What it does
looklike is a personal styling and fashion discovery app that aims to solve the biggest problem with online shopping: not knowing how clothes will actually look on you. It guides users through creating a personalized digital avatar from their photos, then serves an endless stream of outfits for them to try on virtually. Users can swipe through looks, get style feedback from an AI assistant, and shop for items they love.
Where it shines
The app's most compelling feature is its avatar-centric experience. The onboarding, while lengthy, successfully builds anticipation for the moment you see yourself in new clothes. The style quiz, which uses tappable split-screen images (00:41), is a standout piece of UX that makes data collection feel like a fun game. The core interaction of swiping through outfits on your own avatar (03:37) is addictive and delivers on the app's core promise immediately. Furthermore, the feedback loop for rejected outfits (03:41) is a smart way to refine recommendations, showing a commitment to learning the user's taste.
UX highlights
- Conversational UI: The app uses a chatbot-like interface for onboarding and questions, making the experience feel guided and personal.
- Tangible Personalization: Instead of abstract style labels, personalization is made concrete through the user's own avatar, which is a powerful hook.
- Actionable Feedback: When a user dislikes an outfit, the app immediately asks for structured feedback, turning a negative moment into a productive data point.
- Seamless Shopping Integration: Tapping on an item within a saved look (07:20) seamlessly transitions the user to a shopping interface with price watching capabilities.
- AI Stylist Chat: The ability to upload your own photos for feedback (06:04) extends the app's utility beyond just virtual try-ons, positioning it as a true style companion.
- Focused Core Loop: The Tinder-like swipe mechanic is simple, intuitive, and perfectly suited for rapid discovery and data collection.
Monetization & growth
The app introduces a subscription paywall after the user has engaged with several features, including saving items and setting price alerts (07:35). It presents two options: a monthly plan and a recommended yearly plan that offers a significant discount and a 3-day free trial. The paywall uses a small carousel to highlight key premium benefits, such as the number of outfits shown per day. The yearly plan is pre-selected to anchor the user towards the better value offer. The purchase is confirmed through the standard App Store flow.
Who it’s for
This app is clearly designed for fashion-conscious online shoppers who are frustrated with the uncertainty of buying clothes online. Its target audience likely skews towards individuals who enjoy discovering new styles but are also practical, wanting to see how items fit their specific body type before purchasing. It also appeals to users who are interested in building a better wardrobe but may not have the time or budget for a human personal stylist.
Notes & opportunities
While the onboarding is engaging, its length could be a point of friction for some users; testing a shorter version might be worthwhile. The AI stylist chat is a powerful feature, but its capabilities could be showcased more prominently earlier in the experience. For example, offering a quick style tip based on initial quiz answers could demonstrate its value sooner. Finally, the ability to delete an account is somewhat buried in the settings (07:01), which could be made more accessible to align with user-centric design principles.