While the significant rise in online technologies has enabled further flexibility in the workplace, it has made talent retention far more challenging. With approximately 70% of employees staying at a job for more than a year and the costs associated with sourcing and training talent to work in a specific position, more businesses are exploring ways to maintain their workforce. Even technology leaders like Google confess they are struggling with workplace attrition. Despite receiving more than two million applications every year, employees tend to only stay for just over a year. The costs of replacing an employee are high and have driven businesses to implement new talent-retention measures.
In our interconnected world, people have a much broader scope of potential available jobs when looking to secure employment. Most businesses are looking to hire employees who have the highest level of impact on the success of the workplace. Open positions are often available to those seeking employment and actively advertised to existing employed workers.
This competitive form of recruitment increases with technologies like Machine Learning and the ability to examine large datasets and recommend the best candidate for a particular job, regardless of their employment status. However, by applying ML, a business can predict the potential of a candidate leaving their job shortly. ML can establish an action plan to increase employee retention, especially for employees with a high flight risk. It’s easy to assume that the major driving factors determining a change in the workplace are predominantly financial, but this may not be the case. A Harvard Business Review survey discovered that employees who left their job within six months of the study enjoyed their work 31 per cent less, worked to their strengths 33 per cent less often, and were 37 per cent less confident that they were progressing in their role than those who did not leave.
Working alongside other senior leaders, CFOs need to implement the most efficient employee retention measures. Progression in data analytics has enabled more innovative and intelligent insights into which employees may potentially leave a business. Many companies are now prepared to use this information to understand and influence employee retention trends.
Machine learning examines the data available in a business and enables teams to identify employees that may decide to leave. With this valuable information, it becomes possible to anticipate their behaviour and improve our understanding of why they are thinking of departing and what can get them to stay.
Implementing these employee retention measures in our working world is critical. According to recent studies, nearly 30% of employees are actively looking for new opportunities at different businesses. Data in HR typically contains salary history, performance metrics and disciplinary details for employees. Machine learning algorithms can explore other information such as whether an employee has applied for an internal job, if they manage people, whether their role is critical and if management has noted this individual as a high-potential employee. This process can fit the specific requirements of a company data strategy.
Machine learning isn’t necessarily a complex process compared to other artificial intelligence processes. Businesses have been applying a similar system for some time. Subscription-focused companies, for example, have used customer data and implemented machine learning to determine which customers they may lose and understand what actions could prevent them from leaving and encourage other users to purchase more services.
Machine learning models can efficiently determine which employees intend to leave within the following year. Applying ML has enabled some businesses to determine which individuals may quit or take early retirement. This process was achieved by exploring years of HR information system data and using this to create a set of proactive measures to improve the situation for these particular employees.
Once a business establishes if a top-performing employee is considering leaving the company and decides it wants to retain this person, the next challenge is what can prevent this from happening. Implementing the necessary employee retention measures becomes the focus.
The ultimate goal is understanding how responsive employees are to changes in the value proposition, whether this is compensation, benefits or other rewards. Listening to employees through discussions and surveys enables a business to examine various benefits from health and wellbeing to pension and holiday entitlements.
Another avenue through which ML could significantly aid in retaining talent is through engagement. According to a University study of 50,000 professionals at 59 different international organisations, employees that were more committed or engaged were 87% less likely to leave their current company. The same study also found that raising monetary compensation can increase employee retention considerably. An increasing amount of businesses are striving for more intentional engagement of their employees, and the marriage of the two could prove to have an immense impact on people retention.
Results acquired from ML-focused studies allow businesses to enhance the employee experience for their chosen candidates. Predicting employee intentions gives employers more insight into what the talent pool expects in their careers. Machine Learning provides employers access to committed talent but gives them the opportunities to develop a holistic work environment that employees choose. Despite some hesitancy in implementing AI and other related technologies, it’s clear they can prove to play a critical role in maintaining a stable working environment.
