You're spending ₹60 lakh monthly on Google Search and Meta campaigns to drive travel app installs. Your dashboard shows 80,000 installs with strong D1 retention at 45%. But when you calculate actual booking conversion, the numbers collapse: only 2.8% of installs result in completed bookings, and your CAC-to-booking ratio makes every customer acquisition unprofitable.

Here's what's happening: you're measuring app activity (opens, sessions, screen views) instead of purchase intent signals. A user who opens your app five times but never selects travel dates isn't "engaged," they're browsing. Meanwhile, a user who searches for specific dates, compares prices, and saves properties is showing clear purchase intent, but most teams can't identify these high-value actions until it's too late.

Travel apps face unique measurement challenges that generic event taxonomies ignore completely. Long consideration windows (14-90 days from search to booking), high-ticket purchases (₹15,000 to ₹2+ lakh per transaction), and multi-session research behaviour require specialised tracking that separates browsing from booking intent.

This guide breaks down the 7 events that predict booking conversion and enable profitable UA for travel and hospitality apps.

Why Generic Event Tracking Fails for Travel Apps

Most travel teams inherit event structures from eCommerce or utility app playbooks. They track add_to_cart, checkout_started, and purchase_completed, then wonder why their conversion funnels show massive drop-offs and their retention models don't predict revenue.

The core problem: travel purchasing behaviour is fundamentally different from impulse eCommerce or utility transactions. Users don't browse flight options and book immediately. They research extensively (comparing prices across dates, reading reviews, checking amenities), often across multiple sessions spanning days or weeks, before committing to purchases that cost 10-100× more than typical app transactions.

The long consideration window: Unlike food delivery apps where intent-to-purchase happens in minutes, or ride-hailing apps where booking occurs within seconds of search, travel apps experience consideration periods of 14-90 days. A user might search for Goa hotels in January, compare options for two weeks, then book in February for an April trip. If your attribution window is set to 7 days, you're missing the majority of conversions and severely under-attributing campaign effectiveness.

A hotel booking app we analysed was using 7-day attribution windows standard for most mobile apps. Their internal reporting showed Meta campaigns driving 12% booking conversion, while Google UAC showed 8%. When they extended attribution windows to 30 days and properly tracked high-intent events (dates selected, price compared), the picture changed completely: Meta's true conversion rate was 31%, and Google's was 28%. Their entire budget allocation was backwards because they were measuring browsing activity instead of purchase signals.

The high-ticket validation requirement: When users spend ₹50,000 on a flight or ₹1.2 lakh on a hotel package, they validate decisions through multiple data points: reviews, price comparisons, amenity verification, cancellation policies. Simply tracking "property viewed" doesn't distinguish between casual browsing and serious evaluation. Without granular intent signals, you can't predict which views lead to bookings.

Event #1: Search Performed (Intent Signal)

What it is: User actively searches for travel options by entering destination, dates, or specific criteria. This is your first clear signal of travel intent, distinguishing researchers from casual browsers.

Why it matters: Search represents the transition from passive browsing to active intent. Users who search have specific travel goals and are entering your purchase funnel. Search users convert to bookings at 8-15× higher rates than browse-only users.

Track this event with these parameters:

• search_type: Flights, hotels, packages, activities

• destination: City or region searched

• origin: Departure city for flights, current location for hotels

• search_date: When search occurred

• flexibility: Flexible dates vs specific dates

• party_size: Number of travellers or rooms

Implementation detail: Fire this event when search actually executes (user submits search form), not when they focus on the search bar. Distinguish between autofill selections and manual searches, as manual entry indicates higher intent.

Intent strength indicator: Users who search within 24 hours of install are 5× more likely to book within 30 days compared to users who install but delay searching. Time-to-first-search is a powerful early predictor of conversion likelihood.

A flights app found that users who searched for specific date ranges (not flexible dates) had 3.2× higher booking conversion than flexible date searchers. They started sending date_specific_search as a high-value postback event to Meta and Google, enabling ad platforms to optimise toward users with concrete travel plans rather than aspirational browsers.

