executive summary:
1 - There is an increasing gap between where users want to go and where airlines fly (i.e. planning based on flown data is increasingly irrelevant. => Airline should incorporate the "shopping" data in their process)
2 - Users are increasingly choosing to fly from alternate airports (i.e. planning based on departure airport is increasingly misleading. => Airline should take into account travellers' true origin)
3 - Planning done 10 months in advance is too early to capture changes of trends (i.e. in our fast paced world, users' decision last year shouldn't be used to plan this year. => Airlines should take the steps to move the entire ecosystem into shorter and more frequent planning periods)
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
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
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
