Global FMCG Data | Price Sensitivity Index | Real-Time Elasticity Curves for Staple Goods Across LATAM Cities | 10+ Consumer and Demographic KPIs

This data provides a deep view into how consumers in Latin America respond to price changes in staple goods, revealing the real-time elasticity curves that drive brand loyalty, switching behavior, and competitive positioning. It sits within a broader platform of global consumer insights, giving users the ability to see not only regional dynamics but also how these same patterns compare across markets around the world. What is offered here for a selection of LATAM cities is a window into a scalable data product that can extend to hundreds of cities, dozens of categories, and multiple demographic segments. At its core, this data focuses on three foundational measures. The first is the base price of staple goods within a given city, offering a reference point for local market conditions and cost structures. This acts as the anchor for any analysis of consumer reaction. The second is the switch threshold, the percentage increase in price that prompts most consumers to change brands. This represents the precise tipping point where loyalty collapses and alternative products are adopted. The third is the elasticity index, a statistical measure that captures how sensitive demand is to changes in price. Together, these measures create a detailed elasticity curve for each product, city, and demographic profile. The coverage of this data includes major metropolitan hubs in Latin America such as São Paulo, Rio de Janeiro, Mexico City, Guadalajara, Buenos Aires, Lima, Bogotá, Medellín, Santiago, and Quito. By concentrating on these cities, it captures diverse economic contexts, consumer behaviors, and retail landscapes, from rapidly growing emerging markets to mature urban economies. This geographic breadth ensures that the data speaks to both regional differentiation and broader continental trends. Demographic layering adds another level of richness. Each entry is connected to age groups ranging from Gen Z to Boomers, income brackets from low to high, and household types that include singles, couples, families with children, and multi-generational arrangements. This makes it possible to analyze not just how a city reacts overall, but how specific consumer segments within that city diverge in their behaviors. For example, a price change in a staple such as milk may drive rapid switching among low-income families but produce little reaction among affluent singles. The structure of the data makes these nuances visible and measurable. The value of this data lies in what it enables. For brand managers, it highlights the fragility or resilience of loyalty within specific product categories. For retailers, it shows how far prices can be pushed before consumers defect to competitors. For financial analysts and investors, it illustrates demand-side risk factors and the competitive intensity of markets. For policy makers, it provides evidence of how vulnerable different populations are to price shocks in essential goods. Because the data is rooted in zero-party input—directly reported by consumers—it avoids the distortions of secondary modeling and instead provides a straight line to actual decision-making behavior. Importantly, this LATAM-specific view is only a slice of what can be unlocked at full scale. The larger platform is designed to deliver elasticity data globally, across multiple continents and product categories. A user could explore detergent switching thresholds in Lagos, compare them to rice price sensitivities in Delhi, and then benchmark both against consumer behavior in Chicago or Berlin. The structure is consistent, which allows for clean cross-market comparisons and easy aggregation. This scalability is crucial for multinational brands and retailers who need both local depth and global visibility. The insights teased by this data are powerful. They show that loyalty is not fixed but conditional, tied closely to thresholds that can be identified and quantified. They reveal that these thresholds differ not only by product and geography but also by consumer profile, making segmentation strategies far more effective. They demonstrate that some categories are inherently more elastic than others, requiring different pricing strategies. And they make it possible to move from reactive pricing moves to proactive scenario planning, where a brand knows in advance how consumers will likely respond to a given adjustment. From an operational perspective, this data integrates smoothly into forecasting models, marketing dashboards, and pricing tools. It enriches financial planning by quantifying downside risk from price increases. It informs promotional design by clarifying the discount levels that will actually change consumer behavior. It supports supply chain decisions by showing where shifts in demand are most likely to occur under different pricing scenarios. And it strengthens brand strategy by identifying the conditions under which loyalty can be maintained or lost. This is not data that sits in isolation. Within the platform, it connects to other complementary measures such as consumer trust rankings, sustainability premiums, and AI-influenced purchasing. Together, these modules provide a holistic view of how consumers are navigating markets and making tradeoffs. The price sensitivity data in particular offers the economic backbone of that picture, grounding strategic choices in the hard reality of how much consumers are willing to pay before walking away. As the global economy continues to face volatility in costs, inflationary pressure, and supply disruptions, having real-time visibility into consumer price elasticity becomes mission critical. Brands cannot afford to guess where the breaking point is. Retailers cannot risk setting prices that alienate entire segments of their customer base. Investors cannot make decisions without understanding the demand fragility of the categories they are exposed to. This data equips all of these stakeholders with the clarity they need. The Latin American view provided here illustrates what is possible when zero-party data is collected, structured, and scaled. It provides the clarity to identify thresholds, the granularity to see demographic differences, and the comparability to benchmark across cities and regions. Most importantly, it positions users to extend their analysis beyond a single market into a global framework where patterns can be mapped, compared, and acted upon. This data is not about telling a single story of price sensitivity in one city or one category. It is about giving decision makers the ability to uncover the many stories consumers are already telling through their reactions to price changes. It is about empowering businesses with the foresight to anticipate those reactions before they happen. And it is about enabling strategies that are resilient in a world where consumer tolerance is always shifting, sometimes subtly and sometimes dramatically.