Employee Retention

HR professionals collect massive amounts of data - from hiring pipelines to employee history to performance reviews. With Akkio, you can use that data to uncover insights that lead to employee attrition and keep your best people working for your team.
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Background 

In the United States alone, companies lose a trillion dollars a year from employee turnover. These losses can be devastating, especially amidst a time when many businesses are barely hanging on.

Besides the cost of hiring and training new employees, employee attrition means losing winners, problem solvers, and valuable team assets. The loss of an employee also leads to the breakdown of team morale, which can create a vicious downward spiral, with more employees following suit.

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.

There are many complex, interrelated variables that impact the likelihood of employees quitting, which makes it extremely difficult—if not impossible—to manually predict which employees will quit, when they’ll quit, and why they’ll quit, especially at scale.

This is where machine learning comes in, which finds statistical patterns in vast amounts of historical data, to uncover what humans would miss. In this case, machine learning can be used to find patterns in employee turnover and retention, and create a model for predicting attrition.

With Akkio’s no-code machine learning technology, it’s fast and easy to build a model to predict employee attrition.

Predicting Attrition With AI

To get started, sign up to Akkio for free. As with any machine learning task, the first step is getting historical data and selecting a column to predict.

Historical Data

In this case, our historical data is the “IBM HR Analytics Employee Attrition & Performance” dataset from Kaggle. This file has synthetic data on over 2,000 employees, including columns on the employee’s wage, department, travel amount, education, overtime hours, and more.

There’s also a column called “Attrition,” which is either “Yes” or “No.” That’s the column we want to predict.

We’ll start by hitting “Create New Flow.”

Then, we can click to upload the CSV from Kaggle by hitting “Upload Dataset” and selecting the file.

And that’s it! We’ve now added historical data that can be used for building the model.

Building the Model

From there, we can simply select the column we want to predict, called “Attrition,” and a predictive model will automatically be made in seconds.

You can also select a longer training time—from 1 to 5 minutes—for potentially more accurate models. Keep in mind that longer training times aren’t always better, and may lead to overfitting.

Also note that you don’t pay for model training time, unlike with many traditional automated machine learning platforms, so feel free to go crazy building models.

Analyzing the Model

After a model is created, you get a simple overview, which highlights the top fields, model prediction quality, sample predictions, and more.

We ended up with a very high-quality model, with a raw accuracy of almost 90%. In short, among over 2,000 employees, we correctly predict which would quit, and which wouldn’t, 90% of the time.

There’s also the option in the top right to “See Model Report,” which lets us easily collaborate with others and share these model details with anyone, even if they don’t have an Akkio account.

Deploying the Model

Now that we’ve built a machine learning model, it’s time to deploy it in the real-world.

With Akkio, its easy. You can use any of the deployment mechanisms with a few clicks - including standing up a dedicated webpage for the model. There’s also the option to deploy the model via Zapier, a no-code automation tool. Zapier connects tools with what they call “Zaps,” and you could use a Zap to send new employee data to the Akkio model automatically.

Zapier allows you to link up thousands of different tools, so no matter where your data is coming from, it’s easy to connect it to Akkio to get a prediction, even if you don’t have any technical expertise.

Setting Up An HR Early Warning System

Traditionally, HR teams often use a manual “Early Warning System” to guess if an employee would quit. This includes variables like absences, work quality, and behavior analysis. However, this manual analysis is time consuming, difficult to scale, and can cause employees to slip through the cracks.

A data-driven approach is more justifiable, and far more efficient.

With Akkio, any HR team can easily build and deploy an AI-powered Early Warning System. Let’s walk through a simple example in Zapier. First, we create a “trigger” to pull in new employee data.

For this example, let’s use a Google Sheets file named “Employees” as the source of new transaction data. Note that any database could be used.

We’ll then use the employee attrition prediction model we’ve built to predict whether a new employee is likely to quit. To activate Akkio in Zapier, visit this link, and then select “Make Prediction in Akkio.” Make sure that the data you input exactly matches up with what was used to build the model.

Now, Akkio will predict whether a given employee is likely to quit. We want to capture that prediction, and if the prediction is that the employee is highly likely to quit, we can send an email warning to the HR department. To do that, we’ll add a filter that runs if the prediction of attrition being “yes” is greater than 0.9, or 90%.

Finally, we’ll send an email warning to the HR department for any employee that Akkio predicts is highly likely to quit.

Summary

We’ve explored how employee attrition costs companies inconceivable amounts of money. Massive organizations employ expert AI teams to build attrition prediction models, but these efforts are too costly and time-intensive for millions of other firms.

Using Akkio’s no-code AI, we can effortlessly build and deploy attrition prediction models, and warn HR teams via email when intervention is needed.

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