Attribution note: Search is often your first attributable intent event. Users might install your app from a Meta ad but not search immediately. When they search three days later, that search validates the original install attribution and moves them into your active consideration funnel.

Event #2: Property or Flight Viewed (Consideration Start)

What it is: User views detailed information about a specific property, flight, or package. This marks the beginning of serious evaluation.

Why it matters: Viewing details indicates the user found something worth investigating. While many views don't convert to bookings, view depth and frequency predict conversion likelihood. Users who view 3+ properties in a session are 4× more likely to book than single-view users.

Track this event with these parameters:

• item_type: Hotel, flight, package, activity

• item_id: Unique identifier for property or flight

• price_displayed: The price shown to user

• location: Destination city or neighbourhood

• rating: Property rating or airline tier

• view_duration_seconds: How long user stayed on details page

• images_viewed: Number of photos user browsed

Depth matters: Don't just count views. Track engagement depth: how long users spend on property pages, how many photos they view, whether they expand amenity details or read reviews. High-engagement views (60+ seconds, 5+ photos) convert at 6× higher rates than quick glances (under 10 seconds).

Repeat view signal: Users who view the same property across multiple sessions are showing strong consideration. A hotel app discovered that users who viewed the same property 3+ times (across different days) had a 42% booking conversion rate, compared to 6% for single-view users. They implemented a "viewed again" parameter to identify return visitors and send higher-value postback signals to ad networks.

Price sensitivity tracking: The price_displayed parameter lets you analyse booking conversion by price tier. If users viewing properties under ₹5,000/night convert at 18% while ₹15,000+ properties convert at 4%, you can optimise campaigns and creative toward different price segments.

Event #3: Dates Selected (High-Intent Filter)

What it is: User specifies exact check-in/check-out dates or departure/return dates. This narrows search from general browsing to concrete trip planning.

Why it matters: Selecting specific dates is a high-intent action that indicates users are planning actual travel, not dreaming. Date selection moves users from "someday" to "planning phase" and predicts booking conversion within 7-30 days.

Track this event with these parameters:

• travel_start_date: Check-in or departure date

• travel_end_date: Check-out or return date

• days_until_travel: Days from date selection to travel start

• trip_duration_days: Length of trip

• booking_window: Days from selection to desired travel date

• date_change_count: How many times user modified dates

Booking window urgency: Users selecting dates within 14 days of desired travel show higher urgency and convert faster, but often at lower average booking values. Users planning 30-90 days ahead typically book higher-value trips (international travel, resort packages). Track booking window to understand whether campaigns drive last-minute bookers or advance planners.

Date flexibility indicator: If users frequently change dates (modifying search 3+ times), they're either flexible or price-sensitive. Date-change behaviour correlates with price comparison activity and longer consideration periods. Users who select dates once and proceed to booking convert 3× faster than users who modify dates repeatedly.

A vacation rental app found that users who selected weekend dates (Friday-Sunday or Saturday-Monday) converted to bookings at 2.4× higher rates than weekday date selectors, indicating stronger leisure travel intent. They adjusted campaign targeting to prioritise weekend date searches and improved booking conversion by 34%.

Attribution timing: Date selection often occurs 5-15 days after initial search, requiring attribution windows that connect early-funnel activity (search) to mid-funnel intent signals (date selection). Without proper attribution window configuration, you'll undercount campaigns that drive initial awareness before users commit to specific dates.

Event #4: Price Compared (Decision-Stage Signal)

What it is: User actively compares prices across different options, dates, or providers. This indicates users are in decision-making mode, evaluating tradeoffs before committing.

Why it matters: Price comparison is one of the strongest pre-booking signals. Users don't invest time comparing prices unless they're seriously considering a purchase. Comparison behaviour predicts booking within 7 days at 67% accuracy.

