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A leading global retailer with a diverse presence across hypermarkets, online commerce, and grocery delivery sought to optimize its customer service operations and ensure cost-effective, high-performance service delivery.
Despite advanced systems and data availability, the client faced:
• Low Forecast Accuracy
The client relied on just one month of historical data and top-down aggregation to forecast multi-channel volumes. This led to unreliable predictions, with accuracy dropping to 54%, service levels declining to 57%, and abandonment rates rising to 11%.
• Chronic Overstaffing
Inaccurate weekly forecasts led to persistent overstaffing of up to 39%, inflating labor costs without improving performance. Workforce underutilization also created engagement and morale issues.
• Service Inconsistency
Lack of visibility into demand fluctuations across individual channels especially during promotions or peak retail periods meant the client couldn’t respond dynamically, leading to inconsistent customer experiences.
The client was grappling with poor forecast accuracy, chronic overstaffing, and declining service quality across its customer service channels. These inefficiencies impacted both operational costs and customer satisfaction. Lumina Datamatics delivered a data-driven WFM solution that enhanced forecast precision, incorporated external demand drivers, and realigned staffing in real time. The result: improved service levels, reduced abandonment rates, and a 15% cut in WFM operational costs transforming workforce planning into a strategic asset.
To address inefficiencies in its customer service operations, the client aimed to implement a more granular and statistically sound forecasting process tailored to each service channel. The focus was on improving weekly forecast accuracy, optimizing headcount to prevent overstaffing during peak retail periods, and ensuring resources were better aligned with actual demand patterns. The client also intended to enhance service levels, reduce abandonment rates, and build a flexible WFM model that could scale with the demands of a multi-channel retail environment influenced by frequent promotions, seasonal events, and shifting customer behaviors.
