Thousand of recruitms matched per month
Qualified recruits allocated monthly
Fit-to-position algorithms undergoing
- Find predictive criteria to assist in decision making process
- Innovative engaging admission process
With nearly thousands of new recruits each month, the main challenge tasked was to effectively allocate, train, and navigate the new recruits throughout the system to positions where they can best thrive and contribute according to their inherent strengths.
A creation of an effective people analytics database that maintains the unique characteristics of each team, position, and individual was built to fully identify the skills necessary for each role to match with each candidate. The database is built upon Assense’s unique behavioral data, as well as additional organizational data streams. For each candidate that was assessed, the Assense GO platform would generate a fit-to-position score to delegate candidates to the right position.
The customer’s workforce management became smarter and more data-driven. New insights on a position’s success factor helped to identify the most fitting talent for the most suitable candidate allocation, based on objective and fair measurements. While the team, positions, and individual data enables tailoring the training to improve performance and navigating ongoing personnel.
More Case Studies
Entry Level Professionals
Thousands of recent graduates are recruited annually for entry level positions.
Assessed more candidates per day
Rate the process the ultimate assessment
Sales and Service
A long recruiting process led a high number of candidates to shy away from several rounds of interviews.
Increased acceptance rate
Higher accuracy rate
Customized Professional Recruitment
Identifying future successful employees in a more streamlined and enjoyable process.
Cut external assessment costs
Saved time spent per candidate
Locate the predictive criteria of the current population through Assense, all while delivering top user experience.
New significant measurements identified
Of company’s scales successfully predicted