Choosing a perfume can be overwhelming, with thousands of options and little guidance beyond vague descriptions or brief in-store samples. Blind purchases are risky, and even expert reviews may not account for personal taste or skin chemistry. A tool that systematically recommends fragrances based on proven preferences could simplify the process and help users discover new favorites.

How It Could Work

A website or app could let users input perfumes they already enjoy, then suggest similar fragrances based on shared scent profiles, notes, or trends in user behavior. For example, someone who likes Chanel No. 5 might receive recommendations for other floral-aldehydic perfumes. Over time, machine learning could refine suggestions by incorporating ratings and feedback. Users might also filter recommendations by note preferences (e.g., "no vanilla") or explore crowd-sourced insights like "people who like X also enjoy Y."

Stakeholders and Opportunities

Perfume enthusiasts would benefit from personalized recommendations, while niche brands could gain visibility among the right audiences. Revenue could come from affiliate partnerships with retailers, premium subscriptions for advanced features, or sponsored placements for brands. Unlike existing databases (e.g., Fragrantica) or sample services (e.g., Scentbird), this tool would proactively guide users toward full-sized purchases tailored to their tastes.

Getting Started

An MVP could begin with a basic recommendation engine using a manually curated database of fragrances grouped by notes. Users might input three favorite perfumes and receive a simple list of matches. Later phases could add accounts, note-based filtering, and machine-learning refinements. Key challenges—like sourcing accurate scent data or balancing subjectivity—could be addressed through partnerships with fragrance experts or crowd-sourced ratings.

This approach merges data-driven precision with the art of perfumery, offering a practical solution to a common frustration. By focusing on personalization, it could stand out in a market where most tools rely on generic rankings or trial-and-error sampling.