Case Study: Optimising Recruitment Metrics for a Manufacturing Company in India Using Data-Driven Metrics

Company: A mid-sized manufacturing company in India with around 1,200 employees faced challenges in hiring for roles such as mechanical engineers, production supervisors, and quality control specialists. The recruitment process was slow, expensive, and led to poor long-term retention. To tackle these issues, the company implemented Recruitment Metrics & Reporting to make data-driven decisions.

Challenges:

1. Time-to-Fill: The average time-to-fill key roles was 60 days, affecting production schedules and leading to operational delays.
2. High Cost-per-Hire: The company was spending ?80,000 per hire, driven by extensive use of third-party agencies and job portals.
3. Quality of Hire Issues: High first-year attrition rate (25%) indicated poor candidate-job fit and onboarding inefficiencies.
4. Diversity Gaps: The company struggled to hire women and underrepresented groups in technical roles, particularly in engineering and manufacturing.

Strategy & Metrics Implementation:

1. Time-to-Fill Optimisation

• Metrics Used: Average Time-to-Fill, Time in Each Stage (sourcing, screening, interviewing).
• Action: The company found delays in interview scheduling and internal approval processes. They automated interview scheduling using ATS tools and reduced back-and-forth communication by establishing predefined timelines for managers to respond.
• Outcome: Time-to-Fill was reduced from 60 days to 45 days, a 25% improvement, leading to faster hiring and minimising disruption to operations.

2. Reducing Cost-per-Hire

• Metrics Used: Cost-per-Hire, Sourcing Channel Effectiveness.
• Action: The company analyzed data from different sourcing channels and realised that job boards (e.g., Naukri, Monster) were less effective compared to employee referrals. They shifted focus to an internal employee referral program with incentives.
• Outcome: Cost-per-Hire dropped from ?80,000 to ?50,000—a 37.5% reduction. Employee referrals produced higher-quality hires and cut down on agency fees.

3. Improving Quality of Hire

• Metrics Used: Quality of Hire, First-Year Retention Rate.
• Action: The company implemented a structured interview process to better assess both technical skills and cultural fit. They introduced detailed pre-employment assessments tailored to manufacturing-specific competencies.
• Outcome: First-year retention increased from 75% to 85%, improving employee tenure and reducing the need for replacement hires.

4. Boosting Diversity Hiring

• Metrics Used: Diversity Metrics (gender, caste, underrepresented groups).
• Action: The company partnered with technical colleges that promote education for women in STEM fields and initiated a diversity outreach program with local organizations. They also participated in industry job fairs focused on women and minority groups.
• Outcome: The percentage of women and underrepresented candidates hired in technical roles increased by 15% in the first year.

Key Results (as per Indian standards):

1. Time-to-Fill reduced by 25%: From 60 days to 45 days, resulting in timely recruitment for critical roles.
2. Cost-per-Hire decreased by 37.5%: From ?80,000 to ?50,000 per hire, achieved through optimisation of sourcing channels and employee referrals.
3. First-Year Retention improved by 10%: Structured assessments and cultural fit interviews helped increase retention rates.
4. Diversity Hiring improved by 15%: Focused recruitment efforts through outreach programs and partnerships helped bring in more women and minority candidates.

Takeaway for Indian Companies:

By using Recruitment Metrics & Reporting, the manufacturing company was able to significantly improve its recruitment outcomes. The insights provided by these metrics helped the company reduce hiring time, cut costs, and improve diversity and retention, all while aligning with operational needs. This case highlights the importance of data-driven recruitment in achieving long-term business success in the Indian job market.