Gilad Saadoun

Understanding customer behavior using indoor location analysis and visualization

Understanding customer behavior in brick-and-mortar stores and other physical indoor venues is essential for any business aiming to provide a more personal and compelling shopping experience, optimize store layout, and improve store operations. Achieving these goals ultimately leads to improved user experience, conversion rates, and increased revenue. Todays mobile-based location technologies provide information about the users location that can be used in advanced analytics and visualizations. This means retailers and enterprises can gain insight into customer behavior patterns and understand, for example, how much time customers spend in different areas of the store, what routes they take, how well they are serviced, and more. In this paper, we present a solution approach for better understanding customer behavior based on mobile indoor location data as well as the technologies developed by IBM Research for realizing this solution. We describe significant challenges considering collection, curation, analysis, and visualization of indoor location-based data and illustrate the use of the approach for smarter commerce in a real-world use case.

Visual analytics for movement behavior in traffic and transportation

movement behavior in traffic and transportation P. Bak H. Ship A. Yaeli Y. Nardi E. Packer G. Saadoun J. Bnayahu L. Peterfreund Understanding movement of vehicles, people, goods, or any type of object is important for making knowledgeable decisions regarding public transportation planning. However, movement is a complex and dynamic phenomenon, and until recently, movement data was difficult to exploit for such planning purposes. The widespread adoption of location-aware devices such as Global Positioning System (GPS) sensors in public transportation systems and the adoption of open data principles have set the stage for new methods and tools for data collection and analysis of movement patterns. This paper illustrates the value and benefit of applying visual analytics techniques to movement data to create valuable insight for public transportation planning using vehicle-mounted devices on buses and trams. The contribution of the paper is three distinct visual analytics solutions that we developed using a real-world open data feed published by the Helsinki Public Transport Authority. The current work addresses encounters between objects, stops that interrupt movement, and flow dynamics of a large number of moving objects. We instantiated the described methods by showing that our findings can be applied in real-world use cases.