In today’s competitive talent market, teams that learn from experience excel in securing top talent. While they may not be able to control all the variables, understanding important influences on hiring outcomes makes them more successful in the future. Yet, many organizations overlook a critical piece of the puzzle: understanding why candidates leave the hiring process. Learning from candidate attrition is critical to improving your hiring process and achieving superior hiring outcomes.
On the surface candidate attrition, the number of candidates who fall out of the hiring funnel (through rejection or drop-off), seems simple. However, many organizations overlook the potential of learning from candidate attrition. This leaves them with a significant blindspot – why people leave their meticulously planned hiring process. By analyzing attrition rates and understanding the true reasons behind them, talent acquisition teams can significantly improve their recruitment strategies and overall hiring efficiency.
This blog post dives into the challenges of learning from candidate attrition and equips Talent Acquisition Operations teams with actionable recommendations to overcome them. By leveraging the insights gleaned from this data, you can optimize your recruitment process, attract top talent, and ultimately secure a competitive edge.
Untangling the data maze
Despite its importance, capturing and analyzing candidate attrition data can be a complex endeavor. Not only do many teams face technical limitations, but many leaders also neglect to consider the motivations of their teams. Here are some common roadblocks:
- Definitions: Unclear definitions of drop-offs and rejections can lead to inconsistent collection.
- Standardization: Without a uniform language for attrition reasons, valuable insights can be obscured by ambiguous terms.
- Data Maturity: Organizations might lack the infrastructure to capture attrition reasons beyond a simple “Hired” or “Not Hired” status.
- Data Structure: Inconsistent data collection practices across teams can lead to messy data that is difficult to analyze.
- Training: Recruiters and hiring team members may execute the hiring process differently.
- Motivational Factors: Recruiters might be hesitant to report drop-offs due to concerns about performance metrics.
Capturing the right information
Once your organization is aligned on how to identify and classify attrition, you must consider your collection processes. Similar to all data collection in TA, it relies on a combination of technical capabilities and human-executed processes. Here are some key considerations:
- Data Capture Location: Candidates may apply to more than one of your jobs at more than one time, so it’s ideal to capture attrition reasons at the requisition-candidate level for the most specific insights.
- Uniform Language: Establish a consistent set of terms for attrition reasons to ensure data integrity and facilitate analysis.
- Distinguishing Rejection vs. Drop-Off: Implement clear procedures for identifying the source of candidate departure.
- Reason Selection: Use a dropdown to simplify (and limit) selection to pre-approved options.
- Other “Write-In”: If possible remove any “write-in” opportunities to reduce noise,. If you’re concerned about missing critical information, ensure Other can only be selected after considering the standard reasons.
Pro Tip: Offer an incentive for accurate data entry (e.g., a recognition program) to address motivational concerns.
Beyond a simple “no”
Once you have a robust data collection system in place, you should consider adding more specific reasons to these categories. For example, you might differentiate between underqualified and overqualified candidates, or between compensation and benefits issues.
Remember: Analyze drop-off rates alongside rejection rates. A high rejection rate may indicate candidate pool quality issues, while a high drop-off rate might indicate underlying process issues that need attention.
Standard reasons
- Rejection: Reasons may include:
- Not Qualified
- Compensation Mismatch
- Employment Eligibility
- Drop-Off: Self-elimination can be driven by scenarios like:
- Location Preferences
- Compensation Mismatch salary expectations
- Other Opportunities.
More detailed reasons
This level of categorization provides a deeper understanding of specific issues within your recruitment process or employer branding.
Example:
- Rejection:
- Not Qualified: Underqualified or Over-qualified
- Compensation Mismatch: Salary or BenefitsEmployment Eligibility: Immigration or Background Check Issues
- Drop-Off:
- Location Preferences: Doesn’t Want to Relocate or Lack of Remote/Hybrid Options
- Compensation Mismatch: Salary or Benefits
- Other Opportunities: Hired at Another Company or Chose to Stay at Current Company
Organizational reasons
It’s equally important to capture reasons unrelated to candidate viability, such as roles being put on hold or duplicate requisitions. This helps differentiate these closures from rejections and drop-offs.
Administrative reasons
It is essential to separate administrative reasons for closing out a candidate (e.g., role closure, duplicate requisition) from actual rejections and dropoffs to maintain data accuracy. Administrative reasons represent a completely different reason why a candidate may have left the hiring funnel and should be held out of any attrition analysis.
The ideal candidate attrition capture setup
Putting it all together can ensure that you are primed to collect consistent and actionable attrition information. For a minute, let’s step into the shoes of a recruiter who has candidate attrition to document. If we have followed the advice above, their experience will be a straightforward and efficient 4-step process that unlocks learning from candidate attrition.
- Candidate Outcome: A dropdown menu with options for Hired and Not Hired
- Attrition Reason (for Not Hired): A dropdown menu with options for Rejection, Drop-Off, and Admin.
- Attrition Category: A dropdown menu with specific reasons categorized within each (e.g., “Compensation” under Rejection)
- Additional Notes: An optional text box for capturing context specific to each candidate interaction.
Investing in training your team will empower them to document actionable reasons. Consider a situation where a candidate is not a good fit for the role. Today you may have a variety of ways to document this. By helping your team understand what constitutes good or bad attrition data, you can ensure your learnings from candidate attrition will be maximized.
- Bad: “Interviewed the candidate for this role and was rejected” (uninformative)
- Good: “Rejected – Not Qualified” (clear and actionable)
- Bad: “Not aligned with [Company] DNA” (jargon)
- Good: “Rejected – Culture/Role Fit” (specific and actionable)
- Bad: “Candidate Withdrew – not interested in hours, location, salary/benefits” (too many reasons)
- Good: “Dropoff – Location,” “Dropoff – Salary,”
Perform by learning from candidate attrition
By discerningly using data to make informed, impactful decisions, you can elevate your hiring process to be consistently fair, efficient, and performant. If you find that many candidates are removing themselves from your hiring process due to compensation reasons, consider conducting market research to ensure you are offering market-rate compensation. If you find that most of your rejections are due to candidates being unqualified, consider adding more clarity to your job posts to ensure you are attracting candidates with the experience you are looking for.
As the labor market and world of work continue to evolve, embracing data-driven TA will be essential for organizations looking to attract and retain the talent they need. And learning from candidate attrition can be a powerful contributor to your data-driven hiring future.
While tackling the challenges associated with candidate attrition data can feel daunting, the potential rewards are substantial. By learning why candidates leave, you can identify and address pain points that might otherwise go unnoticed. This could mean improving job posts, streamlining a lengthy application process, or ensuring a more positive equitable experience. Each improvement translates into a more attractive recruitment funnel, one that retains qualified candidates and fosters a positive employer brand, while also showcasing the potential of your data-driven hiring future.
Ultimately, learning from candidate attrition empowers you to make data-driven decisions that optimize your recruitment strategy. It’s the difference between casting a wide net and hoping for the best versus strategically attracting and engaging the talent your organization needs to succeed. In today’s competitive landscape, where top talent has options, learning from candidate attrition isn’t just a good idea – it’s a strategic imperative. To make this a reality, consider embracing a comprehensive yet accessible reporting platform like Datapeople Insights.
Bonus Tip: Leverage your learning from candidate attrition to create targeted outreach campaigns! Reach out to candidates who dropped out due to specific reasons (e.g., compensation) to see if you can address their concerns and bring them back into the fold.