The difference between Arena and talent assessments
Talent assessments – also called pre-employment screening tests – have been around for over 100 years.
Inspired by Alfred Binet’s Intelligence Tests of the early 1900’s, psychologists developed the first multiple choice screening test to classify WWI army recruits. With a pictorial version for the 40% of draftees who were illiterate, and a written version for the rest, “Army Alpha” and “Army Beta” [50-minute tests] classified 1.75 million men over the course of the war. These tests determined assignments and training, recommending 135,000 to be commissioned officers and 8,000 to be dismissed.
Over the course of the 20th century, screening tests evolved. Most still take 40-50 minutes to complete and are focused on comparing question responses made by candidates to a theoretical model of competency or ‘fit.’ These assessments challenge job applicants with test-like questions designed to measure skills, knowledge, motivations, personality, or cognitive processes.
Arena, on the other hand, analyzes candidates in a context. Arena’s technology applies a range of machine learning techniques to thousands of data points collected directly from candidates as well as from data sources reflecting the labor market and the prospective employer.
One source of data from candidates is their interactions with a short (5-7 minute) questionnaire on Arena’s platform. This questionnaire, however, is not a criterion-based test with right/wrong answers aimed at revealing competence.
Relevant, publicly-available data sources — Glassdoor, Indeed, local news feeds, U.S. Bureau of Labor Statistics, et al — further contextualize the candidate.
Additional key differences include:
- Measurable impact: traditional assessments have been in use for quite some time, yet have never demonstrated any real impact at improving retention. Turnover continues to rise year-over-year, nationally. For all Arena clients, however, turnover rates measurably decrease.
- Real-Time Updates: Traditional assessments are static and don’t account for changing dynamics in the place of work and the current market. Arena uses real-time data feeds. Algorithms adjust as an employer makes changes in management or operations.
- Machine Learning: Our algorithms change constantly and improve due to our application of cutting edge machine learning tools. Not only do our algorithms automatically learn from new information, but our team of data scientists review and investigate algorithms and predictions in order to adjust data sources, re-calibrate algorithms, and improve outcomes.