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Using Data-Driven Approaches to Overcome Recruitment Bottleneck

· Data Science


Introduction

Recruiting is a critical issue facing many companies today, and McKinsey, BCG, and Bain are three of the leading consulting firms that have conducted extensive research on this topic and provided insights on how companies can improve recruitment practices. These articles offer a wealth of insights into the challenges of recruitment analysis and provide evidence-based strategies for improving recruitment practices. While each article offers unique insights and perspectives, they all share a common goal of helping companies attract and retain top talent and succeed in today's competitive business environment.

McKinsey's article "Recruiting in the age of AI" (Chopra-McGowan, De Smet, & Weiss, 2018) suggests that AI can help companies improve the efficiency and effectiveness of recruitment, but that it is important to balance the use of technology with human judgment and a focus on the candidate experience. Similarly, BCG's article "The Digital Future of Talent Acquisition" (Strack & Bhalla, 2018) recommends that companies use data analytics to improve the efficiency and effectiveness of recruitment, while also creating a seamless candidate experience and a culture of innovation.

Bain's report "Recruiting the Right Talent" (Bain Insights, n.d.) emphasizes the importance of being proactive and strategic in recruitment practices. The report suggests that companies need to use data and analytics to identify the best candidates, create a strong employer brand, and use technology to streamline the recruitment process. Similarly, McKinsey's "How to recruit and retain millennial talent" (Hancock, Schaninger, & Weiss, n.d.) and Bain's "Attracting and Retaining the Right Talent" (Zook & Allen, n.d.) both emphasize the importance of creating a culture of continuous learning, providing opportunities for career growth, and offering competitive compensation and benefits.

The approach taken by the HR recruitment team at the airline company can be seen in the context of these broader trends in the literature. By using econometric machine learning to identify a data-driven rule for solving a key bottleneck in the recruitment process, the team demonstrated how companies can use data analytics and technology to improve the efficiency and effectiveness of recruitment. The team's approach also reflects the emphasis on creating a seamless candidate experience and a culture of innovation that is found in many of these articles.

The research conducted by McKinsey, BCG, and Bain provides a useful framework for understanding the challenges and opportunities of recruitment analysis. Companies can use these insights to improve their recruitment practices and attract and retain top talent in today's competitive business environment.

Background

The airline company in question is a large, established company that operates in a highly competitive market. To maintain its position in the market and continue to grow, it needs to recruit around 400-500 ground crew members per year. The recruitment process is lengthy and expensive, taking between 5-10 months and costing between $3000-$15000 on average for each new hire.

The HR recruitment department has recognized the need to improve the quality of the new hires to reduce employee turnover and improve the overall performance of the company. They are reviewing their current recruitment processes and methods to identify areas for improvement.

The cost of a bad hire for the company is significant, with 60%-80% of employee turnover attributed to poor hiring decisions. This turnover can have a negative impact on team output, with 39% of employers reporting that a bad hire can affect the productivity of the team. Conversely, a good hire can boost the employer's brand and improve future hiring prospects, with 83% of employers reporting this as a benefit of successful hires. Additionally, a bad hire can result in financial losses of up to $25,000 for the company, as reported by 41% of employers.

Given the importance of the ground crew to the successful operation of the airline company, it is crucial to ensure that the recruitment process is optimized to attract and hire the highest quality candidates. By conducting a recruitment analysis, the HR recruitment department hopes to identify the areas for improvement in their current recruitment process and make changes that will lead to better hiring decisions and ultimately improve the overall performance of the company.

Possible Approaches

The HR recruitment team at the airline company explored various approaches to improve their recruitment analysis process. The team sought to optimize their approach to recruit better ground crew and reduce the negative impacts of bad hires. Two primary approaches were identified and studied: identifying signals of good hires and identifying bottlenecks in the recruitment process.

The first approach focused on identifying the signals of a good hire. This approach aimed to help recruiters spot better talents in the hiring process. Despite extensive analysis, the team was unable to find conclusive evidence to identify better ground crew using this approach. However, the team did discover new data requirements that could be used to develop the future data roadmap for the future generation to use when returning to analysis with the first approach should data harvesting be completed. The team found that the approach was not futile, as it provided new insight into future research direction.

The second approach focused on identifying bottlenecks in the recruitment process. This approach aimed to help recruiters improve the throughput efficiency of their recruiting cycles. Through a data-driven analysis, the team was able to find statistically significant data that provided conclusive evidence to help recruiters identify one key recruiting bottleneck of the recruiting process. This finding can help recruiters focus their attention on specific areas to improve the recruitment process and ultimately lead to better hiring decisions.

While the team was unable to find conclusive evidence to identify better ground crew through the first approach, the research led to important discoveries that will shape future research. The second approach provided concrete data-driven evidence that can be used to make targeted improvements to the recruitment process. By continuing to explore different approaches, the team hopes to develop a comprehensive recruitment analysis process that will result in the hiring of high-quality ground crew and improve the overall performance of the company.

Iterated Approach

The HR recruitment team at the airline company explored various approaches to improve their recruitment analysis process. The team sought to optimize their approach to recruit better ground crew and reduce the negative impacts of bad hires. Two primary approaches were identified and studied: identifying signals of good hires and identifying bottlenecks in the recruitment process.

In the second approach, the team worked with the recruiting team to tease out the 7 stages to complete a recruiting cycle, including requisition, job posting, shortlisting, interviewing, selecting, offering, and onboarding. Upon reviewing the data against data sufficiency condition and process, the team identified a key bottleneck at the offering stage. At this stage, the recruiter makes an offer to the candidate of first choice and awaits the response of the candidate to accept the offer, make a counter offer, or reject the offer.

The dilemma here is that the recruiter can choose to play aggressive to actively follow up with the target candidate or play passive to await the candidate's response. The former approach presents high risks for losing candidates, and the recruiters have to go back to the first stage of recruiting, resulting in an opportunity cost for the recruiter to go to the second candidate. The latter approach can result in a lost cause for the recruiter to go back to stage 1 again, presenting high risks of being slow and losing candidates. In the age of talent war, the target candidate can be snapped up by competitors.

As a result, the analytics team used econometric machine learning to identify a data-driven practice for solving this bottleneck. That is, the recruiter can decide to follow up with the target candidate by observing the expected number of days that the candidate will respond based on the grade of the job. If the target candidate is taking a job grade at level 3, then the recruiter doesn't have to follow up after 3 days. If the target candidate is taking a job grade at level 6, then the recruiter will have to follow up within 17 days.

Business Impact

The potential economic impact of this data-driven rule is expected to save at least 800 working hours of the recruiters, at least $30,000 of man-hours per year, and at least $60 to $300 per job recruiting cycle. The recruiting team can use this new data-driven rule to decide whether to follow up with the target candidate across all jobs worldwide, and this rule can eventually be built into their recruitment management system and applicant tracking system, augmenting their recruiting intelligence, increasing the employment brand, and saving the company money in a tangible fashion.

Disclaimer

It is important to note that the quantified numbers have been masked to protect the identity of the company. However, the findings and recommendations are based on rigorous analysis and research and can be applied to other companies facing similar recruitment challenges.

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