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ARC

  • Chuck Thackston, Managing Director – Data Research and Analytics
  • Vincent Smith, Senior Data Analyst and Statistician

Data provided is one full year (2011) of global ticket transactions hosted by ARC in the COMPASS® database. The ticket sale transactions will include data from travel agencies as well as from direct airline sales. While not all airlines’ data is included it is estimated that over 75% of the world’s issued airline tickets are included in the dataset provided. The data will include “ticket-level” data that includes fare information for the entire ticket. It will also include (in a separate dataset) all the “coupon-level” data that includes the routing of the journey and all the flights included in the ticket. Additionally, reference tables (i.e. currency conversion) will be provided to allow normalizing the data for financial analysis. In total the estimated ticket count will be over 500 million ticket transactions and over 1.5 billion individual flight coupons.

This dataset can be used to evaluate any number of trends in the passenger air market including:

  • Trend analysis by region or airport
  • Impact of external events (GDP changes, weather disruptions, etc.) on ticket sales
  • Recovery of travel into an area over a local event or disruption
  • Advance purchase trends/changes over time
  • Seasonality effects on ticket purchases to specific regions or areas
  • Country-specific sales trends to other regions or countries
  • etc.

Some specific models or studies that could be done with this data include:

  • What are some under-served regions of the country (or world) that could benefit from additional air travel capacity?
  • Is there a hub airport that is getting significantly underutilized and travelers could benefit from considering connecting there more frequently?
  • Are there significant seasonality differences in BOTH travel and advance purchase in different regions of the world?
  • Is there a logical grouping of markets that can be used for analysis that have similar profiles … other than just number of travelers? This could potentially identify test markets for airlines’ new products or similar markets for A-B level testing at an operational level.
  • Does a schedule change (moving a flight earlier or later) have a material difference on the number of passengers?
  • If a flight has a chronic history of being “late”, does this have a short-term or long-term impact on purchase behavior? [ Note that this would require correlation with operational performance data from FlightStats. ]