Track this event with these parameters:

• comparison_type: Same property different dates, different properties same dates, same flight different classes

• items_compared_count: Number of options evaluated

• price_difference_percentage: Variance between cheapest and most expensive option

• comparison_session_duration: Time spent comparing

• cheapest_option_selected: Did user choose the lowest-priced option

Comparison depth: Users who compare 2-3 options are in active decision-making. Users who compare 10+ options are often still researching or waiting for better deals. Track comparison count to segment ready-to-book users from extended researchers.

Price sensitivity insights: If users consistently choose the cheapest option when comparing, they're price-driven buyers who respond well to discount campaigns and last-minute deals. If users choose mid-range or premium options despite seeing cheaper alternatives, they're value-driven buyers who prioritise quality, amenities, or convenience over price.

A flight booking app analysed price comparison behaviour and discovered two distinct user segments: "deal hunters" who always chose cheapest flights (32% of comparers, 18% booking conversion, low average booking value) and "quality seekers" who chose mid-tier options (41% of comparers, 29% booking conversion, 2.1× higher average booking value). They created separate campaign optimisation strategies for each segment, improving overall ROAS by 42%.

Implementation note: Price comparison can happen within a single session (user opens multiple tabs, swipes between options) or across sessions (user checks prices Monday, returns Friday to compare again). Track both same-session and cross-session comparison to understand full user behaviour.

Event #5: First Booking Completed (Conversion Milestone)

What it is: User successfully completes payment and confirms their first booking. This is your primary revenue event and the goal of all upstream activity.

Why it matters: First booking validates your entire funnel: marketing attracted the right users, product enabled successful search and comparison, and checkout removed friction. It's also your ROAS calculation anchor and customer acquisition validation point.

Track this event with these parameters:

• booking_id: Unique transaction identifier

• booking_type: Flight, hotel, package, activity

• revenue_amount: Total booking value in INR

• commission_earned: Your revenue from transaction

• destination: Where they're travelling

• travel_start_date: When trip begins

• booking_to_travel_days: Advance booking period

• payment_method: Card, UPI, wallet, EMI

• install_to_booking_days: Time from install to first booking

Critical implementation: Always validate bookings server-side before firing this event to ad networks. Client-side events are vulnerable to payment failures, cancellations, and fraud. Only send booking_completed postbacks after payment is confirmed and booking is guaranteed.

Install-to-booking window: The median time from install to first booking is 14-21 days for most travel apps. However, 35-40% of bookings occur 30-90 days post-install. If your attribution window is shorter than 30 days, you're severely under-attributing campaign effectiveness.

Revenue value tracking: Include both gross booking value (what user paid) and net revenue (your commission or margin). For attribution purposes, use net revenue in ROAS calculations to measure actual business impact, not just transaction volume.

A hotel aggregator found that campaigns optimised toward booking volume drove high booking counts but low average booking values (₹3,200 per booking). When they switched to optimising toward booking revenue, average booking value increased to ₹7,800 while booking count decreased by 18%, but total revenue increased by 34% and profitability improved by 61%.

First-time vs repeat bookings: Track whether this is the user's first booking or a repeat transaction. First-time booking conversion validates acquisition effectiveness. Repeat bookings validate product quality and retention, and typically occur with lower friction and higher average values.

Event #6: Review Submitted (Trust-Building Action)

What it is: User submits a review or rating after completing travel. This indicates post-purchase engagement and willingness to contribute to community trust.

Why it matters: Review submission predicts repeat booking likelihood with 78% accuracy. Users who review their trips are 4-6× more likely to book again within 6 months compared to users who never submit reviews. Reviews also improve conversion for future users, creating a compounding trust effect.

Track this event with these parameters:

• review_type: Property review, flight review, overall trip review

• rating_score: 1-5 stars or other rating scale

• review_length_words: Word count of written review

• photos_uploaded: Number of images included

• days_after_travel: Time from trip completion to review

• review_sentiment: Positive, neutral, negative (if analysed)

Retention predictor: Users who submit reviews within 7 days of trip completion have an 83% probability of booking again within 90 days. Those who delay reviews beyond 14 days show 32% repeat booking likelihood. Prompt review submission indicates high satisfaction and active platform engagement.

