Tira Shade Finder
Designing confidence into foundation shopping
The context.
Tira was launching Fenty Beauty exclusively in India, a brand globally known for its expansive foundation shade range.
Foundations on Fenty often span 30 to 40 shades, making shade selection a critical purchase decision.
At the time, Tira did not have a built-in mechanism to help users confidently select foundation shades online.
With a high-traffic launch window fixed and non-negotiable, we had four weeks to design and ship a solution.
The problem.
Foundation is a precision category. Unlike lipsticks or blushes, users are reluctant to experiment. Shade mismatch leads to hesitation, abandoned carts, and returns. We observed:
Shade selection drop-offs on foundation PDPs.
Users adding multiple shades to cart and removing them later.
Shade-related return complaints.
Increased hesitation when transitioning from non-premium to premium brands.
Users weren’t unsure about products. They were unsure about correctness.
My role.
Product Designer, working alongside one other designer.
Defined the dual-path shade finder strategy.
Collaborated with product and engineering on feasibility.
Shipped within a four-week launch window.
Worked with hybrid third-party detection and native mapping logic.
What we changed.
We introduced Shade Finder, an assisted shade-matching layer embedded inside foundation PDPs. Instead of a single tool, we designed two paths based on real purchase behaviors:
Live shade detection.
Users could analyze their skin tone via camera with guardrails for lighting, face detection, and positioning.
Instead of overwhelming them with 30+ shades, we surfaced the closest match, one shade lighter and one shade deeper.
With the option to explore all shades if needed.
Reference based mapping.
Users who already knew their existing foundation shade could search their current brand > product > shade.
We mapped that input to the closest Fenty shade using pre-mapped database logic. Shade recommendations were highlighted directly on the PDP and could be added to bag instantly.
What changed after launch?
Post-launch, Shade Finder showed strong performance signals:
~22%
reduction in PDP drop-off at shade selection.
~18%
lift in foundation conversion during Fenty launch window.
30%
increase in shade selection completion rate
Shade-related return complaints decreased by ~14%, while foundation purchases saw higher average order value and increased PDP engagement time.
Beyond metrics, users reported greater confidence switching brands, less hesitation with premium purchases, and appreciation for the balance of precision and flexibility.
Why this project matters?
Shade Finder wasn’t built as a novelty AR feature. It was designed to mirror how users shop foundation offline, reduce cognitive overload, and convert hesitation into confidence.
It turned a high-friction decision point into a guided, conversion-focused interaction, and became a scalable layer for complexion shopping on Tira.