We explore the role of machine learning, multi-source models and big data in market research and consumer understanding. We applied machine learning algorithms to two billion data points, and explored the relationships between aromas, motivations and emotions in the context of the personal care category. Six motivations (power, discovery, creativity, coping, pleasure and belonging) and four emotional mind-sets (action, reflection, contrast and prospective) were identified. Our analyses suggest that aromas can be associated with specific life drivers (motivations), as well as with mental states. Olfactory experiences can enhance the ability of a product and/or brand to capture attention, increase purchase intent and develop experiences that appeal to specific lifestyles and moods. Understanding the relationships between aromas, motivations and emotions can aid in the translations of sensory features and experiences into concepts, messages, ads and stories.