If an e-commerce website was a physical store, it would look vastly different to each shopper, containing only shoes in their size or items in the right color. To compete with this deeper personalization, the savviest brick-and-mortars look to mold every aspect of the in-store shopping experience — from lighting to music to smell — to give their core customers every reason to return.
Yet, most physical retailers fail to achieve this heightened level of personalization because they remain unaware of who is coming into their locations and when. It’s crucial, however, for retailers to obtain this data. Clearer insights on in-store customer behavior can help make the experience enticing enough to pull them away from the convenience of Amazon.com. Computer vision holds the key.
Technology like vision artificial intelligence will be essential for the survival of physical retail, opening up new opportunities to create experiences that will encourage customers to continue shopping in person.
Deeper, Anonymized Data Collection
Vision AI software can be deployed across a physical store’s entire network of IP cameras to gather and aggregate insightful demographic data.
Unlike the use of facial recognition for theft prevention or Bluetooth beacons to detect in-store traffic, vision AI for analytics dispenses of the need for any kind of matching database and doesn’t connect an individual in-store to their account or phone data.
Instead, the technology is trained to identify an anonymized human form in the store, distinguishing between different subjects by comparing hundreds of vector points on their faces. At no time does it store any individual’s identity.
Through its person detection capabilities, vision AI can provide insights into how many unique shoppers are visiting the store, at what times, how long they’re staying, and what sections they’re shopping in. It can also be trained to detect whether a shopper is making purchases and even analyze facial expressions. All of this data can be cross-tabulated with gender and age determinations.
Transforming the Retail Experience
New insights from vision AI will help retailers make small changes that create subtly different store experiences at different times of the day, maximizing the impact of their physical space.
With data about which kinds of customers enter the store at certain times of day, a store operator could determine that women aged 50 and over are most likely to shop in the early afternoon and then play music most appealing to this subset during this time.
Alternatively, understanding that men flock to the skin care area of the store and linger for a longer time than other demographics could help store managers reorganize the products in this area to highlight new men’s skincare offerings.
Meanwhile, by aggregating facial expression data from all customers walking away from a customer service desk, a store operator could better understand if shoppers consistently have a positive experience with service staff.
Finally, even simple patron traffic pattern analyses can help retailers reorganize the physical layout of their stores. This will create better flow and capitalize on the huge opportunity of in-store media to place ads where they’re likely to get the most attention.
Maintaining Loyal Customers
In a recent KPMG report, retail leaders predicted AI will have its biggest impact on the industry in the form of customer intelligence over the next two years. Vision AI will be the tool to provide it.
Forward-looking retailers that install vision AI into their already existing network of cameras will be able to pull back the curtain on deeper behaviors and desires of their core shoppers, tailoring their stores to remain exciting shopping destinations for years to come.
Terry Schulenburg is a Vice President at CyberLink, a pioneer in AI and facial recognition technologies.
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