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?

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

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

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

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

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

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

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

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

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

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 ?

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

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

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

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

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

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