Leaders: Veronica Zixi Jiang and Songting Dong
A growing proportion of social interactions and shopping activities are now mediated by digital services and devices. Such digitally mediated behaviours can be recorded and analysed, fuelling new services such as personalized search engines, recommendation systems, and targeted online marketing. A demand to make these personalized services more persuasive and effective is emerging in both academic research and industries.
The effectiveness and persuasiveness of personalised communication are significantly higher when they are tailored to an individual’s unique psychological characteristics. However, the challenge prevails as to the ability to predict an individual’s psychological characteristics on a large scale. We propose to address this challenge by utilizing publicly available social media data, such as an individual’s Facebook profile.
Participating Organization: Aglo Retail Intelligence
Aglo is a retail intelligence company that gathers in-store data for brands, using photos taken by crowdsourced shoppers, representatives, and merchandisers. The in-store data are extracted from photos via image recognition using crowdsourcing and artificial intelligence (AI).
It is important for Aglo to keep a network of active shoppers, representatives, and merchandisers and motivate them to take photos in retail stores via the Aglo mobile application. This sandbox:
- Aims to understand Algo users’ psychological profiles and finds ways to motivate them with personalized communication;
- Aims to identify the relative effectiveness among different psychological characteristics, such as big 5 personality traits versus information processing styles;
- Aims to investigate how to maintain a network of consumer workers.