This document summarizes a presentation by Scott Gillespie on better frameworks for travel data analysis. The presentation covers sources and uses of travel data, how to move beyond basic statistics to more insightful derivative data analysis, and examples of answering key questions about programs through concepts like fair market share, hotel clustering, variance analysis, and policy compliance measurement. Gillespie stresses combining good data with expert analysis and judgment to tell a clear story and provide useful insights for travel program stakeholders.

1. Global Business Travel Association
Travel Data: Better Frameworks,
Brighter Insights
Scott Gillespie
Managing Partner
tClara – Travel Data Made Brighter
August 2013 San Diego
V14 Cliff Notes
© 2013 Scott Gillespie
Source: epicgraphic.com
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2. Global Business Travel Association
About Scott Gillespie
One of the travel industry’s leading experts on travel
procurement, data analysis and Managed Travel 2.0
Managing Partner of tClara, an on-demand data analysis shop
specializing in the travel category
Author, “Gillespie’s Guide to Travel+Procurement”
Founder and CEO of Travel Analytics, the industry’s leading
independent travel consultancy
A.T Kearney’s global expert on travel sourcing
Author of a U.S. patent covering airline bid analysis
Inventor of the hotel clustering concept
MBA, University of Chicago; BS Arizona State
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Where we’re headed
• Intros and Interests
• Sources and Uses of Travel Data
• Boring Data Reports and Stupid Statistics
• What’s the Story? Making Good Data-driven Presentations
• Answering Key Questions with Derivative Data
– Seven practical examples
– Key concepts needed for travel data analysis
• Discuss GBTA’s KPI Resource Document
• Design Your Own Travel Dashboards
• Discussion and exercises throughout the day
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3. Global Business Travel Association
What Are The Key Differences Between Agency
And Card Data?
•Agency data
Global Data Quality
Airline Data
Agency
Card
has better
Point Of Sale
Excellent
Excellent
Ticketing Carrier
Excellent
Excellent
analytical
Origin & Destination
Good
Poor
Booking Class (e.g., H)
Good
Poor
value*
Itinerary Details
- Carrier, Flight No.
- Dept. Time/Date
- Arr. Time/Date
- Stopover Code
Good
Good
Good
Good
Poor
Poor
Poor
Poor
Amount Spent
- Base Fare
- Taxes
- Surcharges, Other Fees
- Refunds, Exchanges
Booked
Good
Fair
Fair
Poor
Paid
Fair
Poor
Poor
Fair
•Card data
has better
total spend
*Exceptions include
UATP, AirPlus, Level 3
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It’s Not Easy To Integrate Card And Agency
Data – So Why Bother?
Illustrative
Card Spend
Preferred
60%
Non-Prfd.
Booked Spend
Preferred
15%
Non-Prfd.
15%
Spend Visibility
Visible… 60%
Semi-Visible… 30%
Invisible… 10%
100%
10%
Integration
Improves Spend
Visibility
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4. Global Business Travel Association
Rating of Sources by their Uses
Best
Sources
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1st Generation Data is Easily Produced…
• Total Air Spend
• Average Ticket Price
• Price per Mile
• Average Room Rate
• Average Rental Rate
The stuff upon
which most travel
reports are built
• Top 25 Suppliers
• Top 500 Markets
• Top 25 Travelers
…But Is Boring and Nearly Useless. Why?
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5. Global Business Travel Association
They have no
context, and so
give no insight
High Value
Options and Targets
Root Causes and Context
Low Value
Lists, Statistics and Trends
Normalized Data
Consolidated Data
Source: Gillespie’s Guide to Travel+Procurement
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…It Produces High-value Derivative Travel
Data, Such As:
• Rational Airline Discounts
• Hotel Clusters
• Supplier Scenario Maps
• Price Variance Explanations
• Program Savings Options
• Clear-cut Policy Implications
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Must combine
good data
and analytics
with expert judgment
Analytical Complexity
Subject
Decision
Logic
Most Travel
BI Tools
Matter
Human
Judgment
Experts
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The Land of
Stupid Statistics
Which Cabin(s)?
Leisure or
Corporate?
Booked how far
in advance?
$257, +/- ?
Includes taxes?
What type of
travel policies?
What size
companies?
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7. Global Business Travel Association
Audience’s appetite
Presenting
Data and
Concepts
Key questions, time limit,
details, take-aways
Establishing credibility
Telling a concise story
>> Scene, characters, plot
= Situation, conflict,
resolution
Providing context is key
Clarity and brevity (not
always the same thing!)
