KAWASAKI and TOKYO―Toshiba Digital Solutions Corporation (TDSL) and DATAFLUCT, Inc. (DATAFLUCT) today announced a machine learning solution that optimizes the prediction of the number of visitors to a store. The solution is possible through the combination of TDSL’s GridDB Cloud*1, a cloud data platform capable of delivering real-time analysis and DATAFLUCT's DATAFLUCT cloud terminal., which enables utilization of advanced machine learning without the need for an expert. This solution makes it possible to quickly and easily predict the number of visitors to a store, which is difficult to realize without technical expertise and a complex data infrastructure. The two companies began collaborating in May 2020 through Toshiba OPEN INNOVATION PROGRAM 2020*2,3 organized by Toshiba Corporation with the aim of creating new businesses. This collaboration results in a solution that will be brought to the market and sold by DATAFLUCT.
Until now, most of the store visitors’ predictions are based on human intuition and experience. However, as we enter an era where intuition and experience become less relevant, it is difficult to respond to major changes in social conditions, consumer behavior, as well as the different characteristics of a store such as its surrounding environment and customer base. One of the solutions to this problem is data utilization. By using data to predict the number of visitors and customer segments for a store, accurate decisions can be made in regards to the amount of merchandise ordered, the timing of displays, etc., to maximize sales. However, if the accumulated data is inadequately analyzed, the structure of the data is disjointed and unusable for analysis, or if there is a shortage of data scientists, it becomes impossible to create a highly accurate prediction model. In order to solve these problems, GridDB Cloud and DATAFLUCT cloud terminal. are brought together to create a highly accurate prediction model using in-house and external data. This makes it possible to quickly and easily predict the number of visitors to a store and analyze the purchasing behavior of consumers.