Retail: Transformed Seasonal Hiring with AI-Powered Recruitment

HR teams spent 80+ hours weekly manually reviewing 10,000+ applications, causing them to miss qualified night-shift warehouse workers, resulting in 15% unfilled roles during peak December sales.

60% of seasonal staff quit by New Year’s, often because full-time students were scheduled for morning shifts or warehouse workers were placed as cashiers, costing $500K in rehiring and lost productivity.

Some store managers prioritized past retail experience while others valued 'friendliness,' creating wild service quality swings, causing a lot of back-and-forth and conflicted feedback.

Slow approvals delayed offer rollouts, leaving stores understaffed during peak season and losing sales due to long checkout queues.

Delayed role-fit assessments led to mismatched hires, increasing shadowing time and distracting managers from sales and inventory during peak retail periods.

Automatically filtered 10,000+ applications in 48 hours, flagging top candidates for night shifts/bilingual roles using keywords and availability matching.

AI aligned candidates’ preferred hours/locations with store needs, reducing mismatches and cutting turnover from 60% to 25% post-holidays.

With quiz responses automatically assessed by AI, the recruiting team slashed the screening time by 50% and contacted the scoring candidates directly.

Automated preboarding with instant offer letters, real-time acceptance tracking, and digital document uploads, clearing 95% of candidates for approval within hours, not days.

LeoHire’s role-specific assessments and AI-generated screening filtered the right candidates, minimizing mismatch, reducing onboarding prep, and saving managers valuable time.