De-Q

Live prototype

The interactive Figma prototype below is the real thing — click through the flow to see how the product works end to end.

Problem identified

Traditional retail shopping has long fitting-room wait times, which lead to customer frustration, abandoned purchases, and inefficient store operations. The uncertainty of waiting deters customers, and retailers struggle to manage queues effectively — hurting both sales and satisfaction.

Challenges faced

Integrating seamlessly into existing retail operations without disruption; providing accurate real-time wait estimates while keeping customers engaged through personalized recommendations; and convincing retailers to adopt new technology despite resistance to change.

Hypothesis

A digital queue-management system can significantly reduce wait times and improve the shopping experience. Real-time updates plus personalized recommendations keep customers engaged and increase the likelihood of additional purchases — lifting both satisfaction and store profitability.

Opportunity sizing

India has 600,000+ fashion retail stores. Capturing even a small share of organized retail represents a multi-million-dollar market. Given how central fitting rooms are to in-store purchase decisions, the potential for adoption and impact is significant.

Screens & user journey

Metrics to measure success

• Reduction in Wait Times — 30%+ vs pre-De-Q.
• Customer Satisfaction — 20%+ improvement in ratings.
• Conversion Rate — 10%+ increase in purchases after fitting-room use.
• Average Transaction Value — 15%+ increase via trial-cart recommendations.
• Adoption Rate — 70%+ among visiting customers.

Metrics to monitor

• Queue Abandonment Rate — flags wait-time accuracy or engagement issues.
• Engagement with Recommendations — measures the recommendation engine's sales impact.
• Staff Efficiency — less manual queue management frees staff for service.
• Feedback & Issues Logged — fuels continuous improvement.