Function of Beauty
personalized product formulation by algorithm from a quiz
Function of Beauty formulates custom hair care through an algorithm from a quiz that supports 80 percent of its business (more than one million engagements per year), with more than 15 million formulations created and expected 2023 revenue of 125 to 150 million dollars.
Key points
- Personalized product formulation by algorithm from an online quiz.
- Proprietary algorithm, Visual Quiz Builder front end, made-to-order factory.
- Quiz carrying 80% of the business, 80% completion (+16.4%), more than 15 million formulations.
- Evidence level B, confirmed active status.
Objective
Convert through a quiz that turns hair goals into a custom formula, make the quiz the near-single entry point of the business, and fuel subscription-based replenishment.
The deployment
Function of Beauty, founded in 2015, personalizes hair, skin, and body care from an online quiz. The answers feed a proprietary algorithm that composes a custom formula and routine, then an API passes the profile to manufacturing to customize product and bottle. The company says it has created more than 15 million personalized formulations to date, with a potential of billions of combinations, on a factory with automated lines. The quiz carries most of the activity: it supports 80 percent of the business, more than one million engagements per year. After a quiz redesign, the brand reached an 80 percent completion rate, up 16.4 percent from its in-house solution, with hundreds of thousands of completed quizzes in the first three months. Expected 2023 revenue was between 125 and 150 million dollars; the company has raised 164 million dollars in total and acquired Atolla in 2021.
Results Proof B
Platform case study (Visual Quiz Builder) quantifying the share of the business and completion, plus a BeautyMatter profile quantifying formulations and revenue. Two consistent quantified sources, but interested or secondary and without public financial results, hence B.
How it works
Documented architectureThe stack in detail
- outil Algorithme de formulation proprietaire In-house engine that converts profile and hair goals into a custom formula and routine; its exact nature is not detailed publicly
- outil Visual Quiz Builder Online quiz front end, used for the redesign that raised completion to 80 percent
- infra API de transfert vers la fabrication In-house API that passes the quiz profile to the factory to customize product and bottle
- infra Usine de fabrication a la commande Automated lines producing the personalized formulas one at a time, more than 15 million formulations created
How it runs, concretely
For ops teams-
1Online quiz customer
The customer fills in profile and hair goals (up to several goals).
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2Algorithmic formulation AI / algorithm
The proprietary algorithm composes a custom formula and routine.
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3Handover to manufacturing AI + operations
An API sends the profile to the factory to customize product and bottle.
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4Manufacturing and replenishment operations + CRM
Made-to-order production on automated lines, then re-subscription.
The quiz answers and its completion rate. If the quiz drops off, it is 80 percent of the business that loses its entry point.
How your customers perceive this type of use
Sourced studiesLe paradoxe est documente des deux cotes : 71% des consommateurs attendent des interactions personnalisees et 76% sont frustres quand elles manquent (McKinsey, 2021), mais 75% declarent ne pas acheter aupres d'organisations auxquelles ils ne confient pas leurs donnees (Cisco, 2024). La « creepy line » est localisee : messages recus quelques secondes apres une recherche et suivi de localisation sont les pratiques qui mettent le plus mal a l'aise (Periscope by McKinsey, 2019).
Acceptance conditions
- La confiance dans le traitement des donnees precede l'achat : 75% ne achetent pas sans elle (Cisco 2024)
- Un cadre legal protecteur rassure : 59% des consommateurs disent que des lois fortes sur la vie privee les rendent plus a l'aise pour partager des informations dans des applications IA (Cisco 2024)
- La personnalisation elle-meme est attendue quand elle est consentie : environ la moitie des consommateurs (US 55%, UK 52%) disent s'inscrire souvent ou parfois a des services personnalises (Periscope by McKinsey 2019)
Red lines
- Le message declenche quelques secondes apres une recherche ou un achat : deuxieme ou troisieme cause de malaise selon les pays (Periscope by McKinsey 2019)
- Le suivi de localisation percu comme de la surveillance : 40% de malaise en Allemagne et au Royaume-Uni (Periscope by McKinsey 2019)
- Le mesusage des donnees personnelles par l'IA, devenu la premiere inquietude des consommateurs, a 53% et en hausse (Qualtrics 2025)
Sources: McKinsey & Company 2021 · Periscope by McKinsey 2019 · Cisco 2024 · Qualtrics 2025
How to replicate
Inference, not sourcedData prerequisites
- structured quiz converting goals into formula attributes
- ingredient reference and compatibility rules
- data connection between the quiz front end and manufacturing
Org prerequisites
- made-to-order manufacturing or a flexible contract manufacturer
- product R&D to validate the formulas
- e-commerce with subscription
Possible stack
- quiz builder (Visual Quiz Builder type)
- personalization / rules engine
- modular production line + API
The plan, step by step
- Step 1Build the quiz (profile, goals) and the ingredient reference with compatibility rules, validated by product R&D.Deliverable: Online quiz and answer-to-formula-attribute mapping on one category
- Step 2Develop the formulation engine (rules or scoring) and have a first set of formulas validated by R&D.Deliverable: Engine that outputs a valid formula for each answer combination
- Step 3Prototype made-to-order production with a flexible contract manufacturer and the API that passes the profile to manufacturing.Deliverable: Quiz-to-manufacturing chain tested on a pilot batch
- Step 4Launch D2C on the pilot category with subscription, and measure quiz completion, conversion, and replenishment.Deliverable: Offer on sale with a completion / conversion / replenishment dashboard
- Step 5Optimize the quiz (completion) and ramp up industrial capacity before opening other categories.Deliverable: Stabilized production and category extension plan
First step: Build the quiz and the mapping to a formula on one category, then prototype the connection to made-to-order production.
The same pattern, proven in other industries
Taobao
Real-time product recommendation with deep learning, served across hundreds of scenarios in the app
Amazon
Item-to-item collaborative filtering product recommendation engine, served across every touchpoint
ASOS
catalog-scale deep learning product recommendation, plus a conversational style assistant
Sources
- S1 Function of Beauty Delivering On the Promise of Personalization Secondary archive pending
- S2 Function of Beauty personalizes hair care with Visual Quiz Builder Interested party archive pending
An error, newer info, a source?
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