The client, a large bank, wants to improve its recruitment process to attract better candidates and enable a better match of job roles to available profiles.
Key Challenges faced were -
- How to match available job roles with available profiles?
- Is there a way I can learn from what has worked well for my organization?
- How many to hire?
- When to hire?
- How will external and internal factors influence the hiring outlook?
The Approach followed was -
- The first step was to scour through the available internal and external data to assess the situation.
- The possible data components captured in the process were –
- Recruitment Data (Employee/candidate) – Job profile, Resume information (Education, Demographics, Experience), Recruitment Channel, Interview Results, Annual Assessments, Promotion/Lateral Movements, Tenure
- Recruitment Data (External) – Unemployment rate, Organization attractiveness, Industry attractiveness, Cost of living, Industry Pay levels
- Used statistical models like Logistic Regression, Decision Trees, ROI and Interactive Dashboards to arrive at the following solutions:
- Offer drop prediction
- New hire attrition
- Channel optimization (Vendor/Campus, Referral, Employment sites etc.)
- Demand forecasting / Skill based warm pipelining
- Successful employee profiling
Below were the improvement achieved -
- Increased organisational flexibility
- Reduced recruiting costs
- Improved candidate success and tenure
- Improved efficiency