As an avid traveler based in Singapore, I find myself constantly exploring travel tech platforms, mapping out my next adventure across Southeast Asia. What draws me to this region isn’t just its accessibility, but the beautiful complexity of its cultures and the organized chaos that makes each destination unique. While browsing apps like Klook, Trip.com, and Traveloka, I’ve found myself switching between two mindsets: that of an eager traveler and a curious product thinker.

These three platforms particularly intrigue me as they represent distinct approaches to solving travel pain points. Each has carved its own niche in the travel ecosystem, evolving their product strategies to serve different user personas and journey touchpoints. Through analyzing their product decisions, we can uncover valuable insights about user experience design, market positioning, and product-market fit in the travel tech space. In this blog, I am going to explore how core competencies change product decisions in each player.

Evolution

The Evolution of Travel Tech Giants

Each of these platforms has undergone significant transformation, adapting to changing market needs and expanding their service offerings. Let’s examine their evolutionary journeys and core product DNA:

Trip.com (Founded 1999) Originally launched as Ctrip in China, Trip.com evolved from a traditional OTA into a comprehensive global travel service platform. Starting with flight and hotel bookings, they gradually expanded their product suite to include rail tickets, car rentals, and local experiences. Their platform architecture reflects their heritage — prioritizing scalability and cross-border functionality while maintaining strong roots in the Asian market.

Traveloka (Founded 2012) Born as a flight search engine solving the complexities of Southeast Asian air travel, Traveloka identified a crucial pain point in their target market: the lack of a reliable flight booking system in Indonesia. Their product evolution mirrors their understanding of the Southeast Asian traveler’s journey — expanding into hotels, experiences, and even financial services. Their user experience is carefully crafted to serve a market where many users are making their first digital travel bookings.

Klook (Founded 2014) Klook flipped the traditional OTA model by starting with in-destination experiences. They recognized an underserved market opportunity: helping travelers discover and book local activities seamlessly. Their initial focus on experiences shaped their product design — emphasizing discovery, mobile-first booking, and local payment integration. They’ve since expanded into transportation and accommodations, but their core strength remains in experiences and activities.

Core Differentiators:

• Trip.com: Global reach with strong APAC presence, comprehensive travel services

• Traveloka: Deep Southeast Asian market understanding, localized solutions

• Klook: Experience-first approach, strong mobile user experience

Each platform’s current interface and user journey reflects this evolutionary history, with distinct approaches to solving travel planning and booking challenges.

Trip.com (1999) ────────────────────────────▶ Now
│OTA → Global Expansion-> Full-Service

Traveloka (2012) ──────────────────────────▶ Now
│ Flights ->Hotels ->Experiences

Klook (2014) ─────────────────────────────▶ Now
│ Experiences -> Transport ->Global

Core Design Philosophy

User Journey Comparison

Strength Matrix

Product focus

Core User Focus

Klook:
👤 Global Experience Seekers
👥 Mobile-first Users

Pain points

Let’s start with diving more into Klook.

Traveloka:
👤 Regional Travelers
👥 Value Seekers

Trip.com:
👤 Chinese Outbound
👥 Business Travelers

Core Issues with Klook’s Experience Details Page

1. Information Overload

The current content hierarchy doesn’t match user mental models or decision-making patterns:

• Excessive scrolling required to reach key booking information

• Critical ticket comparison details buried within lengthy content

• Visual clutter from an oversized gallery that competes for attention

2. Poor Information Architecture

• Ticket options and variants aren’t immediately clear

• Essential booking details are mixed with supplementary information

• No clear differentiation between must-know and nice-to-know content

3. Limited Discovery Flow

• Design optimized for high-intent users who know what they want

• Browsing users can feel overwhelmed without clear navigation paths

• Lack of quick comparison features for similar experiences

Proposed Solutions:

1. Reimagined Content Hierarchy

• Move booking/ticket selection above the fold

• Create collapsible sections for detailed descriptions

• Prioritize user-generated content (reviews with photos) over marketing gallery

2. Quick Decision Framework

• Implement a “Quick View” feature highlighting key details:

Price

Duration

Available time slots

Key inclusions

• Add comparison tooltips for different ticket types

3. Enhanced Navigation

• Add breadcrumbs for easier category navigation

• Implement “Similar Experiences” shortcuts

• Create a simplified view toggle for browsers vs. buyers

The core issue seems to be that Klook’s current design assumes all users need the same depth of information, when in reality, different users are at different stages of their decision-making process.

Klook’s current flow should be changed into a progressive flow.

Key differences in the flows:

Current Flow:

• Loads everything at once

• High initial cognitive load

• Requires significant scrolling

• No information hierarchy

Progressive Flow:

• Three distinct levels of detail

• Information revealed based on user interest

• Clear hierarchy of importance

• Reduced cognitive load at each stage

The progressive disclosure approach has several advantages:

1. Faster initial page load

2. Clearer decision-making path

3. Better mobile experience

4. Reduced overwhelm for users

To tackle information architecture challenges, some of the below tools can be used.

