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DataSlaves BigData Travel Hackings
Transcript of DataSlaves BigData Travel Hackings
Query (Amadeus) - Date of Travel: Oct 29
Result (TravelPort) - October Travel (searched in September)
Booking (ARC) - Date of Travel: Oct 29 - Dec 31
Departure Date/Time then
Airline (or preferred airline)
I want to book a flight. When should I book it?
# of stops
Class of service
Shop on Tuesday mornings for sales.
Fact or myth?
7 weeks in advance. 11 for int'l.
On flights that are less full.
Never buy within 14 days.
No day of the week is best.
Use Travelport data
Determine average price for a route (trip origin / destination) based on days in advance
Use ARC data
Determine average price paid for a route
Determine days to book in advance to determine consumer purchase profiles
Use Amadeus Data
How many days in advance do customers start their search?
Compare above data to visualize number of days in advance and price as a percent above/below average
Break down based on number of stops
Further granularity by drilling down by route
Spent significant time/effort learning tools we were not familiar with
Data Model (index) was not initially setup with appropriate data types for statistics
Would this benefit the Travel Industry?
Does the trend of when consumers book their tickets correspond to the best price?
How do major holidays impact this?
Does willingness to book flights with connections change based on days before flight?
Facets allow very fast analysis by terms (popular airports), dates, and perform statistics (average price, etc)
Richmond is popular!
Parsed raw data to JSON
Made minor transformations
Days booked in advance
Origin / Destination
Import JSON data to ElasticSearch
Use facets for statistical/term analysis
Visualize the results
The travel industry has lots of data!
Shiny new technologies aren't always the most appropriate!
Conduct more research on tools output prior to using
Tableau can do cool things
ElasticSearch has many capabilities once familiar with DSL
Prezi allows collaborative presentation creating
Travelport data only came from search in a specific period
Ignores seasonal or event differences
Human intervention from Revenue Management
Query Data does not match up with result data