Retailers today face mounting pressure from digital-first competitors, fragmented data ecosystems, and lagging category insights that limit timely, informed decision-making. Traditional tools often fail to capture real-time market dynamics or predict competitive shifts. Predictive Market Share Analytics bridges this gap, integrating diverse data streams to deliver competitive intelligence, enabling faster decisions, optimized assortments, and measurable revenue growth across categories and regions.
Problem Statement
A leading US grocery and general retailer was struggling to maintain its category-based leadership amid rising competition from online-first players. Reliance on delayed, fragmented market share data led to limited visibility into performance gaps and competitor trends, preventing timely strategic action and hindering growth in key categories across regions and channels.
Impact
- Targeted strategies contributed to approximately $2.7B revenue impact in Q4’25
- Reporting accuracy increased from 65% to over 80%
- Merchandising teams made faster, data-driven category and regional decisions
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