1. Information Architecture Challenge

• Cognitive load during browsing

• Potential decision paralysis

• Impact on conversion rates

Suggested improvements:

• Personalized content filtering

• Progressive disclosure of information (as the above progress flows)

• AI-powered recommendations based on browsing patterns

2. User Demographics & Design Evolution

• Travel booking apps generally see higher usage among 25–44 age group

• Growth opportunity is to design modernization for younger audiences

• Social media integration

• User-generated content features

• Short-form video content for experiences

3. Leveraging Review System for Growth is a strong foundation to build upon. Klook has a strong base where users share, engage and follow reviews. I personally always check reviews even if I do not purchase the tickets in the platform itself. Consider:

• Gamification of review system

• Review rewards program

• Cross-platform review sharing

To increase user growth and engagement in the platform, strategy includes:

1. Viral Loop Creation

• Implement referral programs with experience-based rewards

• Add social sharing features for booked experiences

• Create shareable travel itineraries

2. Community Building

• Develop user-generated travel guides

• Create travel planning groups

• Enable experience wishlists sharing

3. Acquisition Channels

• TikTok marketing focusing on experience previews

• Instagram Reels partnerships with travel creators

• Location-based push notifications for spontaneous bookings

Retention Strategies

• Post-trip engagement through photo albums

• Travel milestone achievements

• Loyalty program tied to experiences, not just spending

Product Improvements for Growth

• Simplified booking flow

• Better price comparison features

• Multi-language support enhancement

• Local payment method integration

Engagement Metrics to Track:

• Review submission rate

• Review quality score

• User-generated content volume

• Conversion impact from reviews

AI-Powered Corporate Travel Booking Solution for Traveloka

While Traveloka has established itself as a leading consumer travel platform in Southeast Asia, there’s an untapped opportunity in the corporate travel space. By leveraging their existing infrastructure and implementing AI capabilities, Traveloka could transform into an end-to-end corporate travel management solution that dramatically reduces administrative overhead while increasing compliance and efficiency.
The platform could function as an AI-powered executive assistant that seamlessly integrates with existing corporate systems. Whether through email, Workday, or internal HR portals, travel requests would be automatically captured and processed. The AI engine would parse travel requirements, understanding not just the basic needs but also individual preferences and corporate policies.
What sets this solution apart is its intelligent processing capabilities. The system would automatically generate travel options that align with company policies, optimize for cost, and consider factors like preferred airlines and hotels. More importantly, it would handle the entire workflow from initial request to post-trip expense management, eliminating manual intervention at every step.

Core Features:

1. AI Workflow

Corporate Request → AI Analysis → Travel Options → Approval → Booking → Tracking

1. Technical Architecture

Input Channels:
- Email
- Workday
- Internal HR portal

AI Processing:
- Requirements parsing
- Option generation
- Policy compliance check
- Cost optimization

Output:
- Detailed itinerary
- Booking confirmations
- Expense reports

To visualize the above context, here is the flow.

Partnership and AI-Enhanced Approach for Trip.com

Building on the workflow diagram shown, Trip.com could revolutionize its B2B approach through intelligent account targeting and personalization. By leveraging AI across the entire customer lifecycle, Trip.com can transform raw data from market research, sales databases, and industry trends into actionable insights. . This segmentation then drives customized strategies — from tailored pricing models to specific service packages and dedicated support channels. The key innovation here is automating the traditionally manual process of account management while maintaining personalization. For instance, AI could automatically generate customized marketing materials that reflect each account’s unique travel patterns, create targeted partnership proposals based on historical booking data, and provide proactive customer support by anticipating needs based on past behavior patterns.

1. Key Account Targeting Strategy

Target Segments:
- Large Enterprises
- Tech Companies
- Consulting Firms
- Multinational Corporations

2. AI-Powered Partnership Tools

• Custom sales deck generation

• Personalized account insights

• Automated proposal crafting

• Predictive account potential scoring

3. Partnership Workflow

Identification → Profiling → Custom Solution → Engagement

4. AI Marketing Customization

• Dynamic content creation

• Segment-specific messaging

• Real-time adaptation

• Personalized communication flows

5. Support Optimization

• AI-driven customer journey mapping

• Predictive support needs

• Tailored communication protocols

• Account-specific knowledge bases

6. Revenue Enhancement Approach

Partnership Value:
- Bulk booking discounts
- Customized travel policies
- Integrated expense management
- Dedicated account managers

Recommended Implementation:

Implementation Breakdown:

1. Account Identification Phase

• Data Collection Methods:

• Market research databases

• Internal sales data

• Industry trend analysis

• LinkedIn/professional network insights

2. AI-Powered Profiling

• Scoring Criteria:

• Annual travel spend

• Company size

• Industry vertical

• Previous travel booking patterns

• Potential for long-term partnership

3. Segmentation Strategy

• Tier Classification:

• Platinum: High-value, strategic accounts

• Gold: Significant potential

• Silver: Emerging opportunities

• Bronze: Standard corporate accounts

4. Customization Approach

• Tailored Offerings:

• Flexible pricing models

• Dedicated account managers

• Custom reporting dashboards

• Integrated expense management

• Priority support channels

5. Partnership Proposal Framework

• AI-Generated Components:

• Personalized value proposition

• Cost-saving projections

• Comparative analysis

• Tailored service recommendations

6. Engagement Tracking

• Monitoring Metrics:

• Booking volume

• Cost savings

• User satisfaction

• Contract renewal rates

7. Continuous Optimization

• Machine Learning Refinement:

• Adapt proposal strategies

• Predict account needs

• Identify expansion opportunities

Technology Stack:

• CRM Integration

• AI Analytics Platform

• Machine Learning Models

• Data Visualization Tools