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PROS Holdings (FareNet / airTRFX) — Airline shopping-intent and observed-fare corpus: non-personalised capture of every flight search performed on participating airline websites, recording route, travel dates, journey type, cabin, site edition, language and device alongside the average fare that user actually saw — yielding route-level search volume, fare trajectory across the booking window, search-to-departure lead-time distributions and trip-length preferences.. Enterprise-scale, high-value, narrow-buyer: a ~$330M-revenue, ~1,300-person privately held airline-software group whose dataset i…
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Airline shopping-intent and observed-fare corpus: non-personalised capture of every flight search performed on participating airline websites, recording route, travel dates, journey type, cabin, site edition, language and device alongside the average fare that user actually saw — yielding route-level search volume, fare trajectory across the booking window, search-to-departure lead-time distributions and trip-length preferences.
From 2 yearsCoverage Industrials · Information TechnologyAsset class Equities
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PROS Holdings (FareNet / airTRFX) — Airline shopping-intent and observed-fare corpus: non-personalised capture of every flight search performed on participating airline websites, recording route, travel dates, journey type, cabin, site edition, language and device alongside the average fare that user actually saw — yielding route-level search volume, fare trajectory across the booking window, search-to-departure lead-time distributions and trip-length preferences.. Enterprise-scale, high-value, narrow-buyer: a ~$330M-revenue, ~1,300-person privately held airline-software group whose dataset i…
Pros offers (Alternative, Price, Sentiment, Reference) — Very large: every search on each participating airline's website, each carrying route, travel dates, cabin, site edition, language, device and the fare presented. For one large carrier that is plausibly tens of millions of observations a month; across a 200-airline-class installed base the corpus reaches the billions of search-fare observations per year. No total record count is published..
Price-elasticity and demand-curve model training per route; booking-window/lead-time demand models for media-buy timing; dynamic-pricing model training and offline evaluation; marketing-spend attribution and campaign-response models; route-launch and capacity-trimming decisions from revealed search demand; travel-demand nowcasting (search intent leads bookings by weeks); benchmark sets for time-to-purchase behavioural models; agent training on realistic airline shopping trajectories.
The data is with 2 years of history.
Coverage spans US, UK, APAC, Europe; Passenger Airlines, Application Software; alternative, price, sentiment, reference; equities.
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