Revenue Management. Louis Busuttil Consultant [email protected] Frequently asked questions about revenue management. Why sell more seats than you have seats available for sale? Why charge so many different prices for the same product?
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economy class booking curve
Days prior to departure
elasticWhy charge so many different prices for the same product?
price/demand relationship, market segmentation, rev = area under curve
CAPACITYWhy do your prices have so many conditions attached?
People will still shop around for the lowest price they can get, but you can force them to progressively “trade up” to the more expensive seats as you approach day of departure.
Revenue mgt is the integrated control of capacity and price
- pioneered by mega airlines who could fund the R&D costs, rev mgt systems can now be bought “off the shelf”, but systems-integration, data-quality, and business-process- improvement remain major challenges
- other industries following (hotel, car rental, broadcasting…)
- a must-have for high-fixed-cost, low-margin, price-segmentable businesses
“The airline business is won and lost at the margins” Don Burr
“An airline applying rev mgt techniques head-to-head with one which doesn’t, can enjoy a 10% rev advantage” Peter Belobaba
300 seats: full fare at $1,900; discounted at $1,300; would you prefer:
a) 50 full fare and 250 discounted, or
b) 190 full fare and 50 discounted, or
c) 135 full fare and 135 discounted?
The one that makes you most money (c) is not necessarily the one that gives you the highest average yield (b) or the highest load factor (a).
a) (50*1900)+(250*1300)=$420,000 420000/(50+250)=$1,400 ((50+250)/300)*100=100%
b) (190*1900)+(50*1300)=$426,000 426000/(190+50)=$1,775 ((190+50)/300)*100=80%
c) (135*1900)+(135*1300)=$432,000 432000/(135+135)=$1,600 ((135+135)/300*100=90%
Revenue management processes need to be supported by systems to help with passenger demand forecasting and fare mix optimisation.
There are many human biases in forecasting:
- treat more easily available / recallable data as more significant
- confidence in uncertain estimates often higher than warranted
- gamblers fallacy: assume that random processes ‘self-correct’
- overemphasise conclusions from small samples: anecdotal evidence
- distorted perceptions from order of data, scaling of graphs, summaries
- start from solution and insufficiently correct for current situation
- failure to regress to the mean, extreme values expected to continue
- attach higher validity to info which confirms previously held beliefs
- fact/value confusion: strongly held beliefs are presented as facts
- success attributed to personal ability, failure to bad luck
- order effects: eg undue importance on first and last items presented
- seeking information to support views, wishful thinking
- conservatism: failing to use new info to significantly revise estimates
- this is a mathematical, number-crunching, optimisation issue
point of indifference
1A 1B 1C 1D
- but it relies on good forecasts which in turn depend on good data
- garbage in, garbage out
Expected Marginal Seat Revenue = fare * probability of selling that fare
Economic point of indifference: 70% of $100, or 100% of $70Why do you need computers for fare mix optimisation?
Y B H T
eg 2) Name change abuse
Book in Y (still has seats)
H class available now, but anticipating a clamp down on class abuse, fill in some dummy bookings today, then as day-of-departure approaches when a real customer materialises, change Michael Mouse to Mr Smith.
But ticket in H
(even though it’s closed)
Y B H TAre there real-world, external barriers to implementation?
Even if you get the algorithms and the systems and the internal business processes right, you can’t do this without external market discipline, and that means stopping distributors from undermining your efforts at revenue management through abusive practices.
Poor fare structure / inadequate yield management data maintenance
Hi variance fare bands leads to “inversions”
What you thought was $350 is only $190 when you take into account free limo/hotel
(higher-class-lower value trumps lower-class-higher-value)
Y B H T
Y B H TAre there real-world, internal barriers to implementation?
Sales don’t buy in to discipline “we’ll lose business to our competition”
O&D control: managing the trade-offs between high-yielding point-to-point (eg 3rd/4th freedom) versus low-yielding connecting-traffic (eg 6th freedom) to maximise net-net revenue in real-time on a network-wide basis.