Review quality matters: Detailed reviews (100+ words, photos included) correlate with higher user lifetime value. These users are invested in the platform community and typically become power users who book 3-5× more frequently than review-avoiders.

A tours and activities app implemented a review reminder notification 3 days post-activity. Review submission rates increased from 8% to 23%, and users who submitted reviews showed 5.7× higher 180-day LTV. The platform also started sending review_submitted as a postback event to ad networks, enabling optimisation toward users who engaged beyond transactions.

Trust ecosystem impact: Every review submitted improves conversion for future users viewing those properties. Apps with strong review ecosystems (15%+ of bookings result in reviews) see 20-30% higher overall booking conversion because user-generated content reduces purchase anxiety for new customers.

Event #7: Repeat Booking (Loyalty Signal)

What it is: User completes their second or subsequent booking. This validates customer retention and indicates successful first-trip experience.

Why it matters: Repeat bookings drive unit economics in travel apps. CAC for repeat customers is effectively zero (no acquisition cost), margins are higher (less price sensitivity), and average booking values typically increase 20-40% on repeat transactions as users trust the platform more.

Track this event with these parameters:

• booking_number: Second booking, third booking, etc.

• revenue_amount: Transaction value

• days_since_last_booking: Time between bookings

• destination_type: Same destination repeat, new destination

• booking_value_change: Percentage increase/decrease vs first booking

• loyalty_tier: If applicable, user's loyalty program status

Repeat rate benchmarks: Healthy travel apps see 25-35% of first-time bookers make a second booking within 6 months, and 45-60% within 12 months. If your repeat rate is below 20% at 12 months, you have either a product quality problem (bad first-trip experience) or a retention marketing problem (users forget about your app).

Time-to-repeat analysis: Users who book again within 60 days typically show higher lifetime frequency (5+ bookings over 2 years). Users who delay 6+ months before second booking show lower overall engagement and are more price-sensitive.

A bus booking app found that users who made their second booking within 30 days had an average lifetime booking count of 8.4 trips over 18 months, compared to 2.1 trips for users whose second booking occurred 120+ days after their first. They implemented post-booking retention campaigns targeting the 30-day window, increasing repeat booking rates by 47%.

Booking value progression: Track whether repeat bookings increase in value. Users who book higher-value trips on their second booking (indicating growing trust) have 3× higher LTV than users whose booking values decrease or stay flat. Value progression indicates platform confidence and reduced price sensitivity.

Attribution Windows for Travel: Why 30-90 Day Windows Are Standard

Travel apps require significantly longer attribution windows than most mobile app categories because of extended consideration periods and advance booking behaviour.

Standard window configuration:

• Install to first search: 7 days (intent activation window)

• Install to date selection: 14 days (planning phase window)

• Install to first booking: 30-60 days (conversion window)

• Install to repeat booking: 90-180 days (retention window)

The 7-day fallacy: Most mobile apps use 7-day attribution windows inherited from eCommerce or utility apps where purchase decisions happen quickly. For travel apps, 7-day windows catastrophically under-attribute campaign effectiveness. Research shows 65-75% of travel bookings occur 8-90 days post-install, meaning short windows miss the majority of conversions.

A vacation rental app was using 7-day attribution windows and attributing only 31% of their bookings to paid campaigns, making all UA appear unprofitable. When they extended to 30-day windows, attribution coverage increased to 68%. At 60-day windows, coverage reached 84%. Their cost-per-booking calculations were off by 3× because of measurement configuration, not actual campaign performance.

Seasonal considerations: Travel apps often see even longer consideration windows during peak seasons (summer holidays, Diwali, Christmas). Users book international trips 90-180 days in advance. Domestic weekend getaways book 14-30 days ahead. Configure different attribution windows for different booking types to maintain accuracy.

