Dynamic pricing is practiced across industries to personalize prices based on customer data analytics. Pioneered by airlines in the 1980s as revenue management, it involves segmenting customers, adjusting prices based on demand forecasts, and unbundling/rebundling services. Tom Bacon has applied these techniques at several airlines, leading to increased ancillary revenue and competitive fare positioning through integrated pricing, merchandising, and operations.

1. DYNAMIC PRICING
Tom Bacon
Airline Strategist
April, 2015
Lessons from Airline Revenue Management

2. DYNAMIC PRICING
 “Dynamic pricing” is practiced across the globe in a
variety of industries
 Personalized pricing strategies based on sophisticated analytics and Big Data
 Reduced “consumer surplus” as every individual customer pays close to his
personal value
 Facilitated by the internet and e-tailing
 Pioneered by airlines in the ‘80’s in the form of
Revenue Management
 A multitude of rapidly changing fares exploiting elasticities of different segments
 Airlines continue to perfect the craft. Key features are:
 Creative Market Segmentation
 Big Data Analytics
 Competitive Positioning
 Unbundling and Rebundling
 Merchandising Sell-up

3. Y S O
CREATIVE MARKET SEGMENTATION
 Elasticity-based segmentation techniques
recognize:
 Huge range of elasticities across customers – and the
same customer at different times
 Reliance on behavioral “proxies” correlated with
actual elasticities
 Airlines may have 100+ different fares paid by
travelers on the same flight
 Fare rules designed to keep more price-inelastic
travelers from paying lower fares
 Forecast systems predicting demand in $15-$25
increments
 Ancillary fees exploiting observed lower price
elasticity of certain additional features
2 1 2
2 1 2 1
%
%
d
P D
P D
Q
E
P
Q Q P P
E
P P Q Q



 
 
 
Price
Demand
159
129
99
189
159
129
419
349
279
Executive
Fully Flexible
Biz Discount
Price with extras
Best Price
Price Inelastic
Price Elastic
Fare levels

4. BIG DATA ANALYTICS
 Airlines forecast demand for each fare level by flight
up to a year in advance – a 50 aircraft fleet requires
a million forecasts at a time
 Seasonal, day-of-week and time-of-day patterns
 Regression models tapping into 3-5 years of history
 Allocations designed to optimize an entire network
 Forecasts updated automatically each night
 Allocations based on both forecast demand levels & uncertainty
 Real-time inventory adjustments as bookings come in
 Just as critically, airlines have developed processes
to actively manage the models
 Reports, alerts, cross-functional meetings; metrics,
accountability
 Consumer products companies and e-tailers
experience similar product proliferation (SKU’s) and
rely heavily on computer demand models
 Real-time updates based on actual orders across disperse
outlets
$0.00
$50.00
$100.00
$150.00
$200.00
$250.00
$300.00
$350.00
$400.00
$450.00
$500.00
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
EMSR ($400)
EMSR($300)
( ) u h l
o u h
C f f
F n x
C C f

  


5. COMPETITIVE POSITIONING
 Airlines have historically monitored competitors’ fares
multiple times a day – matching low fares within 3 hours
 Reliance on third party distribution channels commoditized air travel
 More recently, competitive analysis has become more
sophisticated
 New investment in their own websites; changing content on third
party sites
 New systems calculate elasticity for a fare premium or discount
 “Screen scraper” systems monitor fares on multiple outlets
 Business rules adjust allocations based on demand and fare levels
 More generally, however, airlines are advised to adopt a
proactive, internally-consistent, stable pricing strategy
 Based on demand for its unique network, product, brand, etc.

6. UNBUNDLING AND
REBUNDLING
 Unbundling further exploits elasticity
differences among customer segments
 “Pay for what you use”
 Bag fees add revenue & reduce handling costs
 New value added services can make trip easier for
those willing to pay more
 Spirit Airlines now reports ancillary @ 45% revenue
 Rebundling drives new differentiation; can
target certain segments for further upsell
 Frontier was first in U.S. to launch “branded fares”
Service Fee Service Fee
1st bag $30 Same Day Stby $50
2nd bag $25 Priority Bdg $5
Chng fee $150 Resrved Seat $5
Onbd Meal $8 Double miles $8
Video $6 WIFI $8
Drink $6 Big Seat $35

7. MERCHANDISING SELL-UP
 With variety of fares and fees, customers have new
choice – but can become overwhelmed
 Transparency is more important
 Third party distribution is particularly weak in conveying rules & fees
 Sell-up is maximized by the right offers at the right time
in booking process
 Personalized offers help individual customers navigate
new choice
0
20
40
60
80
100
120
50
120
190
Frequency of Fare
Level
Inspire Research Plan Book Sell-up Travel Share

8. CULTURE
 Organization must match sophistication of forecast
system; many other elements to consider…
 Dynamic pricing cannot be silo’ed
 Requires collaboration across Pricing, Marketing,
Distribution, e-commerce, Loyalty, and Operations
 Speedy response to market requires a flat organization
 Organization must be learning oriented
 Constant, disciplined hypothesis testing
 Continual questioning of conventional wisdom
 Open to new ideas
 To keep everyone aligned, metrics must be
similarly aligned across functions
System Sophistication
Airline
Sophis-
tication

9. DO YOU PRICE DYNAMICALLY?
 How granularly do you segment demand?
 Do you understand the elasticity of different segments?
 Do you adjust prices based on forecast demand by market segment?
 Do you update the forecast algorithms frequently based on the latest data?
 Do you factor in forecast accuracy in decision making? What is the relative cost of over- vs.
under-forecasting?
 Do you distinguish between “actual” underlying demand and “observed” demand based on
supply, merchandising, and pricing decisions?
 Do you regularly test for higher fare demand?
 Do you know your competitors’ pricing?
 Can you predict their response to your pricing actions?
 Can you charge separately for different features/services?
 Can you personalize offers?
 Do you integrate Pricing with the rest of the organization? How linked are
pricing and merchandising?
 Do you have a culture that supports Big Data Analytics?

10. DO YOU PRICE DYNAMICALLY?
 Tom Bacon has applied Big Data Analytics and Merchandising to
support dynamic pricing at numerous airlines
 He has led RM and commercial initiatives as an executive at 5 carriers
 Restructured carriers in changing markets or facing new competition
 All sectors: global legacy carriers, LCC’s, regionals, and niche carriers
 Global: North America, Asia, Middle East, Europe
 Assessed RM systems & modules; and airline’s success in RM management
 Implemented new RM organizational structure and competitive fare tracking at Gulf airline
 Evaluated competing RM systems in context of an airline merger
 Recommended process changes at European carrier
 Persistent advocate for cross-functional collaboration between RM and other departments
 Quadrupled ancillary revenue at Frontier Airlines
 Launched first branded fares in U.S.
 To implement dynamic pricing in your organization please contact
Tom at tom.bacon@yahoo.com