1. Forecasting, a cause of yield decline?
AGIFORS
Reservations & Yield Management Study Group
New York, March 2000
Gert-Willem Hartmans
Revenue Optimisation & Research
KLM Royal Dutch Airlines
Forecasting, a cause of yield decline?

2. Revenue Management Objectives
Goal: maximum revenue from given capacity
By determining pricing & availability control
Based on segmentation of:
price sensitivity,
acceptance of restrictions,
booking behaviour (includes when, group size, volume ...)
Sometimes limited by:
Competition

3. Revenue Management Process - Model
Group
Desk
Reservations
& Sales
Forecast
Optimise
Fares
Display
Availability
Capacity
Capacity
Competitor
Fares
Display
Availability

4. Forecast Inputs / Outputs
Group
Desk
Reservations
& Sales
Forecast
Optimise
Fares
Display
Availability
Capacity
Forecast Inputs Forecast Outputs
Demand by fare class
Fares by fare class
Res / Sales data
Fares + Group fares
Availability+Waitlists

5. What have we lost in the process ?
What we wanted to segment by:
price sensitivity,
acceptance of restrictions,
booking behaviour
What we have left:
Segmentation is limited to booking classes

6. Customers Seek Lowest Fares
Europe North America
Both Business and Leisure travellers seek lowest fares

7. Forecast Error - Dilution
Airline A ++
Forecast too high, accept high yield,
but restrict low yield (unconstraining ?) --
Forecast too low, accept low yield,
but restrict high yield (unconstraining
high + low yield ?)
High yield traffic may buy lower fare
CRS availability display fluctuations re-enforce this

8. Forecast Error in Competitive Environment
Airline A
Airline B ++ ++ ++ -- -- ++ -- --
High yield traffic may divert to competitor ....

9. Gap between Forecast - Demand
Steady state achieved, only when fare restrictions keep customers in higher booking class
Fare
Maximum
Willingness
to Pay
Fare paid
Demand

10. What are the consequences ?
Low load flights, all booking classes available, only pricing restrictions save us
Forecast error, high yield traffic may choose lower booking classes
Competitor forecast error, high yield traffic may divert
With capacity increase, high yield traffic may choose lower booking classes

11. Possible Solution Identify traffic by Business / Leisure
Add price elasticity to optimisation to avoid targeting leisure on routes with no leisure demand
Accept lower load factors, but maintain yield
Higher profitability due lower costs/ less chance on spillage how ?

12. Booking Lead-time
Average Booking Lead-time
Booking Lead Time differentiates Business/Leisure Segments

13. PNR data un-used potential
Booking Lead Time (creation date)
Point of Sale
CRS
City/Country
Agent
Number in party / Group Booking
Trip Duration
Saturday night stay

14. How to identify price insensitive segment?
#1 Identify POS codes as Business or Leisure
#2 Identify share of Business travel per flight
#3 Develop strategy to minimise dilution
#4 Update optimisation output to set availability to enforce sell-up

15. Experiment
Cluster analysis of PNR data identifying POS as primarily Business/Leisure based on:
Booking lead time
Number in party
Share group travel
Possibly add ... Trip-duration, Saturday-night stay
Merge cluster analysis data with history on PNR level
Aggregate per flight/weekday, calculate % potential business
Add business share factor to forecast

16. Lessons Learned
Countries with long Visa procedures may lead to diffuse booking lead time differentiation
Solution: Use difference from average per country
Booking lead time increases with distance
Solution: Add distance in booking lead time function
Booking lead time varies per season
Solution: Need to use full years history
No segmentation is perfect

17. Conclusions 1) Forecast excludes important variables
2) Human factor, forecast error and availability display fluctuation may lead to downward bias
3) Forecast output is not maximum willingness to pay but reflects ‘lowest fare found’ demand
4) Current systems may allow unnecessary dilution on excess capacity
5) Other approaches may help identifying demand by segment