SkinFit

Global Beauty Match SaaS

Why Us? Trust Structure Demo

🌍 Global Recommendation

🧪 Survey Based

🧠 Analysis + Routine

🛒 Direct Buy

📈 Data Driven

60s Skin Test for a
"Wow..." Routine
and Instant Purchase

When users respond to Sensitivity · Age · Condition · Tone surveys, we generate a Personal Skin Profile, calculate Ingredient/Texture/Fit, and present a Custom Routine + Recommended Products. Connected to checkout, with data building over time.

How it works? ↘ How we build trust? 🧾

✅ MVP starts as Web SaaS: Survey → Result → Recommendation → Buy in one flow.

Mini Demo: "Fun Survey"

The real service can be much longer.
This 5-item demo shows the "Wow" feel.

✨ READY

👀 Survey data is collected for Recommendation Accuracy and Conversion.

What Makes Us Successful

We embed these 5 success points directly into the "Product Message".

⭐ Core Principles

🎮

1️⃣ Fun Survey Experience

Gamified with sliders/instant feedback. High completion rate equals high revenue.

🤯

2️⃣ "Wow" Results

Visualize Sensitivity, Hydration, Tone, and Barrier scores with a one-line diagnosis.

🧾

3️⃣ Trusted Recommendations

Shows the "Why" (ingredients/texture). User trust drives repeat visits.

🛒

4️⃣ Instant Purchase

Buy buttons on recommendation cards. Auto-routing to local stores (Amazon/Shopee/etc).

📈

5️⃣ Data Accumulation

Events like surveys/clicks/buys are saved. Accuracy improves as data grows.

🔁

Bonus: Growth Loop

Share results → New users → More data → Better recs → Higher conversion.

Making Recommendations Not Look Like "Ads"

Trust is a design, not just a feature. (Ready for MVP)

🛡️ Trust Layer

✅ Transparent Logic

High sensitivity automatically excludes fragrances/essential oils.
High oiliness gives more weight to gel/lotion textures.
Low barrier score boosts Ceramides/Panthenol weights.

✅ Safety/Caution Tags

Display "Caution Ingredients" as warning tags.
Auto-suggest "Patch Tests" for sensitive skin.
One-line summary of why it fits for every recommendation.

Web SaaS MVP Structure

Capture conversion and data on Web before moving to an App.

🧩 Flow

1) Survey

Completion Opt.

2) Analyze

Scores/Rules

3) Recommend

Proof + Routine

4) Buy

Local Links

5) Store

Events/Conv.

Rule-based recommendations are fastest and most accurate for MVP. As data builds, we evolve into weighted learning or ML/LLM models. (This demo is a scaled-down rule-based version.)

See your "Wow" results now

Demo Survey → Result Report → Recommendation/Buy buttons all in one go.

60s Skin Test for a "Wow..." Routine and Instant Purchase