Selected examples of how we turn data into pricing, strategy, and growth decisions for global businesses.
Global consumer goods pricing in large, unequal markets
ChallengePricing is usually set from business metrics — margins, competitive benchmarks, category norms — without reflecting what people can actually afford. In big, economically diverse markets, national averages hide large regional gaps in purchasing power, leaving products mispriced and under-penetrated.
ApproachWe codified affordability into a simple, inflation- and currency-proof heuristic — the “Minutes Worked to Afford” model (single-use formats price best at ~5 minutes of work, larger multi-serve packs at ~15). We added a subnational lens for high-inequality markets (India, South Africa, Brazil, Mexico), segmenting each country into economic clusters so price-pack architecture matched local affordability.
ResultsSales data confirmed purchase frequency clustered right at these thresholds. The lens exposed clear mispricing — an entry product in South Africa required ~20 minutes of work, 4× the ideal — and revealed regions that could sustain dual-tier pricing (5-minute affordability in lower-income areas, 2× pricing or larger packs in higher-income ones) to grow revenue without sacrificing volume.
A leading global food retailer — delivery pricing across 5,000+ stores
ChallengeA one-size-fits-all delivery discount — free or half-price everywhere, all the time — was eroding margins regardless of demand, competition, or cost to serve, while unpredictable spikes in discounted orders strained logistics.
ApproachWe built a data-driven dynamic pricing model, estimating 20,000+ delivery price elasticities at the store, daypart, and day-of-week level. AI-accelerated processing cut data prep from a week to 1–2 days, and a custom optimization engine let the retailer flex toward maximum savings or maximum traffic.
ResultsDeployed across 5,000+ stores, turning an unsustainable cost center into an optimized revenue lever.
Global strategy function of a US-founded multinational CPG company operating in 50+ markets
ChallengeThe client aimed to double international business by 2030 — but global strategy teams have no direct authority over largely autonomous local markets and can only influence, not compel. With 50+ markets at very different maturity levels, a single global playbook wouldn’t work.
ApproachWe built the Cluster Growth Model, an AI-based approach that segments markets by macroeconomic, cultural, and market dynamics so recommendations stay locally relevant while pooling insight across similar markets. We quantified the growth levers in each cluster, benchmarked every market against the 90th percentile of its peers, and socialized results with local teams over two collaborative rounds to earn buy-in.
ResultsA quantified, market-by-market roadmap with implied CAGRs and a clear path to the 2030 goal — aligning autonomous local markets around shared growth actions and building lasting internal capability.