This is a strategy and analytics approach that helps large consumer packaged goods (CPG) companies use their data and AI as a new kind of ‘economies of scale’—not just buying more shelf space or running bigger TV campaigns, but spotting profit opportunities and efficiency gains across brands, markets, and channels using advanced analytics and generative AI.
Global CPGs struggle to grow margins and market share in a fragmented, low-growth environment despite massive scale, data, and marketing spend. This approach reframes data and AI as a core scale advantage—systematically uncovering growth pockets, pricing and promo efficiencies, and operational improvements across markets and categories, rather than relying on siloed, manual analysis and one‑off initiatives.
Defensibility comes from proprietary CPG data (sell‑in, sell‑out, loyalty, panel, media, supply chain), embedded decision workflows, and accumulated insight patterns that competitors cannot easily replicate even if they use similar AI tools.
Hybrid
Vector Search
Medium (Integration logic)
Data integration and data quality across many countries, channels, and legacy systems; plus LLM cost/latency for large-scale what-if and scenario analysis.
Early Majority
Positioned specifically around using AI to create ‘efficiency of scale’ for global CPGs—integrating growth analytics, promotion/pricing intelligence, and generative decision support into a unified operating model, rather than selling generic AI or BI tooling.