Overview

While working on the hotel booking experience at Eghamat24, I identified a significant gap in the reservation journey.

Users could search for hotels using only destination and travel dates. Information such as the number of guests, children, or required rooms was not captured until much later in the booking process.

As a result, many users spent time comparing hotels and room options only to discover near the end of the funnel that the selected room did not fit their actual travel needs.

This issue was particularly common among families traveling with children and larger groups.

The goal of this project was to reduce booking uncertainty, improve room selection confidence, and establish a foundation for future guest-based booking experiences.

 

The Challenge

The existing experience did not adequately support several common travel scenarios.

Families Traveling with Children

Users had no clear way to understand:

• Whether a room accepted children
• Whether an extra bed was required
• How child accommodation policies worked
• Whether the selected room was suitable for their family

Larger Travel Groups

Users traveling with three or more guests often discovered room-capacity limitations late in the booking process.

Multi-Room Reservations

There was no way to specify multiple rooms when evaluating hotel options.

Unclear Capacity Information

Information related to room capacity and extra guests was not easily accessible, forcing users to continue through the booking process before understanding whether a room matched their requirements.

 

Research & Discovery

To understand the problem, I combined multiple sources of insight, including user research, competitive benchmarking, technical analysis, and stakeholder discussions.

User Research

One of the strongest signals came from the platform’s Questions & Answers section.

A large number of user inquiries were related to:

• Child accommodation
• Room capacity
• Extra guests
• Family bookings
• Multi-room reservations

Examples included:

“We are two adults traveling with a three-year-old child. Do we need an extra bed?”

“Can three people stay in a double room?”

“How are children under 12 charged?”

“Which room should I book for three adults and two children?”

These recurring questions revealed a clear gap between user expectations and the information available during the booking process.

 

Competitive Analysis

I reviewed both domestic and international travel platforms, including Booking.com, Expedia, Agoda, FlyToday, Alibaba Travel, Snaptrip, Iran Hotel Online, and MrBilit.

Key Insight

Most leading travel platforms collect guest composition information early in the journey, including:

• Number of adults
• Number of children
• Child age
• Number of rooms

This enables more accurate availability results and helps users make informed decisions earlier in the booking process.

 

Technical Constraints

Discussions with engineering teams revealed that several international hotel providers, including Rayna, TBO, and HotelBeds, require guest composition data as part of their availability APIs.

This highlighted that guest configuration was not only a usability improvement but also an important consideration for future platform scalability.

 

Design Constraints

Although the ideal solution would introduce guest configuration directly within the search experience, several constraints needed to be considered:

• Existing infrastructure did not support guest-based filtering
• Room packaging logic was highly complex
• Search-level implementation required significant backend changes
• The team needed a solution that could be validated quickly

Given these constraints, an MVP approach was selected.

 

Design Strategy

The strategy focused on balancing user value with implementation feasibility.

The primary objectives were:

Reduce Booking Uncertainty

Help users determine room suitability earlier in the journey.

Validate Demand

Collect behavioral data around family travel and multi-room booking scenarios.

Build for Future Iterations

Create a scalable foundation for more advanced guest configuration experiences.

 

Solution

Guest & Room Selector

I introduced a new guest configuration component within the Hotel Detail Page (PDP).

Users could specify:

• Number of adults
• Number of rooms

The room list dynamically updated based on these selections.

Configuration limits:

• Adults: 1–6
• Rooms: Up to 4

 

Capacity-Based Room Filtering

Rather than implementing complex room-packaging logic, room availability was filtered based on room capacity.

Rooms that could not accommodate the selected guest count were separated from primary results, helping users focus on relevant options while maintaining transparency.

 

Child Acceptance Badge

Child accommodation policies varied significantly between hotels, and exact child pricing was not consistently available.

To reduce ambiguity, I introduced a dedicated visual badge for rooms that accepted children.

This allowed users to quickly identify family-friendly room options without displaying potentially inaccurate pricing information.

 

Why This Solution?

This approach provided several advantages:

• Delivered value without major backend dependencies
• Reduced implementation complexity
• Enabled faster validation of assumptions
• Lowered delivery risk
• Established a foundation for future enhancements

Most importantly, it addressed a meaningful user problem while remaining realistic within existing technical constraints.

 

Success Metrics

The project defined several success indicators to evaluate impact after launch.

Business Metrics

• Increase bookings involving children
• Increase multi-room reservations
• Improve conversion rates for larger travel groups

User Experience Metrics

• Reduce capacity-related support questions
• Reduce booking modification requests
• Improve booking confidence

Product Metrics

• Measure adoption of the guest-selection feature
• Validate demand for family travel scenarios
• Support future investment decisions

 

Design Process & AI Support

Throughout the project, I used AI-assisted tools such as Claude, Gemini, and Figma AI to support research synthesis, competitive analysis, design exploration, and documentation.

These tools helped accelerate repetitive tasks, explore alternative approaches, and improve decision-making efficiency, allowing more time to focus on user needs, product strategy, and design quality.

AI supported the process, but product thinking, prioritization, design decisions, and final execution remained human-led.

 

Key Takeaways

This project reinforced an important product design principle:

The most effective solution is not always the most comprehensive one.

By balancing user needs, business goals, technical constraints, and implementation feasibility, I was able to design and launch an MVP that addressed a real customer problem while creating a scalable foundation for future growth.

The project also demonstrated how thoughtful use of AI can enhance modern product design workflows by accelerating research, expanding exploration, and improving collaboration without replacing human-centered design thinking.