SKAN implications for iOS: SKAdNetwork's conversion value window is 3 days by default, creating massive measurement gaps for travel apps. Configure SKAN to prioritise high-intent signals that occur within the measurement window: search_performed, dates_selected, price_compared. Don't wait for booking events that occur weeks later.

Common Implementation Mistakes to Avoid

Most travel teams make predictable errors when implementing event tracking. Here's what to watch out for:

Treating all property views equally: A user who views a property for 5 seconds is not equivalent to a user who views for 90 seconds, browses 12 photos, and reads reviews. Track engagement depth, not just binary views.

Ignoring booking cancellations and modifications: Users can book then cancel, or modify reservations significantly. Track booking_cancelled and booking_modified events to maintain accurate revenue attribution and conversion funnel analysis.

Using 7-day attribution windows: This is the #1 mistake travel apps make. You will under-attribute by 60-70% and make all UA appear unprofitable. Use 30-60 day windows minimum.

Missing web-to-app attribution: Many users research travel options on web (desktop browsing, price comparison sites, review platforms) before installing your app to book. Without web-to-app attribution, you're undercounting campaigns that drive awareness before app conversion.

Not tracking trip metadata: Simply logging "booking completed" without destination, trip duration, and booking window prevents you from understanding which types of travel drive retention and LTV. Always include rich metadata in booking events.

Forgetting post-booking engagement: The relationship doesn't end at booking. Track trip completion, review submission, and post-trip communication to measure customer satisfaction and predict repeat behaviour.

How Linkrunner Simplifies Travel App Attribution

Travel apps need attribution platforms that handle long consideration windows, high-ticket revenue tracking, and complex multi-session journeys. Platforms like Linkrunner reduce setup complexity while providing the measurement depth travel teams require.

Linkrunner's SDK makes it simple to track custom travel-specific events (search, date selection, price comparison, bookings) through a clean API. Instead of configuring separate postbacks for each ad network, Linkrunner's unified dashboard sends events to Meta, Google, and TikTok from a single interface.

The platform automatically supports extended attribution windows (30-90 days) that travel apps need to capture delayed conversions. You can configure different windows for different event types: 14 days for search, 60 days for bookings, 180 days for repeat purchases.

For teams running campaigns across Meta, Google, and affiliates, Linkrunner's campaign intelligence dashboard shows full-funnel visibility from click to booking with campaign-level ROAS. You can see exactly which campaigns drive users who progress through search → date selection → price comparison → booking, not just users who install and churn.

At ₹0.80 per attributed install, Linkrunner costs 3-10× less than legacy MMPs while providing the revenue attribution accuracy and funnel visibility that travel apps need to scale profitably.

If you're spending 5-10% of your UA budget on attribution tooling, request a demo from Linkrunner to see how modern MMPs deliver better measurement at a fraction of the cost.

Key Takeaways

Travel apps require specialised event tracking that captures high-intent signals and extended consideration behaviour:

• Track search_performed as your first intent signal, distinguishing researchers from browsers

• Measure property_viewed with engagement depth (duration, photos viewed) to predict conversion

• Capture dates_selected to identify users moving from browsing to concrete trip planning

• Monitor price_compared behaviour to understand decision-stage activity and price sensitivity

• Validate booking_completed server-side and include revenue amounts for accurate ROAS calculation

• Track review_submitted as a powerful retention predictor (reviewers book 4-6× more frequently)

• Measure repeat_booking within 60 days to identify high-LTV customers early

• Use 30-60 day attribution windows minimum to capture delayed conversions

Implementation takes one week if you prioritise intent signals and extend attribution windows before scaling spend. Most travel teams waste months with 7-day windows that miss 70% of conversions.

The teams winning in travel are those who measure what actually predicts bookings: search specificity, date selection, comparison depth, and post-booking engagement. Everything else is noise.