AI powered real-time video recognition solution for retail providing accurate in-store traffic flow analysis, sales performance, and product recognition.
Sensei is an AI-powered, real-time video recognition solution that helps retailers understand the in-store activity. Sensei leverages streams from existing store cameras to provide analysis on traffic, customer flow, and product interaction as well as to detect empty shelves.
At Sensei we developed algorithms that turn any installed camera into a powerful sensor, which allows it to digitize brick and mortar stores, digitize its products (SKUs) and measure real-time customer experience. Sensei provides retailers with accurate in-store analytics and insights ranging from traffic flow, store benchmarking, products’ performance, out-of-shelf detection and allows for optimized operations and drive more sales.
Despite e-commerce growth, brick and mortar stores still generate 89% of the total volume of sales in Europe. Furthermore, sales conversion is on average 20% for fashion stores, and on-shelf availability of products in supermarkets can be as low as 85%. Both these issues represent lost sales opportunities leading to an overall of 3.3 trillion dollars lost annually due to these inefficiencies.
To date, unlike online stores, there is no systematic way of tracking which products elicit interest from References/Customers but are not converted (lost sales) and to automatically digitize a store space, and its products. The current state-of-the-art technologies for tracking can only determine (inaccurately) how many people enter and leave the store and do not provide any information of the client's engagement in store, which creates a significant blind spot leading to over/understaffing stores, stocking the wrong inventory and missing sales opportunities.