Turning Predictive Analytics Into Actionable Retail Strategies

Predictive analytics is reshaping decision-making in retail and e-commerce. However, unlocking its full potential requires more than large datasets—it demands clarity of purpose. The key lies in defining the right questions before diving into data and technology.

“At the heart of analytics is asking the right question. Without it, analytics becomes a directionless tool,” says Omar Capur, Senior Director of Ecommerce, Walmart. He notes that for many businesses, especially smaller ones, having complete data is often an exception rather than the rule.

Daniel Colunga, General Director, Uber Eats Mexico, agrees and stresses the importance of focusing on what can be controlled. “You can’t change the outcome, but you can influence it by adjusting the inputs. Our experiments must be rigorous, with tolerances of just 1 or 2%,” explains Colunga. On a platform managing millions of transactions, even a 0.10% shift can mean significant financial impact, making accuracy critical.

Colunga also points out a significant challenge in Mexico: “Not all data is digitized. Complementing available data with fieldwork is essential.” For Uber Eats, predictive analytics is deeply integrated into operations, from optimizing delivery times to real-time adjustments for weather and traffic conditions.

Data alone is not enough, though. “Feeding data into a system is not the solution—you need the right data and the infrastructure to interpret it effectively. AI and predictive analytics are now foundational in market research,” says David Hernández, Retail Analytics Director for Latin America, NielsenIQ.

However, the integration of these tools demands a shift in how success is measured. “Why are we still using outdated KPIs to evaluate companies? We need to question if these metrics truly drive operational improvements,” Hernández argues.

Patrick Lassauzet, Head of Corporate Communications, SHEIN Mexico, highlights the importance of understanding consumer behavior to guide strategy. “Mexican consumers embrace omnichannel shopping. They browse in-store, buy online, and return in-store. Creating a seamless experience is crucial,” he says.

This omnichannel model generates vast amounts of data, but it only becomes actionable when aligned with clear goals. “Using advanced tools without a defined purpose is like owning a Ferrari but never driving it,” Colunga remarks.

Capur emphasizes the role of practicality alongside technology. “Sometimes, analytics gives you the answer you want to hear. Simplifying data management and applying common sense is just as important,” he says. Capur also points out that many companies still view e-commerce as secondary to traditional operations. “E-commerce isn’t the side business anymore—it is the main business,” he states.

Predictive analytics, ultimately, does not offer definitive answers. Instead, it delivers refined hypotheses. “Analytics does not replace intuition or common sense. But when paired with clear objectives, it provides tremendous value,” concludes Capur.