Thread Content
Predictive analysis (http://yunrun.com.cn/community/2768.html) can help businesses better analyze customer interactions, predict system failures, and forecast trends in emerging markets. However, companies need to pay attention to the movements of their internal employees, and predictive analytics can also help them improve employee retention rates, hiring rates, leadership development, and job satisfaction. When selecting candidates, making promotion decisions, and determining which employees may leave in the coming months, business managers usually rely on intuition rather than solid evidence. However, advances in workplace analytics are changing this situation, enabling more data-driven decisions to be made regarding human resources issues. Reduce turnover rates: Employee turnover is costly for businesses, as the costs associated with recruiting, hiring, onboarding, and training a new employee can be 1.25 times their salary. This means that companies must recruit employees more wisely and retain top talent in order to achieve significant returns on investment. Predicting the reasons and timing of employee departures can provide valuable insights and help companies save a significant amount of money. Dave Weisbeck, chief security officer at workplace analysis firm Visier, said, “When employees start leaving a company, it often triggers a wave of resignations.” Big data, along with skilled analysts and engineers, can be used to develop algorithms that identify these patterns and determine the reasons behind them, such as who the managers are How long does it take them to get to and from work? When was the last time they got a salary increase? When was the last time they were promoted? Then, plans can be developed to address these issues. ” Recent research by Harvard Business Review and ENGAGE Talent shows that companies need to use big data to track indicators of employee turnover intentions, and to identify those employees who may be at risk of leaving. Matt Pietsch, ENGAGE Talent’s first Chief Risk Officer, said, “Research shows that there are four main factors that can predict why an employee decides to make a change.” This is useful for companies to predict when their employees may consider leaving and what factors might drive employees of competitors to leave. ” Four main factors that lead to employee turnover: 1. Life events – Employees may experience divorce, marriage, their spouse being transferred to another location, or working in a different city. Pietsch said this could also include events such as corporate layoffs, business closures, and loss of funding for projects and departments, as well as natural disasters like hurricanes, tornadoes, and floods. 2. Management issues: Anything that causes employees to feel uneasy or distrustful can lead to staff turnover, including company scandals. Matt Pietsch asked, “Is the CEO facing lawsuits?” Were there any fraudulent activities by the companies under investigation? No matter what problems arise, this will cause employees to lose trust in their company. ” 3. Work environment and job embeddedness: Research published in Harvard Business Review defines job embeddedness to determine the level of connections with other employees and those in technical communities. Pietsch asked, “Has a colleague left?” Are other departments laying off employees? Is it not encouraging employees to actively join larger communities? Is a company’s reputation getting worse? Are companies losing market share? Have managers failed to bring in new talent and new ideas to the workplace? Concerns of this kind can be potential indicators that employees may consider leaving their jobs. ” 4. Market demand: Market conditions are always a consistent factor affecting turnover; whether the economy is doing well or in a downturn, some employees may be more likely to leave than others, and this must be kept in mind. Matt Pietsch said, “For example, if I were a software engineer, I might leave work at 8 a.m., and by 8:15 a.m. another company would have offered me a new job.” However, if there is a shortage of job openings, departing employees may take 6 to 12 months to find new jobs, which will have a significant impact on the company’s turnover rate as well as the timing and manner in which employees leave. ” To predict competitors’ turnover, Pietsch says companies can also use predictive analytics to determine who is most likely to leave their competitors’ companies, with a view to recruiting such talent. By understanding the factors that affect employee turnover, it is possible to increase the likelihood of candidates responding positively to phone calls or emails from recruiters. To test this theory, Harvard Business Review and ENGAGE Talent developed a Turnover Intent Index (TPI) scoring system for over 500,000 workers across various industries in the United States. The Turnover Intent Index (TPI) is based on public data from employers, including ratings on Glassdoor or from analysts, changes in stock prices, news reports, as well as regulatory or legal actions. It also takes into account an individual’s work situation and background, such as past job positions, length of employment, tenure, skills, education level, gender, and geographical location. Then, the machine learning algorithm classifies each person as unlikely, less likely, more likely, or most likely to accept a new job opportunity. Brooks Holtom, a professor of management and senior associate dean at Georgetown University and one of the authors of the research report, along with David Allen, a professor of management and associate dean at Texas Christian University (TCU), said, “First, we wanted to see how well the Turnover Intent Index (TPI) predicts the level of transparency in job postings.” ” Subsequently, the researchers sent email invitations to 2,000 respondents to view available jobs that matched their specific skills and interests. Among nearly 75% of those who received the email, 161 opened the invitation email, and 40 clicked on it. Research shows that those rated as ’most likely’ to accept an invitation are more than twice as likely to open the email as those rated as ’least likely’ (5.0% vs. 2.4%). Furthermore, among those who opened the email invitation, those rated as ‘most likely’ to accept it were more likely to click on the email. ” The author concludes that this indicates that the Turnover Propensity Index (TPI) scores can identify employees with a higher risk of leaving, allowing companies to use publicly available data to strategically recruit top talents who may be more inclined to accept offers from outside organizations. Pietsch said, “This helps recruitment managers and recruiters understand who is more willing to receive calls or emails from recruiters.” ”Research shows that these people have a 60% higher chance of finding a new job within 90 days. Not only that, but before someone answers a headhunting call, it’s also possible to consider what measures need to be taken to retain them once they decide to take action. ” Job satisfaction: a key indicator worth quantifying. In terms of retaining or hiring employees, job satisfaction is crucial. Pietsch pointed out that there are five key elements that indicate job satisfaction: strong leadership, business stability, organizational resilience, growth opportunities, and a positive work environment. Matt Pietsch says that therefore, by identifying the personal and emotional factors that are most important to employees, it becomes possible for companies to determine what is most crucial for them, thereby helping to set goals or retain them. Pietsch said, “For example, if someone has a high score in terms of business stability and places a lot of emphasis on the resilience of a company, then they are likely to not want to work for a startup, as such companies are inherently unstable.” Therefore, strategies can be adjusted to focus on internal factors, in order to better attract and retain top talent within the company. ” Weisbeck said that the BLS surveys on job openings and labor turnover rates show that the unprecedented state of the labor market is making it increasingly difficult for companies to recruit enough talent to fill available positions. The number of vacant positions has remained stable over the past few months, as has the total number of jobs available for applicants. The number of people resigning and leaving their jobs is increasing, which means that many people are leaving their current positions to take on other jobs. Matt Pietsch said, “It is a competitive advantage for companies to actively understand the reasons why employees leave and to retain top performers.” Five years ago, we only saw large companies using data analysis in this way. Now, even smaller companies are adopting data analysis techniques. ”