Most analytics stops at describing what happened. How many visits did we get? Where did they come from? Which centres grew, which fell? Useful, but incomplete. It tells you what did happen , not what should have happened . If you don’t know what a centre should  be doing given its catchment, competition and format, you can’t answer basic questions: Is this asset genuinely strong, or just average for its position? Is that suburb “difficult”, or are we just under-performing it?

Why We Use Mobile Signal Data Mobile signal data has been around for more than a decade, and it promises something retail property has always wanted: a consistent way to understand how people move, where they spend time, and how places compete. But in many solutions, the outputs don’t pass the pub test. You’ll often see impressive visualisations and very precise-sounding numbers that fall apart the moment a property professional asks the obvious follow-up: “Does this reflect

Stop Paying Monopoly Prices for Blunt Retail Analytics Retail property analytics hasn’t kept up with the market it’s meant to serve. If you’re an industry professional you probably know the current options: Premium banking-data products with genuinely strong signal, but heavy price tags and usage restrictions making it out -of-reach or unavailable for many. “Trade area” analysis where a consultant draws a neat shape on a map and calls it insight. Cheap data feeds and self-ser