Powering the fragrance discovery journey
Choosing a fragrance online is inherently complex. Preferences are sensory, emotional, and often difficult to articulate. Most e-commerce catalogs classify perfumes only by brand, gender, or price — leaving customers confused, uncertain, and more likely to abandon or return a purchase. WikiParfum transforms this complexity into a clear, guided and personalized discovery experience. Built on a curated database of 31,000+ perfumes and 1,500+ ingredients — classified by fragrance experts in partnership with Fragrances of the World — it provides the olfactive intelligence that brands and retailers need to help their customers discover, understand, and choose fragrances with confidence.What WikiParfum enables
- Expert olfactive classification — every fragrance classified by family, subfamily, ingredients, and intensity
- Intuitive fragrance exploration without requiring prior knowledge
- Enriched product experiences with visual ingredient breakdowns, perfumer profiles, and olfactive data
- Personalized recommendations grounded in real olfactive affinities, not popularity
- Persistent preference understanding across sessions, channels, and CRM
The business impact
WikiParfum addresses the core barriers to online fragrance purchase:| Challenge | How WikiParfum helps |
|---|---|
| Customers don’t know what a perfume smells like | Visual ingredient breakdowns and olfactive profiles make each fragrance understandable |
| Catalogs are poorly classified and confusing | Expert olfactive classification standardizes and enriches every product |
| Generic recommendations don’t drive trust | Olfactive-affinity-based recommendations feel authentic and accurate |
| High return rates on fragrance purchases | Better understanding before purchase increases confidence and reduces returns |
| No insight into customer scent preferences | Olfactive preference data enables targeted CRM and lifecycle campaigns |
What WikiParfum is — and is not
WikiParfum is not a static perfume database or a generic recommendation rules engine.It is a fragrance-native intelligence layer, designed specifically for how people explore, evaluate and choose perfumes.
Technical foundations
All API interactions are performed through GraphQL.- Endpoint:
https://api.wikiparfum.com/graphql - Authentication:
Authorizationheader with API key - Execution model: Server-side only
- Multi-language: All content available in multiple languages via the
langparameter
The fragrance discovery journey
A typical integration powered by WikiParfum follows these steps:| Step | Description | Guide |
|---|---|---|
| 1. Connect | Map your product catalog to WikiParfum via EAN codes | Catalog Integration |
| 2. Initialize | Establish a user session for personalization and analytics | Sessions & User Identity |
| 3. Classify | Enrich your catalog with expert olfactive classification | Olfactive Classification |
| 4. Explore | Let users discover fragrances by name, ingredient, family, or guided questionnaire | Fragrance Exploration |
| 5. Enrich | Enhance product pages with olfactive profiles, ingredients, perfumer data, and visuals | Fragrance Library |
| 6. Recommend | Generate personalized recommendations based on olfactive affinity | Recommendations |
| 7. Persist | Reuse preference data across sessions, channels, and CRM | CRM & Personalization |
Next steps
Authentication & Security
Set up your API key and understand the server-side execution model.
Catalog Integration
Connect your product catalog to WikiParfum.
Fragrance Library
Enrich your product pages with olfactive data and visuals.
Exploring the Schema
Use GraphQL introspection to discover all available queries and types.