Patrick Tu, the CEO of Dayta AI, tells us how retailers can analyze surveillance camera data and how they tackle privacy concerns.
Patrick Tu is the co-founder and CEO of Hong Kong-based startup, Dayta AI. The company’s retail analysis system, Cyclops, helps shops increase sales by recording and analyzing customers’ movements in a store. Its service has been adopted by nearly 1,000 shops in Asia and Europe, including shopping malls, fashion chains, restaurants, and supermarkets.
What’s special about Dayta AI’s services?
Retail analytics is not something new, but why hasn’t it gotten popular? One problem we pinpointed is that this type of products require[s] you to install new hardware, be it new sensors, new cameras or new servers. For most small and medium enterprises or retailers, having new installations brings tremendous trouble. One of the biggest features of Cyclops is we can plug and play with their existing surveillance cameras. We can feed their camera streams into our cloud for analysis. We also cover the consultation part, providing recommendations and insights.
What kind of shopping behaviors are being tracked by Dayta’s systems?
We can analyze what the cameras have captured; for example, how many people pass by your shop. Knowing that helps you understand how good the shop position is. We can also understand the number of people coming in, which would get us the conversion or walk-in rate. It shows whether or not your display has been effective in bringing people into the shop. We can do A/B testing on which display works better.
Inside the shop, we can see which pathways the customers take, whether they come in and turn left or turn right, and which area or product category they linger at. We also conduct age and gender analyses to find out about the target audiences.
Could you give an example of retailers using these data to improve their sales?
For a high-end fashion brand, we evaluate product performances and provide suggestions on how they could adjust the displays every two weeks, so they could move popular products to near the entrance or higher-traffic areas. At a fast-food chain, we track how long the queues are in real time. Once they pass a threshold, we would alert the shop managers, who would send more workers to help.
We also serve a food mart. Their total transaction values are dominated by females. But we found out that men spent four times as much as women in each transaction, because they tended to buy non-necessities such as frozen meat and seafood. After knowing this, the company included more male-driven customer relationship programs. They can make more money if they attract more male customers.
How would you address shoppers’ concerns that their movements are being recorded and analyzed without their consent?
Hong Kong’s Personal Data (Privacy) Ordinance requires companies to obtain consent from customers to process personally identifiable information, such as names, phone numbers, ID numbers or emails. We do not collect these data. Our data is stored in an aggregate instead of an individual way. For example, we know 15 people are coming into this zone, but we cannot identify who these 15 people are. We don’t store any video or capture any faces. Shoppers would not leave any images in our database. We cannot use it to trace it back to anybody.
Will the kind of personalization we see in e-commerce reach offline shops?
Maybe not. You may already be giving your consent when you click into a website. But for offline shops, you really need to fill in a form. It depends a lot on the regulations of a certain country and customers’ acceptance. Now we are seeing a trend of tightening [regulations]. So we don’t foresee a future like that.
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