Hiring Process

Organizations are only as good as their top performers. However, with high turnover rates, some companies lack the necessary tools to fulfill their talent acquisition needs. In January 2024, the U.S Bureau of Labor Statistics reported nearly 3,385 quits, with leisure and hospitality having the highest turnover rate and mining and logging having the lowest rate. And if that isn’t concerning, in 2021, over 47 million people quit their jobs in the United States. With rising concerns stemming from the 2021 surge of the "Great Resignation," a term coined by Anthony Klotz, more organizations are turning to recruitment automation.When HR recruitment needs help, they turn to technology. Data from Aptitude Research found that nearly 55% of companies use recruitment automation (Laurano, 2021). So, what are the major problems in the hiring process, and how can data analytics transform the HR strategic hiring process? Below is an exploration of how data analytics can assist organizations in enhancing the HR lifecycle, identifying problems and solutions, attracting new talent, and retaining existing employees.

‍Problems with traditional recruitment methods:

‍• Resume screening bias
• Hiring the right skills but wrong personality
• Ineffective resume screening
• Heavy burden on generalist recruiters
• Time-constraining interviewing and resume reviews  
• Psychometric testing

‍

Imran Syed, founder talks about the most counterproductive behaviors he’s observed in leaders is arriving at an organization with preconceived notions about its problem

There are several factors that influence an organization's success, no matter if you’re the CEO, HR. In Imran Syed’s blog he provides tips to a systematic approach to attain success.

1. Learn from multiple levels of an organization, not just those above you.

2. Ask questions about the organization's past, present, and future state.

3. Communicate your learning agenda with others and set the expectation that learning is your primary focus.

4. Consider how you can leverage external sources for learning, such as customers, or even past customers, which can provide invaluable insights.

‍

How data-driven insights is improving the hiring process

‍• Improving the quality of hires: Involves recognizing diverse communication styles and various factors.Firstly, it's crucial to acknowledge that a majority of interviews heavily rely on auditory information, which may not align with how some individuals process information (Villiers, 2024). Secondly, different roles may demand distinct dimensions of conceptual expression. For example, senior positions often require individuals who can grasp the bigger picture rather than focusing solely on details. To enhance hiring quality, companies should explore data-driven recruitment methods, offering a more objective decision-making approach. Traditional hiring approaches, such as interviews and cover letters, may inadvertently overlook promising candidates due to poor interview performance not tailored to their role or communication style. In contrast, data-driven recruitment includes various elements, instead of focusing on one assessment such as: skill assessments, personality testing, psychometric testing, and writing assignments, contributing effectively to identifying top talent. Instead of basing applicants on one interview, recruitment has a variety of assessments to consider.

‍

•Predicting speed of hire: With data-driven recruitment tools, and analytics it allows recruiters to estimate hiring timelines, and the amount of time it will take to fill the role.

‍

•Reducing hiring bias and promoting Diversity, Equity, Inclusion, and Belonging (DEIB): According to an eye-tracking movement study, HR recruiters only take about 7.4 seconds to look at resumes (Ladders,2018). While this study might be controversial, how can we be assured there isn't any bias? Such bias might include names, race/ethnicity, country of origin, type of school, and so on, which are all bias triggers that can influence whether a candidate is selected. Traditional Methods of hiring based on intuition, and experience are being replaced by more systematic approaches such as using artificial intelligence (AI) automation. This involves leveraging data to make informed, unbiased and intelligent hiring decisions. Utilizing data-driven recruitment promotes impartial candidate selection, minimizing bias and supporting Diversity, Equity,Inclusion, and Belonging (DEIB) initiatives. Incorporating work samples can streamline pre-employment assessments, leading to a more diverse workforce and improving business performance and decision-making.

•Enhancing candidate experience: Roughly 17% of organizations ask for candidate feedback during a multi-stage recurring process. Candidate experience is essential for top talent retention. Data-driven recruitment identifies key factors affecting candidate experiences, like communication timelines and interviewer interactions. Understanding these enables organizations to enhance candidate experience and retain skilled candidates.

Employee Retention

Employee retention poses a significant challenge in the workforce. Organizations must focus not only on satisfying employees but on retaining top talent. Gallup reports that 52% of employees said their organization could have done something to prevent them from quitting ( Pendell, 2021)). How can we ensure long-term employee retention? One approach is to examine the social exchange theory.

Social Exchange Theory

American sociologist George C. Homans in 1958 first coined the social exchange theory. Homans was a distinguished behavioral sociologist, and his social exchange theory is a concept in which two individuals undergo a process of "cost-benefit analysis(Emerson, 1976, as cited by Tulane University). In other words, it involves weighing the pros and cons that could be exchanged for a reward or a risk. To put this into perspective, the social exchange theory is applied in the workplace, where employees must assess whether to invest their efforts in a company and receive rewards that lead to dedicated long-term employment. Research conducted on the social exchange theory and the relationship between training and employee retention found that three factors—training and development, job satisfaction, and the working environment—significantly influence employee retention (Xuecheng, W., Iqbal, Q., & Saina, B., 2022).

