The IDDASS project, implemented in 2016 and 2017 in collaboration with the University of Geneva and CTI (now Innosuisse), developed an advanced software library for analyzing customer behavior during shopping sessions in physical stores. This technology combines accurate behavioral pattern recognition with online and offline data integration to create a comprehensive picture of the customer experience and provide personalized shopping incentives. The project, which was implemented as the second machine learning project by Aioma, demonstrates the successful application of indoor positioning and behavioral analysis to improve customer loyalty and decision-making in retail.
In 2016 and 2017, I had the opportunity to work as an economic partner on an important project with University of Geneva and the Commission for Technology and Innovation (CTI), today's Innosuisse. This project that has the name IDDASS (Interests Detection During a Shopping Session) represents the second machine learning project of my former company Aioma. In the following, I would like to present the main aspects and findings of this project, which should be of particular interest to entrepreneurs in the software industry.
IDDASS is a software library that analyses the behavior of users during a shopping session in a physical store. The innovation of the project lies in the high accuracy with which behavioral patterns are recorded and evaluated. The aim was to integrate IDDASS into a loyalty application that runs on smartphones. This application combines the collected data with existing e-commerce analysis tools, creating a connection between online and offline data. This enables a more comprehensive knowledge of the customer experience over the entire life cycle: The customer is accompanied from their online interaction to their physical shopping experience and rewarded with digital bonuses.
The project combined several advanced technologies:
Daniel visits Jelmoli's website for the second time in a week and looks at a pair of black shoes. Although he looks at the shoes online, he doesn't buy them as he always tries on new shoes first. During his trip to work, his loyalty app informs him about special offers at the nearest store. As soon as he enters the shop, the app automatically starts and shows him relevant articles. Daniel's behavior in the store, such as the areas he visits and the time he spends there, is recorded and analyzed. Based on this data, Daniel receives personalized offers and reminders that positively influence his purchase decision.
A first prototype was used in Origammi's offices in Zurich. This demo version demonstrated to interested parties how the technology works and the added value it offers. For example, a customer's movements in the store were recorded to improve the shopping experience.
This project was a collaboration between TaM and Origammi, with Origammi integrating the research findings into their ECHOO product. ECHOO increases customer loyalty and provides shop owners with high-quality data. IDDASS as the last component fits perfectly into this overall picture and ensures seamless integration of online and offline data.
The project resulted in several scientific publications, including:
These publications underline the scientific and technological significance of the project.
The IDDASS project shows how a deep understanding of customer behavior can be achieved by combining machine learning, indoor positioning and data integration. For entrepreneurs in the software industry, this project provides valuable insights into the possibilities of behavioral analysis and the development of innovative solutions that both strengthen customer loyalty and provide valuable data for business decisions.
The IDDASS project was not only a technological success, but also an example of fruitful cooperation between science and industry. Entrepreneurs in the software industry can benefit significantly from such partnerships by gaining access to the latest research and technologies and turning them into marketable products.
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