The careful placement of focus items at eye level, the bulk displays of specials at the end of the aisle where they're inevitably rounded trolley-by-trolley.
They're the kinds of 'layout' ploys that have been at the heart of retail marketing since supermarkets were invented.
However the latest developments in artificial intelligence and in-store camera technology are about to take the game to a whole new level.
A research team led by Queensland University of Technology has harnessed the advances to identify a ground breaking store design framework to "better service" customer behaviour and maximise sales.
The idea employs computer vision techniques to zero in on key areas of shoppers' faces like the corners of their eyebrows, tips of their noses and edges of their mouths to create 'emotion recognition algorithms'.
"CCTV offers insights into how shoppers travel through the store, the route they take and sections where they spend more time," said QUT's Dr Kien Nguyen.
"But this research proposes drilling down further, noting that people express emotion through observable facial expressions such as raising an eyebrow, eyes opening or smiling."
While it's generally known customers enjoy browsing unfamiliar brands and buy more from upper shelves, Dr Nguyen says deeper understanding of their behaviour is the ultimate goal for business intelligence.
"Obvious actions like picking up products, putting products into the trolley and returning products back to the shelf have attracted great interest from smart retailers," he said.
"Other behaviours like staring at a product and reading the box of a product are a gold mine for marketing to understand the interest of customers in a product."
The researchers say heatmap analytics, human trajectory tracking and customer action recognition can be assessed from video, allowing more careful evaluation of things like traffic flow or the popularity of displays placed in different areas.
"Stores like Woolworths and Coles already routinely use AI empowered algorithms to better serve customer interests and wants, and to provide personalised recommendations," said fellow researcher Professor Clinton Fookes.
"This is particularly true at the point-of-sale system and through loyalty programs.
"This is simply another example of using AI to provide better data-driven store layouts and design, and to better understand customer behaviour in physical spaces."
Dr Nguyen said with privacy a key, data could be de-identified by examining customers at an aggregate level.
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