Suresh Acharya, vice president of product development, JDA Software explores how the emergence of advanced price elasticity models and readily available comp-set price shop data has driven this shift from a “yieldable” to a “priceable” approach.

1. Revenue Management: Yieldable to Priceable…
Suresh Acharya, JDA Software
September 13, 2012

2. Topics
Traditional Yield Management
• History
• Assumptions
• Shortcomings
Price Optimization
• New Reality
• Concepts
• Evolution
What next??
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

3. What is Revenue Management?
Selling the right product
to the right customer
at the right time
for the right price
through the right channel
Revenue Management is a process of maximizing
revenue from a “perishable” product through a
combination of pricing and inventory control.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 3

4. What is Revenue Management?
Perishable Products or Services
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 4

5. History Lesson: AA & People Express
The Challenger:
People Express
• Product - Full-service network carrier
• Fare Structure - Regulated oligopoly
• Average Cost - 8.9¢ per Available Seat Mile
• Average Yield - 12.3¢ per Revenue Passenger Mile
The Champion:
American Airlines
• Product - Low cost, no-frills
• Fare Structure - Rock-bottom
• Average Cost - 5¢ per Available Seat Mile
• Average Yield - 7.2¢ per Revenue Passenger Mile
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

6.  Because they were able to underprice us at will
 All they needed to take away from us was
that marginal traffic above break even
 All you have to do is take away
a few seats on every flight and the guy is dead
“The day…American Airlines came at us with Ultimate
Super Savers…was the end of our run.”
““What changed? Nothing changed at our company,
but our competitors used widespread yield management in every
one of our markets, and they pushed us straight into bankruptcy.”
– Don Burr, CEO, People
Express
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 6

7. The Birth of Revenue Management
First Class
First Class
Extra Revenue
Fare $60
Economy
Fare $120 $60
Fare $100 $40
Fare $80 $20
Fare $60 $0
30 Spoiled Seats Fare $50
Fare $40
} passengers
unwilling to pay even $60
• 1 price sells 105 economy seats • 6 prices sell 126 economy seats
• $6,300 revenue • $9,420 revenue

8. Assumptions of Traditional RM
Traditional RM worked best when:
– Prices were pre-determined
– Unconstrained demand was often
larger than capacity
– Booking classes were well-fenced
(e.g., a B-class customer would never
purchase a Q-class ticket)
– Competition did not heavily influence
demand
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

9. Prices: Flexible but they Change
Dynamically
Price-sensitive customers can now compare the prices of competing products.
In a sense, they know the price of the capacity in the market.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

10. Hurdle Rates vs. Optimal Rates
Traditional RM But even on
counsels to skim dates for which
the cream from sell out is not
congested dates expected, prices
should none the
and free-sell at less remain
your lowest rates rational with
on low demand respect to the
dates. market.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

11. Access to Competitor Data
The Rise of the Internet…
…driving Unprecedented Price Transparency
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

12. The Assumptions of Traditional
RM Are Eroding
Traditional RM worked best when:
 Prices were pre-determined
 Unconstrained demand was often larger than capacity
 Booking classes were well-fenced (e.g., a B-class
customer would never purchase a Q-class ticket)
 Competition did not heavily influence demand
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 12

13. Yielding the sought after….
Are the outcomes desirable?
RM systems tend to restrict the availability of “low rate”
business, but this is often business that is heavily
promoted by chains…
…so having paid to obtain the demand, should hotels
really be shutting it out?
Instead of restricting the availability of low yield products, use
price directly to “organically” lower demand
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

14. Competitor Information is an
Afterthought
A typical example of a BAR RM process might provide users with two pieces
of data…but there are two significant problems with this approach
Arrival Date: Mar 17 LoS 1 LoS 2 LoS 3 LoS 4 LoS 5 LoS 6 LoS 7+
RMS BAR Recommendation $120 $140 $120 $110 $110 $110 $110
Competitor BAR Recommendation $98 $135 $160 $126 $119 $115 $98
RM is “blind” to the market It doesn’t solve the core problem
• “RMS Recommendation” is prepared • It leaves the final pricing decision up
without insight into competitor rates to the end user
• Competitor intelligence is brought in • It can leave large & dangerous gaps
for “evaluation”, after the fact between “RMS” and “Market” rates
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

15. Complexity versus Usability
A good revenue management process in today’s
dynamic environment is one that can be interpreted
and completed reliably by the end user.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

16. Price Sensitive Forecasting
It is critical to understand that
Demand is a Function of Price
Promotions 25% 20%
Pricing -8%
Seasonal Lift Spring Peak Summer Peak Christmas
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

17. Competitive Analytics
Which competitors are your true competitors?
How should you react to a competitor’s price changes?
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

18. YM when Demand Exceeds Capacity
Hotel RM systems work by By understanding the inventory
predicting future demand, constraints, a RM algorithm decides
based on booking history and which subset of the remaining bookings
current booking trends we should “yield out”, or displace
Excess
Demand
Hotel Capacity
Rooms Sold
This process works well on nights when
demand outstrips supply, but offers little
insight on nights when it doesn’t
Arrival Date
Days Left to Arrival Date
Legend: Observed Demand Unconstrained Demand Hotel Capacity
Price Optimization recommends rates at which profits are maximized to achieve
a more optimal return on perishable inventory and capital-intensive assets.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 18

19. Finding the Right Price
Capacity At a current reference price of
200 Seats $67, this OD has demand for 164 Price optimization identifies that
seats, a revenue of $10,988 the optimal price is $9 higher than
the reference price
Seat Rate Revenue
Seats Sold
Price: $49 Price: $76 Price: $109
Dmd: 200 seats Dmd: 150 Seats Dmd: 80 Seats
Rev: $9,800 Rev: $11,400 Rev: $8,784
$49 $67 $76 $109
Price
Plan Reference
Legend: Price Sensitive Demand Revenue
Capacity Price
Price Optimization recommends rates at which profits are maximized to achieve
a more optimal return on perishable inventory and capital-intensive assets.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 19

20. Elasticities and Price Optimization
Elasticity values less than -1: Elasticity values between -1 & 0:
Elastic, very price-sensitive. Inelastic, not price-sensitive.
Optimizer wants to lower the Optimizer wants to raise the price
price below market reference above market reference price
price
Elastic Market Segment: Inelastic Market Segment:
Optimal Price to Left of Market Reference Price Optimal Price to Right of Market Reference Price
10 £600 6 £600
9
£500 5 £500
8
7
£400 4 £400
Revenue
Demand
Revenue
Demand
6
5 £300 3 £300
4
£200 2 £200
3
2
£100 1 £100
1
0 £0 0 £0
-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
( p - mktrefprice ) / mktrefprice ( p - mktrefprice ) / mktrefprice
Demand Revenue Demand Revenue
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 20

21. Price Optimization
Price Sensitivity
Inventory
Competitors’
Price Sensitive Forecasts Information
Optimization
Optimal Price Recommendations
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL

22. Is Price Optimization for Everyone?
 Segmentation well-defined and well-fenced
 Unavailability of Competitor Data
 Low Consumer Visibility and B2B
 Demand Mostly Exceeds Capacity
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL 22

23. The Evolution
Maturity
Price
Optimization
Competitive
Analytics
Price
Price-Sensitive
Forecasting Optimization
Traditional Yield
Management
Inventory
Optimization
Inventory
Management
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Benefits
23

24. The Next Frontier?
 Customized Pricing
 Big Data
 Unstructured Data
 Customer Choice
 Real-time Pricing and Decision Making
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Benefits
24

25. Thank You
For more info, contact info@jda.com
www.jda.com/revenuemanagement