Introducing Inclusive Beauty Searches for Mobile
Article Summary
Pinterest just shipped a feature that 60% of their top beauty searches were literally asking for. Yanis Markin walks through how they combined ML with inclusive design to solve it.
Pinterest's design and engineering teams launched skin tone range filters for beauty searches on mobile, starting with iOS. The feature lets users personalize makeup and hair results using a visual palette representing different skin tones, directly addressing user demand shown in search behavior.
Key Takeaways
- 60% of top 100 skin searches included tone descriptors like 'dark' or 'pale'
- Skin tone palette filter rolling out to iOS first, Android following
- Machine learning powers personalized Pin and video recommendations by tone
- Feature built from direct Pinner feedback and search pattern analysis
Pinterest turned search data into an inclusive product feature that lets users filter beauty content by skin tone, making discovery more relevant for everyone.
About This Article
Pinterest's beauty search didn't let users filter results by skin tone, even though people were clearly searching for this. Users with different complexions couldn't find relevant results.
Yanis Markin's team built a visual skin tone palette for beauty searches using machine learning and inclusive design. Pinners can now filter hair and makeup pins and videos to match their own complexion.
The feature addressed what users actually wanted based on their search behavior and feedback. Beauty discovery became more relevant and inclusive on Pinterest's mobile platform.