates.
In conclusion, using Akkio to predict attrition reduces the high cost of losing talented employees. Similarly, Akkio can be used to easily build customer churn prediction models, which is another type of classification problem.
To learn more about how no-code AI can help your organization, sign up for a free trial of Akkio.