Job Applicant Activity Analysis (A/B Test, Metric Design)

Description

This project focused on optimizing the hiring process by analyzing applicant behavior and improving conversion rates. Using A/B testing, funnel analysis, and SQL-driven attribution modeling, I identified key inefficiencies in the applicant journey and provided data-driven recommendations to enhance recruitment strategies.

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Project Highlights:

• Designed & Executed an A/B Testing Framework – Evaluated the applicant conversion funnel to identify inefficiencies that led to drop-offs in the hiring process

• Optimized Channel Spend & Acquisition – Used SQL-driven attribution analysis to measure the impact of social media, referrals, and job search platforms on applicant conversion rates

• Defined & Tracked Key Hiring Metrics – Established and monitored critical recruitment KPIs such as Conversion Rate (CVR), Cost Per Acquisition (CPA), and Time-to-Hire to assess process efficiency.

• Improved Hiring Conversion Rates by 12% – Applied funnel and segmentation analysis to detect applicant drop-off points, leading to process optimizations that increased applicant-to-hire conversions.

• Implemented Statistical Testing for Decision-Making – Used Z-score statistical testing to validate A/B test results and ensure data-driven hiring strategy improvements.

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Final Outcome:

• 12% Increase in Applicant-to-Hire Conversions – Optimized the hiring funnel by eliminating drop-off inefficiencies.

• Data-Driven Recruitment Strategies – Provided actionable insights to optimize job postings, outreach strategies, and candidate engagement.

• Improved Hiring Process Efficiency – Implemented attribution modeling to optimize marketing spend on job search platforms and referrals.

• Scalable Hiring Analytics Framework – Developed a data-driven approach to continuously monitor and improve the recruitment funnel.

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This project showcases how A/B testing, data analytics, and funnel optimization can streamline hiring processes, reduce costs, and improve applicant conversion rates in talent acquisition and HR analytics.

Result

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Skills

- Data analysis & BI Tools: SQL, Python (Pandas, NumPy), Tableau, Excel

- Experimentation & Testing: A/B Testing, Z-Scores Statistical Testing

- Recruitment Metrics & Optimization: Conversion Rate (CVR), Cost Per Acquisition (CPA), Funnel Analysis

- Segmentation & Attribution Analysis: Candidate Source Effectiveness, Drop-Off Rate Analysis