Airline analytics for the 21st century

1. August 2016 Airline analytics for the 21st Century Faical Allou – Skyscanner faical.allou@skyscanner.net

2. Amazon “the world’s most customer centric company” is exploring delivering products to dispatch centre before they are bought to reduce delivery time … while airline still plan based on last year data In deciding what to ship, Amazon said it may consider previous orders, product searches, wish lists, shopping-cart contents, returns and even how long an Internet user’s cursor hovers over an item. Airlines plan based on estimated traffic between airports flown last year

3. Challenging current airline planning with 21st century data capabilities • 21st century data capabilities

4. Planning data available to airlines is collected at the booking and check-in; the overall shopping and travel experience is virtually ignored Inspiration Shopping Booking / Ticketing Check-in BoardingTo the airport Flight Disembarkment Baggage Claim From the airport At the destination To the airport Check-in Boarding Flight Disembarkment Baggage Claim From the airport Feedback: complaints, review Compensations Changes Cancelations Traditional data Traditional data Initial data extension suggestion for the purpose of this presentation

5. Shopping data is big data, but it is fairly “structured” and could be immediately available to most airlines Booking Ticket Shopping MIDT BSP Search BoardingDCS

6. Challenging current airline planning with 21st century data capabilities • The search data, studying behaviour before the PNR is created

7. Most searched destinations are not always the most travelled since conversion depends on service and price • The volume of searches is closer to the actual demand than the traffic itself • Traffic is a subject to availability, price and convenience and as a result the destination where people fly are not always where they wanted to travel • To matching the offer with the demand, travel supplier need to understand the unconstrained demand and not focus on traffic from last year • When rankings match perfectly the correlation factor reaches 1; in large cities such as NYC, LON and SIN the correlation is above average (NYC:0.99, LON:0.99, SIN: 0.995) Source: Travel Insight Destination travelled* NYC BCN DXB BKK AMS ORL AGP ROM DUB TCI Destination searched NYC BCN BKK AGP DXB AMS ORL ROM PAR DUB From LON Destination travelled* LON MIA ORL LAX CHI SFO YTO PAR FLL CUN Destination searched LON MIA LAX ORL SFO CHI YTO PAR CUN LAS Destination searched BKK DPS HKG TPE TYO KUL SEL LON HKT MNL Destination travelled* BKK DPS HKG TPE SEL KUL TYO HKT LON MNL From NYC From SIN Note: based on the exit In the top 10 travelled but lower in rank In the top 10 travelled and higher in rank Not in the top 10 travelled Not in the top 10 searched but in the top 10 travelled 12th searched 11th searched

8. There are significant differences in how much the actual demand is satisfied among large metropolitan areas and an overall degradation over time Source: Travel Insight Correlation searches/exits top 50 top 50-100 Linear (top 50) Linear (top 50-100)

9. Challenging current airline planning with 21st century data capabilities • The geolocation, going beyond the PNR

10. Travelers increasingly chose not to fly from their “home” airport for convenience and price while airline can only see their departure airport and thus misrepresent actual demand • Leaked traffic is traffic from a city that doesn’t originate nor end in the IATA associated city code (e.g. travellers from Paris travelling from BRU) • Every city has a home airport (or group of home airports as defined by IATA city codes) where airline assume traffic originates • Airlines plan their network and revenue management on the basis of this definition and monitor performance “airport-to-airport”. No planning system in the industry is tailored to give insight into the “true” competition • Users increasingly chose alternate airports to fly which questioned current processes • Competition between origin/destination airports is widely misunderstood Source: Travel Insight 0% 5% 10% 15% 20% 25% 30% 35% 40% % “leaked” traffic from the top 100 cities in Skyscanner user base Total

11. As way of example: Traditional O&D based market data allocates incorrectly 31% of the demand from Las Vegas area to other airports such as San Francisco and LosAngeles SFO MNL BKK LON LAX SEL Las Vegas, NV +North Las Vegas, NV + Henderson, NV • Traditional Market data allocate the demand at the first departure airport ignoring the true origin of the travellers • While not perfect IP based geolocation gives a better view of the true origin of the demand • IP based geolocation gives a better understanding of where users live and work than mobile networks Preferred departure airport for our users in Las Vegas, NV, North Las Vegas, NV and Henderson, NV when travelling international Source: Travel Insight

12. Challenging current airline planning with 21st century data capabilities • Up-to-date decision making

13. Capacity planning is typically done 10 months in advance with very limited capabilities to adapt to the current demand of travellers Travel Searches for Travel in July 2016 • From BER, TCI was the 38th most requested destination for travel in Jul 2015 with a conversion rate of 10% • Airlines in October and November planned their network for the summer, published and started selling • For July 2016, TCI was the 24th most requested destination from Berlin (+14 ranks!) but because the demand for TCI came late (started in APR 2016) the capacity was not sufficient and the resulting conversion rate dropped to less than 8%; compared to the 18% conversion last year there is likely 25% of the demand that could not be satisfied • Next year, on the basis of the traffic, (which was constrained by 25%) the capacity will likely be limited once again