‍

How data-driven insights is helping improve employee retention

•Better work-life balance through customized scheduling: AI tools allow companies to have a more flexible scheduling system. Self-scheduling allows employees to pick times and be approved by AI, freeing managers time who may have spent more time in the past reviewing requests. If some employees work better at night or prefer weekends for certain tasks, employees can schedule this easily instead of waiting for manager approval which can sometimes take days or weeks. Data can show us when employees are most likely to attend work, call in sick, or at their peak performance.

•Identifying growth opportunities: One of the top reasons people quit is because there is no support or growth in a job. AI can help measure employee engagement, and work performance. Additionally, it can remind managers and employees if a new goal is needed or if the employee has outgrown their current position or needs training.

•Workflow streamlining: To help improve the work environment, the use of AI-driven automation can help with repetitive tasks. Employees can focus their time on more meaningful tasks, which can enhance overall performance.

Enhance overall organizational performance

In any successful company, effective organizational performance management is essential. Improved performance correlates with increased profits. Even if your organization is thriving, there is always room for enhancement. Without implementing performance management, providing clear feedback becomes challenging. Employees remain unaware of expectations, performance metrics, or criteria for advancement. HR managers must grasp that their role involves focusing on the company's strategy and innovation, rather than micromanaging the team daily

Some of the common problems in organization performance is:

‍•Absence of clear direction

‍•Difficulty blending personalities in teams

‍•Failure to develop key competencies and behaviors

‍•Poor communication & feedback

‍•Lack of awareness

‍How data-driven insights Is helping improve organizational performance

‍• Data-driven decision-making: Employers can utilize data analytics to track and evaluate performances, including feedback, assessments, reports, and surveys, rather than relying on intuition or guesswork. According to Swan, the use of data from performance reviews provides valuable insights over time and information for future improvement(Swan, 2010, as cited by Nguen, & Thy, 2022). Feedback from performance assessments ensures communication on company standards and helps to boost employee morale for better performance. HR management can use strategies such as SMART (Specific, Measurable,Achievable, Relevant, and Time-bound) to monitor strengths, weaknesses, progress, and areas of improvement. Using data-driven insights, organizations can rely on factual information derived from data analysis. This data can inform decision-making processes such as determining the need for promotions and training. Furthermore, data insights can assist in identifying patterns and trends, enhancing decision-making with a greater likelihood of success.

‍• Data predictions & improvement: Data from HR tools can provide departments predictive insights which can help minimize consequences. Tracking data leads to measuring tasks, and goals. Teams can monitor metrics and track successful outcomes, failures and adjust course when needed.

‍Conclusion

Addressing HR challenges through advanced analytics and big data holds immense potential to optimize the hiring process, bolster employee retention, and elevate organizational performance. By harnessing recurrent tools, HR professionals can effectively mitigate bias, accurately measure performance, and ultimately minimize employee turnover. Embracing these innovative approaches not only streamlines operations but also fosters a more inclusive and productive work environment, ensuring sustainable success for organizations in the dynamic landscape of today's business world.

‍

References

[1]Laurano, M. (2021). AUTOMATION WITH HUMANITY: PUTTING THE CANDIDATE FIRST.AptitudeResearch.https://content.predictivehire.com/hubfs/Automation%20with%20Humanity%20%7C%20Aptitude%20Research.pdf

[2]Bureau of Labor Statistics. (March 6, 2024). Econic Ews Table 4. Quits levels and rates by industry and region,seasonally adjusted.https://www.bls.gov/news.release/jolts.t04.htm

[3]Villiers, A. (2024). Why an interview provides poor evidence of communication skill.

https://www.selectioncriteria.com.au/managers-selection-panels/interview-provides-poor-evidence-communication-skills

[4]U.S Bureau of Labour and Statistics (2023). Job Openings and Labor Turnover Survey:JOLTS 2023 BenchmarkArticle. https://www.bls.gov/jlt/joltsbmart2023.htm

[5]Pendell, R. (November 10, 2021). Gallup. 5 Ways Managers Can Stop Employee Turnover.

https://www.gallup.com/workplace/357104/ways-managers-stop-employee-turnover.aspx

[6]Tulane University (April 20, 2018). What Is Social Exchange Theory?https://socialwork.tulane.edu/blog/social-exchange-theory/ Emerson R. Social exchange theory. Ann Rev Sociol.1976;2:335-62. doi:10.1146/annurev.so.02.080176.002003

[7]Xuecheng, W., Iqbal, Q., & Saina, B. (2022). Factors Affecting Employee's Retention: Integration of SituationalLeadership With Social Exchange Theory. Frontiers in psychology, 13, 872105.https://doi.org/10.3389/fpsyg.2022.872105 [8]Ladders (2018). Eye Tracking Study.https://www.theladders.com/static/images/basicSite/pdfs/TheLadders-EyeTracking-StudyC2.pdf

[8]Vuong TDN, Nguyen LT. The Key Strategies for Measuring Employee Performance in Companies: A SystematicReview. Sustainability. 2022; 14(21):14017. https://doi.org/10.3390/su142114017

‍

[9] Ladders (2018). Eye Tracking Study. https://www.theladders.com/static/images/basicSite/pdfs/TheLadders-EyeTracking-StudyC2.pdf