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Revenue Management Example
Illustrative 100-seat Aircraft
X 10 Seats
X 30 Seats
X 100 Seats
$500
$900
X 70 Seats
$1,500
X 20 Seats
$1,200
X 30 Seats
$700
$400
X 40 Seats
$300
$50,000
$55,000
$72,000
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Airfare Inventory Classes
High prices help
ensure last-minute
availability
Illustrative
Low prices make
planned trips more
affordable
Airfare Inventory Booking Classes – Coach Cabin
More Flexible
Less Flexible
Higher Quality Product
Lower Quality Product
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Fare Ladder Discount Implications
Higher discounts
Illustrative
Low or no
discounts
Airfare Inventory Booking Classes
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JVs, ATI and Alliances – The Differences
JV Partners in
TATL, share profits
Delta
AirFrance-KLM
Alitalia
Czech
Have US ATI,
pricing
authority
Korean
Aeromexico
Kenya Airlines
Aerolineas
Argentina
Middle East
Airlines
Aeroflot
Saudia
Air Europa
TAROM
China Airlines
Vietnam Airlines
SkyTeam
Alliance
Members
Single point
of contact
for
contracting
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Competition causes lower airfares
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1
3
5
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Source: Scott Gillespie
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Slide Checklist
 Source?
 Timeframe?
 Definitions, acronyms
(e.g., ASM, BAR, TMC)?
 Title is clear? What
question does it answer?
 Descriptive or
prescriptive?
 What’s the takeaway?
 What are the next 2 most
likely questions a reader will
have?
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Presentation Checklist – What’s the Story?
Background – sets the
scene, why we’re here
 Conflict – the problem or
big question is…
 Approach – who, and
how we tackled the
problem
 Discovery – what we
found, how we reacted
 Ending – Answers,
insights, options,
recommendations, next
steps
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11. Global Business Travel Association
7 Key Questions and Analytical Components
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Which Airline Alliance / Hotel Chain is the best fit?
– Fair Market Share (QSI), Hotel Clusters
•
How will the AA-US merger impact my program?
– Competitive Pricing Slope, Buyer Power
•
Why did my average segment price change year over year?
– Variance analysis, quality indexes, savings definitions
•
What are my savings options?
– DAP price curve, flight durations, option mapping
•
How well are we complying to travel policy?
– Trip scoring, measuring what matters
•
Which travelers are taking on the most trip friction?
•
How good is my airline discount?
– Price benchmarking, maximum rational discount
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Fair Market Share
Delta
Delta’s FMS = 50%
United’s FMS = 50%
(assumes wing-to-wing
schedules)
100 seats a day
United
100 seats a day
Airport A
Airport B
Fair Market Share is the airline’s
expected share of seats in a market,
based on seats, schedules and
routings
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Fair Market Share
Delta’s FMS = 40%
United’s FMS = 40%
Delta
Southwest = 20%
100 seats a day
United
100 seats a day
Airport A
Airport B
Southwest
100 seats a day
Connecting
Airport
Less weight for
connections, and for
longer connections
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The answer looks like this
Alliance Coverage of our FY
2012 Top 100 City Pairs
41%
25%
28%
6%
oneworld
Star
SkyTeam
None
Source: tClara’s FMS Engine, July 2013 Flight Schedules
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13. Global Business Travel Association
Slide are detailcompeting 44)
Clusters 1 groups of (Arial
hotels
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The answer looks like this
Chain Coverage of Our FY 2012 Top 100
U.S. Hotel Clusters
22%
21%
17%
15%
14%
7%
4%
Source: TRX Hotel Cluster Analysis, July 2013 Property Database
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Research shows how airfares correspond to
the number of carriers in a market
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We have $8MM in markets that will likely
see an increase in airfares. FY14 budgets
should be increased by x-y% or $$$-$$$K
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15. Global Business Travel Association
Measuring Buyer Power in a City Pair
Buyer’s Leverage over Carrier
1
5
10
5
1
Low
Moderate
High
Moderate
Low
Less
than
15%
1535%
3665%
6685%
Over
85%
Carrier’s Fair Market Share (Capacity)
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Conclusion: discounts will shrink
Pre-merger score = 4.8
Post-merger score = 3.9
Low = 1, High = 10
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AA+US becomes our largest potential
supplier. Discounts may improve slightly
Pre-merger, USA markets
Post-merger, USA markets
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How will the merger affect our program?
Oneworld will become our largest alliance by capacity
Pre-merger
Post-merger
Analysis of FY12’s top 500 global city pairs using tClara’s July 2013 FMS engine
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Here’s why our ASP is up 15% YOY
Changes in US Domestic Market Airfare Price Drivers H1 2102 vs H1 2013
Higher
Fares
Lower
Fares
Uncontrollable
Controllable
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Variance Analysis Checklist
 Relevant time periods? (e.g. Year over Year)
 Relevant unit of measure? (e.g., Avg.
Domestic Segment Price, Coach cabin)
 Price and Volume are separated?
 Primary root causes and correlations are
used for context?
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How do you measure savings?
No clear standard, but most popular seems to be
(New Unit Price – Old Unit Price) x Purchase Volume
Ex: ($270 - $250) x 10,000 tickets
What is a “Unit”?
