AI-powered consumer insights for food retail
About this service
In short- AI-driven consumer insights
- Behavioural science integration
- Enhanced customer engagement
- Personalised meal suggestions
- Future-ready retail solutions
Wageningen University & Research develops advanced AI methodologies to understand and respond to consumer behaviour in food retail. These insights form the foundation for tailored communication strategies that support trust, engagement, and sustainable choices.
Our approach
Our AI-driven research goes beyond conventional market analysis. Using advanced machine learning techniques, we design systems that adapt communication about food products according to the COM-B model (Capability, Opportunity, Motivation, Behaviour). This ensures that recommendations account for individual barriers, socio-economic contexts, and learning stages, resulting in truly personalised guidance rather than generic advice. For food retail, this means engaging customers in ways that reflect their specific motivations and challenges, fostering informed decision-making and long-term behavioural change.
One example is the use of conversational survey tools powered by Large Language Models, which generate personalised follow-up questions and uncover deeper insights into consumer expectations around transparency.
The integration of conversational AI and behavioural science creates a powerful framework for both understanding and influencing consumer preferences. These tools are designed for retailers, brands, and service providers seeking to strengthen customer engagement, trust, and loyalty in a rapidly evolving market where transparency and personalisation are key drivers of competitive advantage.
Example of our work
Our AI tools can provide consumers with tailor-made suggestions for cooking and (healthy/sustainable) eating. If analysis shows that a consumer segment is highly motivated by sustainability but has limited capability in plant-based cooking, the AI generates personalised content with simple recipe tutorials rather than environmental messaging. Conversely, for skilled cooks with little awareness of sustainability, the system highlights the environmental benefits of their food choices.
A working parent with limited time might receive quick meal-prep suggestions featuring legumes, while someone concerned about protein adequacy could receive nutritional comparisons showing how plant proteins meet daily requirements. The system also adapts timing: for example, sending meal-planning tips at weekends when consumers have more time to shop and prepare.
Looking ahead, we are exploring how consumers would interact with a cooking-help robot. Unlike simple voice assistants, AI-powered kitchen assistants could actively shape choices through conversational interaction. This represents the next step beyond screen-based recommendations, towards integrated household systems that observe, suggest, and guide cooking in real time (while of course respecting user autonomy).
Get in touch with our expert
Do you have a question about AI-powered consumer insights or opportunities to work with us? Please get in touch.
J (Jos) van den Puttelaar, MSc
Researcher in consumer behaviour
