Predictive Airport Peak Hours Feature for Smarter Arrival Planning

User-centric feature designed to predict airport congestion patterns and help travelers plan arrivals more efficiently through data-driven insights

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The Problem: Unpredictable Airport Congestion Disrupts Traveler Planning

Frequent flyers and business travelers often experience unexpected airport congestion at security and check-in, leading to:

Missed flights
Increased travel stress
Inefficient arrival planning

Existing travel apps primarily focused on flight tracking and post-booking updates. While some offered partial airport wait-time insights, these capabilities were fragmented across platforms, leaving airport congestion and arrival planning as a major unresolved user pain point.

Defined and delivered the Airport Peak Hours feature to help frequent flyers proactively plan airport arrivals and avoid congestion-related delays.

Conducted user research, behavior analysis, and competitive analysis to identify airport wait times as a major, unresolved pain point, and defined a primary customer persona to guide feature scope and decisions.

Identified that while some competitors offered partial wait-time insights, these capabilities were fragmented and not integrated into an end-to-end travel planning experience.

Authored product requirement document to align stakeholders across product, design, and engineering, including:

Partnered closely with engineering to scope airport-level congestion insights, define edge cases, and balance data accuracy with user experience.

Supported feature launch and iteration, incorporating user feedback and analytics to refine usability and impact.

Positioned the feature as a proactive decision-support capability, strengthening FlyFi’s value as an all-in-one travel companion.

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My Role

Impact & Metrics

25% increase in user engagement after launching Airport Peak-hour feature

20% improvement in user retention through proactive travel planning

Positioned FlyFi as a differentiated, decision-support travel platform, not just a tracking app

Key Learnings

Learned how to translate qualitative user frustration (airport delays) into a clearly scoped, data-backed product opportunity.

Gained experience balancing user value, data availability, and technical feasibility when defining airport-level congestion insights.

Developed a deeper understanding of how fragmented competitor features can be unified into a differentiated, end-to-end user experience.

Learned the importance of proactive insights over reactive updates in driving user engagement and retention.

Improved ability to validate product decisions using user feedback and post-launch analytics, not assumptions.