An AI-powered attrition prediction model transformed a US workforce management firm’s HR practices, delivering real-time insights into employee retention, engagement, and risk factors by analyzing 20+ data points and providing predictive attrition indicators through dynamic dashboards.
A client in the HR consulting space relied on annual reviews as their only insight into workforce trends—valuable but infrequent, leaving them blind to issues until much later. They lacked real-time visibility into key metrics like retention, satisfaction, and attrition. The absence of dynamic HR data made it difficult to act proactively on emerging trends or intervene before problems escalated.
We developed a mobile-friendly AI-powered HR analytics platform designed for continuous insights. The system uses multi-modal machine learning to monitor over 20 data points including employee surveys, performance reviews, salary benchmarking, commute times, and more, on a biweekly or monthly basis.
It generates dynamic categories like “On the Move,” “Good Standing,” and “Needs Attention” to help managers act strategically. The platform also features the “MAGIC” scoring system—measuring Meaning, Autonomy, Growth, Impact, and Connection—to evaluate employee sentiment and engagement.
All data is centralized into dashboards offering retention rates, satisfaction scores, fire/hire trends, and predictive attrition indicators.
HR insights evolved from reactive to real-time and actionable. Managers gained continuous awareness of team “HR Health,” identifying rising stars and employees at risk of leaving, even flagging underlying causes like pay parity or commuting issues. The engagement-driven analytics empowered more effective, data-informed decisions and facilitated timely interventions, well beyond what annual reviews could provide.
