Case Study: a leading US merchandize retailer achieves $100 million in savings with Wipro's store labor optimization analytics

Wipro employs predictive analytics for Store labor optimization for a leading US retailer leading to enhanced customer experience and $100 million in estimated savings

The client, a leading US merchandize retailer, faced a challenge in optimizing store labor across its 2000+ locations. Their existing top-down, ad-hoc method for payroll allocation did not reflect the individual operational needs of each store, making it difficult to improve customer service and meet corporate sales goals. They enlisted Wipro to employ an analytics-based approach to find the optimal balance between staffing levels and revenue growth potential.

Wipro implemented a bottom-up predictive analytics framework that moved beyond using sales as the sole payroll driver. Their solution analyzed historical data for 10-15 key workload drivers specific to each store to precisely forecast and optimize labor hours. This resulted in an estimated $100 million in savings, a 2-4% yearly improvement in employee productivity, and nearly 50% more stores meeting their sales plans, all while enhancing the overall customer experience.