pass_by Competitors & Alternatives

What’s the competitive advantage of pass_by?

Most companies trying to measure in-store consumer behavior are built on a single, shaky foundation: mobile location data. It’s a narrow, error-prone signal with serious blind spots, especially in dense urban areas, multi-level malls, and enclosed retail formats. The result is missed visits, misattributed traffic, and models that fall apart the moment they are tested in the real world. pass_by was built to solve this problem. We don’t rely on one data source, and we don’t treat foot traffic as the final metric. Instead, we combine multiple high-scale datasets, including mobility, spend, and demographic signals, to deliver a precise and comprehensive view of real-world customer behavior. That means we can measure store performance accurately, even in the most complex locations, and give you visibility far beyond just visit counts. With pass_by, you get answers others cannot provide: Who is visiting your store, not just how many Where they came from and how far they traveled What else they did before and after, including other stores, brands, or neighborhoods And most importantly, where they actually convert We track the full journey, from discovery to purchase. That means you see what drives value, not just volume. It’s not about counting visits. It’s about understanding customer intent, movement, and spend. That’s what drives better decisions in store operations, marketing, real estate, and beyond. While others make guesses based on limited and siloed data, pass_by gives you clear, consistent visibility into every step of the customer journey. Our data is accurate, explainable, and built for action.

What are the best alternatives to pass_by?

Gravy Analytics by Unacast, Factori, Echo Analytics, Huq Industries, Success.ai, DRAKO, Predik Data-driven, Veraset, VisitIQ™, and Versium