Corporate Travel Data Workshop - Key Concepts and Applications

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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

[email protected]