AI-Enhanced Beauty Advisor Experience in Store
Paragon Corp · Posted 1 year ago
HealthcareRecommendation Systems / PersonalizationConversational AI / ChatbotsComputer VisionDocument Intelligence
- Budget
- USD 10,000
- Timeline
- 4–8 weeks
- Closed on
- 31 Dec 2025, 23:59 ICT
Problem statement
In-store consultations today depend heavily on each Beauty Advisor’s (BA’s) personal experience and multitasking capability — from delivering beauty advice to facilitating the checkout process.
BAs are expected to provide personalized product recommendations, educate consumers, and create an intimate brand moment. However, without AI support, personalization remains inconsistent and time-intensive.
Further, the checkout process is disjointed and manual — BAs must generate QR codes, print them, assist consumers in scanning, and sometimes even walk them through the cashier steps. This disrupts the consumer journey, adds friction, and reduces the BA’s ability to serve others efficiently.
There’s a clear opportunity to introduce AI-powered co-pilot tools that enable BAs to deliver consistent, delightful consultations, while also streamlining the purchase process — all in a seamless, tech-enabled flow.
Expected outcomes and success metrics
• Enable BAs to deliver consistent, high-quality consultations with AI-supported diagnosis and recommendations
• Reduce time-to-checkout and BA operational load by automating QR generation and payment flows
• Increase conversion rate and basket size through dynamic product matching and AI-assisted upsell logic
• Improve consumer satisfaction (CSAT/NPS) by offering a seamless, personalized, and efficient store journey
• Collect opt-in 1st-party data (e.g., skin type, beauty goals, preferences) to enrich CRM and support future targeting
• Enable smooth online-to-offline (O2O) integration (e.g., follow-up reminders, digital receipts, loyalty benefits)
Current process
Manual product recommendation by BAs using paper guides or static digital tools
Limited use of tablets or AR or tech devices, often disconnected from CRM or POS
Checkout still involves manual QR generation, printing, and cashier guidance
No centralized platform that unifies consultation, product matching, data capture, and purchase
Current solution
• Manual product recommendation by BAs using paper guides or static digital tools
• Limited use of tablets or AR or tech devices, often disconnected from CRM or POS
• Checkout still involves manual QR generation, printing, and cashier guidance
• No centralized platform that unifies consultation, product matching, data capture, and purchase
Target users and impacted teams
Beauty Advisors (Primary) – empowered by AI to deliver faster, smarter, and more consistent service; Retail Operations & Store Design Teams – responsible for tech enablement and in-store flow; Consumer Experience & Innovation Teams – to map and refine the end-to-end journey; CRM & Loyalty Teams – to capture in-store behavior and power future personalization; Tech & Data Teams – to integrate AI engine, CRM, POS, and ensure performance at scale.
Data availability and readiness
• Product and skin concern knowledge bases are available
• CRM and loyalty systems exist, but currently lack real-time syncing from physical stores
• Limited store data capture during checkout due to manual processes
• No AI trained yet on local skin tone variations, environmental factors, or checkout behavior patterns
Integration, deployment and infrastructure
• (Preferred not must) Integrate with:
1) POS system (to trigger purchase journey and collect transaction data)
2) CRM / Loyalty engine (for profile sync, reward generation, follow-up)
3) Product & diagnosis database (for real-time recommendation logic)
• Deployment:
o On BA tablets or shared AI stations (smart mirrors, kiosks)
o AI engine must support:
1) Bahasa Indonesia interaction
2) Offline backup mode
3) QR code checkout integration (e.g., e-wallets, digital carts)
• Infrastructure:
o Cloud-hybrid or edge-based (for stores with low connectivity)
o Data privacy compliant (ISO 27001, PDPA), with ASEAN-localized processing (Indonesia preferred)
Relevant AI technologies
Recommendation Systems / PersonalizationConversational AI / ChatbotsComputer VisionDocument Intelligence