- All tickets?
- Domestic US?
What is a “Price”?
- Negotiated?
- Average Booked?
- Average Paid?
- Coach Cabin?
- Excluding one-way,
circle trips and open
jaws?
What is the “Volume”?
- All airlines, or just
contracted?
- or Current time period?
- Tied to “Old” time period?
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(New Price – Old Price) x New Volume = (Savings) or Loss
Show Price and Volume Effects
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Savings Report Checklist
 Consistent unit of measure? (e.g., Avg.
Domestic Segment Price, Coach cabin)
 Price and Volume are separated?
 Consistent treatment of price? (e.g., booked,
or negotiated, or paid)
 Can change in price be drilled down into a
change in product mix? (e.g., Old had 15% of
all tickets in Y; New has 30% of all tickets in Y.
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An even better way – use an Option Map
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Traveler
Resistance
D
B
Loss
oneWorld
Star + DL
Savings
Travel Sourcing Options Map
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Cabin Policy Option Map
Potential
Savings, 100%
Compliance at
8 Hours
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21. Global Business Travel Association
The optimization problem
Trip Friction
• Lost productivity
High
• Reluctance to travel
Total Trip Cost
Costs
• Recruiting, retention
problems
•Personal frustration,
stress on home life,
health issues
Trip Cost
None
Harsh
Travel Policy
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Better Management of Salespeople
Bowden, Christina
Barton, Elsie
Goldstein, Gretchen
Watts, Tim
Merritt, Shirley
Dougherty, Kristine
Steele, Eric
May, Alex
Jones, William
Bender, Hazel
Chung, Donald
Underwood, Harvey
Teague, Wesley
Hamilton, Elsie
Walsh, Marcia
Vick, Franklin
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82
78
77
77
66
60
55
50
48
43
41
35
29
25
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Reduce trip
friction:
- fewer trips?
- better trips via
policy
exceptions?
Encourage
more
travel
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22. Global Business Travel Association
tClara offers trip friction benchmarking
Avg. Trip Friction scores
by trip type
Illustrative
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tClara is looking for benchmark volunteers
Main causes of Trip
Friction?
Illustrative
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Potential Policy Elements to Measure?
Pay
1.
Pay with
Corp Card
Air
1.
Proper
Cabin
2.
Lowest
Logical
Fare
3.
Days in
Advance
4.
Use pref’d
carrier
5.
Book via
pref’d
channel
Hotel
1.
Proper Tier
2.
Car
1.
Pref’d
supplier
2.
Proper
Class
3.
Full tank
at return
4.
Decline
insurance
Pref’d Hotel
3.
4.
Book via
pref’d
channel
Booked vs.
Billed Rate
M&M
1.
Per Diem
2.
Min.
Receipt $
Which ones really matter?
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24. Global Business Travel Association
TMC booking data is a rich source
for measuring policy compliance
Source: Travel GPA
8/3/2013
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Gillespie’s Guide to Travel+Procurement
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Even better,
explain actions
and
consequences.
What’s the
story?
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Policy Compliance Checklist
 Too many metrics? (Measure what really
matters)
 Metrics must be practical to measure
(consistent data sources)
 Account for exceptions granted
 Tie compliance to safety or savings
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Expected Profit Margin and Share Shift,
NOT Spend, Drives Discounts
More precisely, the credible threat or promise drives %
Y
Class
Disct.
Curve
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Scenario-based Negotiations
Rank by Preferred Supplier
Savings Scenario
Buyer’s
Scenario
Savings
(000)
Delta’s
Net
Spend
(000)
1
United as Primary,
then Delta and US
Air as Secondaries
$500
$1,200
2
United and Delta as
$400
Co-Primaries, then US
Air
$1,800
3
Delta as Primary,
$2,300
then UA and US as
Secondaries
$350
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27. Global Business Travel Association
Slide 1 detail (Arial 44)
Poor
Compliance
Measure Hotel Compliance
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Cluster Modeling Reveals Relevant Chain-wide
Capacities
Dynamic pricing requires
this type of modeling
Hotel Chain
A:
Offers
Chainwide
Discount
of
B:
Chain’s
Share of
Buyer’s
Hotel
Footprint
AxB=
Capacityadjusted
Discount
Choice
12%
8%
1.0%
Hilton
10%
14%
1.4%
Hyatt
10%
15%
1.5%
IHG
15%
6%
0.9%
Marriott
8%
24%
1.9%
Starwood 9%
20%
1.8%
Worst
Best
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A Man Walks Into A Hardware Store And
Asks For a ¼” Drill Bit
But what he
really wants
is a ¼” hole
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Key Program
Metrics
Key
Performance
Indicators
Indicator of Results
Indicator of Actions
Less Controllable
More Controllable
Descriptive
Prescriptive
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29. Global Business Travel Association
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Thank You!
Scott Gillespie
(O) 440 248 4111
scott@tclara.com
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