If you’ve been following my blog series beginning with Analytics for Employee Retention followed by Creating a Score for Employee Attrition, then you know my analysis so far has been all about the data. I made sure the data was clean and representative, and then binned the data to investigate how each variable relates to my target of attrition. From there, I performed distribution analysis to discover just how predictive each variable is. With all that knowledge, I created a single Attrition Score and established a cutoff to leave me with just the employees likely to attrite. Now that I’ve identified a group of employees that are likely to leave the company, it’s time to figure out how to make use of this information.
Define a Decision Point, Then Start Simple
In this case, I will use an employee’s annual review as the decision point. During an annual review, a manager can use the score along with other information to inform their actions around promotions and raises.
I started with a simple matrix as a way to think about segmentation based on two variables, employee performance and the probability of attrition provided by the score. I plotted these recommended actions for managers, derived from the decision tree, in the matrix below:
Which Employees are Worth Retaining?
A manager will need to take different actions for different employees, this matrix accounts for each employee’s Attrition Score (y axis) along with their performance (x axis) to recommend what the manager should do come annual review time. For example:
- If an outstanding employee has a high probability of attrition, the manager should strive to retain.
- If an outstanding employee has a low probability of attrition the manager should continue business as usual.
- For employee’s with poor performance, the manager should not use resources to retain them, and should manage them out.
Build a Decision Tree
A decision tree allows me to do the same segmentation, but with more variables. In this example, I use the Attrition Score plus other factors to further segment the employee population with the end result of a recommended action. I used FICO® Analytics Workbench™ to create a decision tree; my analysis showed that the number of years at the company, combined with the total working years, created effective segmentation. Other helpful variables include overtime and the probability of attrition score (P_Attr_). One major advantage of using a decision tree is the ability to use variables together to profile unique populations and to apply specific actions based on knowledge other than prediction. You can find more discussion on decision trees here.
Develop and Refine a Retention Strategy
We know that replacing employees can cost a lot of money, so it could be worth the cost to proactively implement programs. However, a decision tree can help create even more effective segmentation than just using the score alone, so I applied a high level exclusion of employees with poor performance. This leaves only employees that are meeting or exceeding expectations. Beyond understanding the attrition rate, segmentation on additional variables gives me insight that can help me tailor the actions to be even more effective.
For employees who have a low number of years at the company and a low number of total working years, a company could set up a “newcomers club.” This is a way for the employer to make employees feel welcome, and help build social relationships that could make them more “sticky”.
For employees with more experience overall, a different approach will be more effective. Presumably, these employees are interested in building their professional network. A company can encourage attendance at conferences or participation in organizations like WITI. This will help keep these employees happy by focusing on helping them grow professionally.
For those employees with more years on the job, the data tells us that working overtime has a big impact on their likelihood to leave. Employees who work overtime and have a high attrition score have a whopping 45% attrition rate, so this is where the company should focus its biggest expenditure. From earlier binning analysis, we recall that having stock options resulted in a lower attrition likelihood. Even though stock options are an expensive investment for the company, this segment of employees will likely offer a good return on investment.
Those employees who don’t work overtime but have high attrition scores still have a 20% attrition rate. Here, the company must work to understand what would make these employees stay. Perhaps their managers can conduct a personal interview, with the goal of eliciting what could make them happier. Busy people managers don’t have time to intensively meet with all their employees; they can use this decision tree to determine which employees are most at risk, then use that information to determinie the best way to allocate their time to produce the most positive outcome for the company.
3 blogs later and we’ve used analytics to address the problem of employee attrition! You should now know how analytics can be applied to an employee retention decision, and from this, you can imagine how anywhere there is data and decisions, there is a way to make decisions more effective through data analysis. FICO® Analytics Workbench™ was my tool of choice to go from data to analysis to action: check it out for yourself or join the conversation in the Analytics Community.