$30M in ROI. The case for real-world context at scale.

The challenge

Managing inventory for a national retail pharmacy group is incredibly complex. It requires tracking millions of SKUs across thousands of stores, with demand constantly shifting based on promotions, seasonality, local behavior, and real-world events.

The group’s traditional forecasting models struggled to explain more than 60% of demand variability, particularly for localized spikes tied to external events. These blind spots led to missed sales, suboptimal inventory positioning, and limited confidence in forecast outputs.

Any solution introduced needed to operate reliably at extreme scale, integrate directly into production forecasting workflows, and earn trust from teams responsible for inventory and operational performance nationwide.

The approach

PredictHQ was integrated directly into their demand forecasting stack to provide trusted real-world context at scale.

This net new intelligence is consistently processed against 40-50 million store-SKU combinations per week, enabling models to explain demand patterns that were previously unexplainable.

The system automatically learned relationships between events and product demand, dynamically grouping impacted stores and SKUs based on shared event sensitivity. This allowed forecasting teams to understand not just that demand would change, but why.

The integration was expanded nationally and embedded into ongoing planning workflows, supporting consistent, high-confidence use across the organization.

The impact

• $30M+ in quantified ROI driven by forecast accuracy improvements

• $0.02 uplift per SKU across ~150,000 SKUs

• 40–50M store-SKU combinations processed weekly in production

• Nationwide rollout supporting consistent, repeatable usage

• One of PredictHQ’s highest-engagement enterprise customers with sustained API usage

Why it worked

Trust was earned through performance at scale.

PredictHQ operated directly inside production forecasting workflows, processing tens of millions of decisions weekly with consistent accuracy and explainability. The intelligence was reliable enough for teams to depend on it for inventory planning, not just analysis.

This level of adoption reflected confidence not only in the data, but in the platform’s ability to operate continuously, transparently, and at enterprise scale.