Case Study: Bloomingdale's achieves $3M+ in one-year claims savings with Axonify

Bloomingdale’s uses microlearning, big data, and machine learning to prove training saved $3 million in 1 year

Bloomingdale’s faced a common executive dilemma: leaders knew training mattered but couldn’t prove its direct impact on business outcomes or justify increased budgets. Traditional methods like posters, classroom sessions, and legacy LMS tools produced little measurable change, leaving asset-protection leaders unsure where to focus resources for continuous improvement.

By adopting Axonify’s microlearning platform and its new Axonify Impact attribution engine, Bloomingdale’s trained 10,000 employees, cut safety claims by 41% (saving more than $2M annually) and—within a trial—attributed 36% of general liability and 24% of worker’s comp reductions to training, equating to $3,008,160 saved in one year. The real-time data and recommendations now let leaders tie training to turnover, inventory shortage and NPS outcomes and prioritize high-impact interventions.

Bloomingdale's

Chad McIntosh

Vice President of Loss Prevention and Risk Management