Japanese Researcher Wins Award for Applying Data Mining Tech to Analyze Big Data in Business Marketing
OSAKA, Japan, Oct. 14, 2019 — Kansai University’s Katsutoshi Yada has been awarded the 2019 Prize for Science and Technology (Promotion Category) in the Commendation for Science and Technology by Japan’s Minister of Education, Culture, Sports, Science and Technology (MEXT) for applying and promoting data mining technology to analyze big-data in business marketing. The annual prize recognizes research, development, and promotion of science and technology to the general public.
Katsutoshi Yada is one of Japan’s leaders in applying data mining technology in the business sector for product development, marketing, and sales. Working with colleagues he developed the ‘MUSASHI’ software platform for large-scale data pre-processing and by releasing it as an open source resource, free of charge, he made major contributions to the advancement big data in business that led to reduced costs of data storage and analysis, and increased efficiency in marketing products.
“I became intrigued by data mining during my graduate student school days in Kobe,” says Yada. “This was the time of the Great Hanshin-Awaji Earthquake in 1995. I worked in a retail company— including at the cashier—in the daytime and graduate school in the evenings.”
Yada saw at first hand the structure of information systems and realized that huge amounts customer data was completely untouched. He studied computers and programming with the view of applying data mining to business. “My knowledge generated consultancy work and projects with store managers and companies. This was the beginning of the development of data mining technology for pre-processing information for business applications in Japan.”
Development of ‘MUSASHI’ the innovative data mining technology for pre-processing business information
Yada collaborated with colleagues to develop mining technology, initially with the release of ‘MUSASHI’—software platform for large-scale data pre-processing— and the development of the application service provider (ASP) system and marketing applications for large-scale data analysis.
Notably, as 90% of data mining is ‘pre-processing’, sorting and clean processing of data before data processing requires enormous work. The outstanding feature of ‘MUSASHI’ is that it’s pre-processing is low cost and highly efficiency. Yada and colleagues not only created ‘MUSASHI’ for data pre-processing, but also released it as open source resource, free of charge, thereby advancing the use of big data in business.
Ongoing projects and the future
The important point about Yada’s approach is that he analyses real-life situations, as is underscored by his projects.
(1) Real-field data using ‘eye-tracking’ and sensors to monitor the movement of shopping carts.
Analysis of 100 customers as they move through a supermarket with emphasis on their eye-movements during decision making stages at product shelves and checkout registers. By collecting “customer flow data” using IC tags attached to shopping carts and in-store wireless LAN and incorporating them into buyer behavior analysis this information will be used to guide customers through stores.
(2) Life support businesses: Delivery trucks are lifelines for the elderly
Japan is one of the most rapidly aging societies in the world. So it is important to understand and cater for the needs of the elderly. Yada notes that, the internet is not widely used by elderly for shopping in Japan, especially by those living in rural locations. He has been studying the delivery of goods, such as household goods and food, by ‘trucks’ in aging communities in Japan. “The trucks are a life-line for elderly people in the less populated regions of Japan,” says Yada. “The drivers offer invaluable conversation with their elderly customers and gain insights into the buying habits of their customers, their health and so on.”
Yada predicts that in Japan, new business models for sales and marketing products will be base on ‘life-support businesses’. He is working with local authorities on trials for these ideas.
Papers and conferences
1. X. Zhong; K. Ishibashi;K. Yada, An Empirical Study of the Relationship Among Self-Control, Price Promotions and Consumer Purchase Behavior, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 1863-1868, (2018).
2. Y. Zuo, K. Yada, T. Li and P. Chen, “Application of Network Analysis Techniques for Customer In-store Behavior in Supermarket”, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018, Miyazaki, Japan), pp. 1861-1866, 2018.
3. K. Yada, Y. Sun and B. Wu, “The Short-Term Impact of an Item-Based Loyalty Program”, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018, Miyazaki, Japan), pp. 1846-1851, 2018.
4. Y. Hamuro, N. Katoh and K. Yada, “MUSASHI: Flexible and Efficient Data Preprocessing Tool for KDD based on XML”, 2002 IEEE 2nd International Conference on Data Mining Workshops (ICDM 2002, Maebashi, Japan), pp. 38-49, 2002.
K. Yada (ed), “Data Mining for Service, Studies in Big Data”, Vol. 3, pp. 1-291, Springer-Verlag, 2014.
Y. Ohsawa and K. Yada (eds), “Data Mining for Design and Marketing”, pp. 1-319, Chapman & Hall / CRC Press, 2009.
Source: Kansai University