Airline analytics for the 21st century

August 2016

Airline analytics for the 21st Century

Faical Allou – Skyscanner

[email protected]

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

Challenging current airline planning with 21st century data capabilities

• 21st century data capabilities

Planning data available to airlines is collected at the booking and check-in; the overall shopping and travel

experience is virtually ignored

Inspiration ShoppingBooking / Ticketing

Check-in BoardingTo the airport Flight

DisembarkmentBaggage

ClaimFrom the airport

At the destination To the airport Check-in Boarding Flight

DisembarkmentBaggage

ClaimFrom the airport

Feedback: complaints,

reviewCompensations

Changes Cancelations

Traditional data Traditional data

Initial data extension suggestion for the purpose of this presentation

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

Challenging current airline planning with 21st

century data capabilities

• The search data, studying behaviour before the PNR is created

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


LON

MIA

ORL

LAX

CHI

SFO

YTO

PAR

FLL

CUN


LON

MIA

LAX

ORL

SFO

CHI

YTO

PAR

CUN

LAS


BKK

DPS

HKG

TPE

TYO

KUL

SEL

LON

HKT

MNL


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

There are significant differences in how much the actual demand is satisfied among large metropolitan areas and an overall degradation over time


Correlation searches/exits

top 50 top 50-100 Linear (top 50) Linear (top 50-100)



• The geolocation, going beyond the PNR

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


0%

5%

10%

15%

20%

25%

30%

35%

40%

% “leaked” traffic from the top 100 cities in Skyscanner user base

Total

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 Los Angeles

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




• Up-to-date decision making

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

Airline analytics for the 21st century

Data & Analytics

Transcript of Airline analytics for the 21st century