This case is reserved for use by Columbia Business School Faculty only.
Could Jelena Le Breton, global vice president of consumer insights at fragrance manufacturer Takasago, use generative AI to reliably mimic human responses in large-scale surveys on consumer preference?
Could Jelena Le Breton, global vice president of consumer insights at fragrance manufacturer Takasago, use generative AI to reliably mimic human responses in large-scale surveys on consumer preference?
Takasago’s Consumer Insights and Market Research Team (CIMR), headed by Jelena Le Breton, studies consumer trends to provide detailed insights into customer reaction to the company’s new and existing fragrances. They frequently use market research surveys to collect data on consumer demographics, attitudes toward brands, and scent profile preferences. The process is costly and time intensive. In this case, students learn about AI-generated ‘silicon sampling’ and discuss how CIMR could use these tools to create a survey that accurately predicts human responses at scale. How should the company develop samples, and what factors should be considered when analyzing results?
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