'Aura Vision' to visualize shop user information through video analysis and make use of it in marketing

Information such as who is a man is a man or a woman, what kind of life you are living in, how much income it is in the first place ... If you get it, it will be useful for marketing It is very hard to actually take a questionnaire for each person. Startup · " Aura Vision " developed by Aura Vision Labs is a technique that automatically identifies user information based on images of security cameras installed in buildings and facilities.

Aura Vision - Unique in-store visitor insights
https://auravision.ai/

ECS entrepreneurs secure £ 100k seed investment for retail customer tracking system | Electronics and Computer Science | University of Southampton
https://www.ecs.soton.ac.uk/news/5579

"Aura Vision" created by Aura Vision Labs is a technology that can analyze the camera image using computer vision and biometric identification technology and distinguish the person who is reflected.

In retail stores, we introduce our own point cards, etc., aggregate information on what kind of customers are using and what kind of shopping content they are using, but in "Aura Vision" such a string It is possible to obtain information on the distribution of customers' sex and age without attachment. In addition, without touching personal information, you can conduct marketing such as improving conversion rate, meeting opportunity loss, targeting to specific customer segments.

Although it is possible to obtain necessary data even in the form of using point cards etc. if it is biased in the purchase of goods, "Aura Vision" uses camera images, thanks to the use of camera images, the "movement of people in the store" is visualized Information can be used to improve the flow line from the entrance to the sales floor, for example.

An example is like this. This is a flow line of "female customers".

And this is a flow line showing "male customers". The left side is the inside of the shop, the right side is the entrance, there are many movements that cross the bridge, but the man knows that there are many movements to go to the upper side.

It seems like this is the flow line of all customers. Based on such information, it is possible to design an optimal store.