Most people dread the idea of a life without sight or hearing but are not preoccupied by living life without a sense of smell.  We asked 1100 participants in 22 countries to choose between giving up - a sense (Hearing, Smell, Taste, or Vision) or a range of commodities (Pet, Hair, Left Little Toe, Cell Phone, 3 months' worth of salary, Social Media, Dream Vacation, Online Streaming, or Online Shopping). 
Our findings revealed three distinct groups of participants. The majority of individuals in our study were unwilling to give up their senses for any of the commodities we proposed. Another group of participants was willing to give up a sense in exchange for a commodity, with their sense of smell being the most common choice to sacrifice for their pets. Finally, a group of participants were indifferent to what they gave up, as they were equally likely to give up a sense or commodity.

Interestingly, these groups of individuals were not evenly distributed across the countries investigated. Countries such as Nigeria, Peru, and Italy had a majority of participants who were unwilling to give up their senses. In contrast, countries like the US, Brazil, and China had more participants who weighed each question carefully. Lastly, countries like Thailand, India, and Germany had a higher percentage of participants who were indifferent and equally likely to give up a sense or commodity.

Overall, our study showed that people value their senses more than any of the commodities proposed. Despite the COVID-19 pandemic's extensive coverage of chemical senses, the value of smell and taste remains underappreciated globally.
 

Making sense of consumer response to fragrance can be challenging due to the subjective and individualized nature of both smell perception and emotion response. Higher-order benefits (e.g., emotion) and other more abstract attributes can be difficult for consumers to explicitly record.  Consumer implicit association testing (IAT) provides an easier way for consumer to express these perceptions to fragrances and provide additional differentiation where traditional self-report shows parity. However, integrating and resolving implicit and explicit responses into actionable results and recommendations can be complex and challenging. In the current study, we explored how implicit response can help identify drivers of explicit liking using regression analyses (e.g., the role of authenticity in driving hedonics) and how the different perceptual attributes interact with one another. This multi-method approach to fragrance evaluation can target features that highlight or hinder brand harmony and ‘fit to concept’ in fragrance development. 

In this talk, we dive into the transformative role of machine learning (ML) in predictive modelling, specifically its applications in extracting actionable insights from historical data within the context of sensory and consumer science research. We aim to underscore the significant advantages ML-based predictive modelling brings to research and product development, such as accelerated time-to-market, enhanced decision-making, and the potential for more personalized consumer experiences. We will navigate through the challenges posed by predictive modelling, including data quality and availability, potential biases, and the risk of overfitting. Through practical use case scenarios, we will illustrate how these models can be adeptly applied to drive business value and enhance the R&D process.

Join panelists Valentina Parma, PhD and Jonas Yde Junge, PhD of Monell Chemical Senses Center, Tessa Moxley of Reckitt, Rachel Horn of HCD Research and Vanessa Rios de Souza, PhD and Bartosz Smulski of Aigora as they answer your questions and speak with Michelle further about the future of research and applications of emerging sciences.