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Identify churn risk

Introduction

Churn risk, or the propensity of employees to quit their organization, is a major challenge in the field of Human Resources (HR). This is a major problem because employees’ turnover can create problems in business, bring down the morale of other employees and be expensive in terms of recruiting and training. This is a difficult task, however, to determine churn risk since it entails assessing many factors including job satisfaction and performance as well as environmental factors. The field of Human Resources has started using Machine Learning (ML) and Artificial Intelligence (AI) to determine the churn risk, and this has helped in making the process more effective.

Challenges

It is therefore important to identify churn risk in the HR industry has some challenges. First, there is the issue of people’s behavior which is rather hard to predict as it depends on many factors that cannot be measured. Second, there is the issue of data. This makes it difficult to obtain reliable and relevant data on employees especially due to issues such as data privacy and trust. Third, classical models for processing employee data do not always work when it comes to identifying the factors that may lead to employee turnover. Finally, there is the issue of implementation. Although an organization may be able to determine which employees are most likely to quit, it may not know how to keep these employees, which is no easy feat.

AI Solutions

These challenges can be effectively met with the help of AI and ML technologies. For instance, AI algorithms can work with big data and provide insights that would be impossible for a human to make. This is not only limited to the numerical data such as employment duration or salary but also unstructured data such as text from employee reviews or social media. These patterns can then be used by machine learning models to make predictions about the future behaviour of, for example, an employee’s intent to leave the company. In addition, AI can offer real-time analysis and recommend courses of action that are most likely to bring positive results given the historical information. Some of the big names in the industry including IBM have been using their ‘Watson Talent’ solution to help predict employee turnover and provide recommendations on how to improve employee retention.

Benefits

There are a lot of advantages of applying AI in identifying churn risk. First, it enables identification of high risk employees at the earliest, thus helping organisations avoid the costs that are incurred due to employee turnover. Second, it offers a better and all round assessment of employee activities thus minimizing chances of prejudice or missed facts. Third, it also enables the HR departments to be more strategic in their approach as they are able to leverage predictive analytics to formulate retention strategies prior to the actual event. Last but not the least, through applying AI, the analysis process can be automated and hence the HR professionals can concentrate on other important and strategic activities.

Return on Investment

It can be very beneficial to invest in AI for churn risk identification. A study done by the IBM Smarter Workforce Institute shows that there is a possibility of reducing costs by implementing proactive retention practices for employees at risk of quitting by $300,000 over two years for every 100 employees. In addition, the application of AI in HR can help in increasing the efficiency of the workforce through the automation of mundane activities and hence enhancing the return on investment. However, it is crucial to point out that the ROI will not only vary with the cost of the AI solution but also with the turnover rate of the company, as well as the success of the retention strategies that it will be implementing.

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