better frameworks, brighter insights · trip cost total trip cost trip friction • lost...
TRANSCRIPT
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Global Business Travel Association
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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
2 Source: epicgraphic.com
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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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What Are The Key Differences Between Agency And Card Data?
Airline Data Agency Card
Point Of Sale Excellent Excellent
Ticketing Carrier Excellent Excellent
Origin & Destination Good Poor
Booking Class (e.g., H) Good Poor
Itinerary Details
- Carrier, Flight No. Good Poor
- Dept. Time/Date Good Poor
- Arr. Time/Date Good Poor
- Stopover Code Good Poor
Amount Spent Booked Paid
- Base Fare Good Fair
- Taxes Fair Poor
- Surcharges, Other Fees Fair Poor
- Refunds, Exchanges Poor Fair
Global Data Quality •Agency data
has better
analytical
value*
•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?
Card Spend
Bo
oke
d S
pen
d
Preferred Non-Prfd.
Pre
ferr
ed
N
on-P
rfd.
Illustrative
Semi-Visible… 30%
15%
15%
60%
10%
100%
Spend Visibility
Integration
Improves Spend
Visibility
Invisible… 10%
Visible… 60%
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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
• Top 25 Suppliers
• Top 500 Markets
• Top 25 Travelers
The stuff upon
which most travel
reports are built
…But Is Boring and Nearly Useless. Why?
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They have no context, and so give no insight
Consolidated Data
Normalized Data
Lists, Statistics and Trends
Root Causes and Context
Options and Targets
Low Value
High Value
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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Human
Judgment
Decision
Logic
Analytical Complexity
Subject
Matter
Experts
Most Travel
BI Tools
Must combine
good data and analytics with expert judgment
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The Land of Stupid Statistics
Which Cabin(s)?
Booked how far
in advance?
Leisure or
Corporate?
What size
companies?
What type of
travel policies?
$257, +/- ?
Includes taxes?
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Presenting
Data and
Concepts
Audience’s appetite
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
$50,000
$900
X 30 Seats
$400
X 70 Seats
$55,000
$500
X 100 Seats
$1,500
X 10 Seats
$1,200 X 20 Seats
$700
X 30 Seats
$300 X 40 Seats
$72,000
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Airfare Inventory Booking Classes – Coach Cabin
Illustrative
Airfare Inventory Classes
High prices help ensure last-minute
availability
Low prices make planned trips more
affordable
Less Flexible
Lower Quality Product
More Flexible
Higher Quality Product
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Airfare Inventory Booking Classes
Illustrative
Fare Ladder Discount Implications
Higher discounts
Low or no
discounts
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JVs, ATI and Alliances – The Differences
Delta AirFrance-KLM
Alitalia Czech Korean
Aeromexico
Aerolineas
Argentina
Aeroflot
Air Europa
China Airlines
Kenya Airlines
Middle East
Airlines
Saudia
TAROM
Vietnam Airlines
JV Partners in
TATL, share profits
Have US ATI,
pricing
authority
SkyTeam
Alliance
Members
Single point
of contact
for
contracting
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Source: Scott Gillespie
Competition causes lower airfares
1 1 3 5 1
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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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7 Key Questions and Analytical Components
• 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 is the airline’s expected share of seats in a market,
based on seats, schedules and routings
Airport A Airport B
Delta
100 seats a day
United
100 seats a day
Fair Market Share Delta’s FMS = 50%
United’s FMS = 50% (assumes wing-to-wing
schedules)
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Fair Market Share
Airport A Airport B
Delta
100 seats a day
United
100 seats a day
Delta’s FMS = 40%
United’s FMS = 40%
Southwest = 20%
Connecting Airport
Southwest
100 seats a day
Less weight for connections, and for longer connections
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The answer looks like this
25% 28%
41%
6%
oneworld Star SkyTeam None
Alliance Coverage of our FY
2012 Top 100 City Pairs
Source: tClara’s FMS Engine, July 2013 Flight Schedules
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Slide 1 detail (Arial 44) Clusters are groups of competing
hotels
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The answer looks like this
22% 21%
17% 15% 14%
7% 4%
Chain Coverage of Our FY 2012 Top 100
U.S. Hotel Clusters
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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Measuring Buyer Power in a City Pair
Carrier’s Fair Market Share (Capacity)
Less
than
15%
15-
35%
36-
65%
66-
85%
Over
85%
Buyer’s Leverage over Carrier
Low Low Mod-
erate
Mod-
erate
High
1 1 5 5 10
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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?
- Coach Cabin?
- Excluding one-way,
circle trips and open
jaws?
- All airlines, or just
contracted?
What is a “Price”?
- Negotiated?
- Average Booked?
- Average Paid?
What is the “Volume”?
- Tied to “Old” time period?
- or Current time period?
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Show Price and Volume Effects
(New Price – Old Price) x New Volume = (Savings) or Loss
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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
Loss Savings
B
D
Travel Sourcing Options Map
Star + DL
oneWorld
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Cabin Policy Option Map
Potential Savings, 100% Compliance at
8 Hours
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The optimization problem
Travel Policy
None Harsh
High
Costs
Trip Cost
Total Trip Cost
Trip Friction
• Lost productivity
• Reluctance to travel
• Recruiting, retention
problems
•Personal frustration,
stress on home life,
health issues
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Better Management of Salespeople
Bowden, Christina 84
Barton, Elsie 82
Goldstein, Gretchen 78
Watts, Tim 77
Merritt, Shirley 77
Dougherty, Kristine 66
Steele, Eric 60
May, Alex 55
Jones, William 50
Bender, Hazel 48
Chung, Donald 43
Underwood, Harvey 41
Teague, Wesley 35
Hamilton, Elsie 29
Walsh, Marcia 25
Vick, Franklin 20
Encourage
more
travel
Reduce trip
friction:
- fewer trips?
- better trips via
policy
exceptions?
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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 Air Hotel Car M&M
1. Pay with
Corp Card
1. Proper
Cabin
2. Lowest
Logical
Fare
3. Days in
Advance
4. Use pref’d
carrier
5. Book via
pref’d
channel
1. Proper Tier
2. Pref’d Hotel
3. Book via
pref’d
channel
4. Booked vs.
Billed Rate
1. Pref’d
supplier
2. Proper
Class
3. Full tank
at return
4. Decline
insurance
1. Per Diem
2. Min.
Receipt $
Which ones really matter?
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TMC booking data is a rich source for measuring policy compliance
Source: Travel GPA
48 8/3/2013 48 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 Savings
Preferred Supplier 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 Co-Primaries, then US Air
$400 $1,800
3 Delta as Primary, then UA and US as Secondaries
$350 $2,300
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Slide 1 detail (Arial 44)
Measure Hotel Compliance
Poor Compliance
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Cluster Modeling Reveals Relevant Chain-wide Capacities
Hotel Chain A: Offers Chain-wide Discount of
B: Chain’s Share of Buyer’s Hotel Footprint
A x B = Capacity-adjusted 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% Best
Worst
Dynamic pricing requires this type of modeling
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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
Descriptive
More Controllable
Prescriptive
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Thank You!
Scott Gillespie
(O) 440 248 4111