Revenue Management with Customer Choice Modeling and Dependent Demand Forecasting

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1 Revenue Management with Customer Choice Modeling and Dependent Demand Forecasting Anne Mercier Senior Scientist IATA Commercial Strategy Symposium Istanbul, Turkey December 9, 2010

2 Revenue Management with Customer Behaviour Definitions Selling the right product to the right customer at the right price and the right time -AMR Annual Report, 1987 The application of disciplined tactics that predict consumer behaviour at the micro-market level and optimize product availability and price to maximize revenue growth -Robert Cross, HAM, 1998 The second definition implies that: 1) There is an optimization tool to choose the best seat allocation scenario 2) There is a forecasting tool (or demand model) to predict customer behaviour and evaluate the revenue associated with a given seat allocation scenario 2

3 Revenue Management with Customer Behaviour The optimization tool relies on the demand model The best optimization tool can only be good as long as the demand model is accurate In our opinion, to be accurate, a demand model needs to handle offer-dependent forecasts For example: Predict a null demand for a booking class when a lower bucket of the same fare family (or product) is still open Include buy-up probabilities and other substitution patterns 3

4 Overview of Classical Revenue Management Approaches Classical Revenue Management Forecasting 08:00 Forecasts are generated independently for each itinerary/class combination but demand is rarely independent! 10:00 Class 1 Class 2 Class 3 Forecast Forecast Forecast Class 1 Class 2 Class 3 Forecast Forecast Forecast 12:00 Class 1 Class 2 Class 3 Forecast Forecast Forecast 4

5 Choice Models: How do they work? Choice Set Which alternative is the most convenient? 5

6 Choice Models: How do they work? A natural approach is to rely on Choice Modeling No Travel/ Competition 08:00 Class 1 Class 2 Class 3 O-D Market/Choice Interval Forecast Choice Model 10:00 Class 1 Class 2 Class 3 12:00 Class 1 Class 2 Class 3 6

7 Choice Models: How do They Work? All relevant alternatives enter a customer s choice set Preference and convenience are expressed mathematically through a utility function Utility integrates several choice criteria, for instance Product-related criteria: price, comfort level, flexibility Itinerary-related criteria: route, travel time, departure (or arrival) time Loyalty-related criteria: preference for a certain brand/carrier The resulting model is not only price-sensitive, but also time-sensitive, loyalty-sensitive A choice model approach can integrate an internal or an external segmentation of the customer base Internal: segment attributes in utility function External: clustering 7

8 Choice Modeling: Different Perspectives on Demand Consider three choice alternatives A higher-value product (e.g. Unrestricted full fare) A lower-value product (e.g. Restricted discounted fare) The No Travel/Competition alternative 8

9 Choice Modeling: Different Perspectives on Demand Unconstrained Demand 9

10 Different Perspectives on Demand Demand Constrained by Physical Capacities Unconstrained Demand Three physical units, no availability restriction 10

11 Different Perspectives on Demand Demand Constrained by Allocations Unconstrained Demand 0/3 1/2 2/1 11

12 Different Perspectives on Demand In this small example, it is easy to determine the best seat allocations because we have the preference list of each potential customer In real life, we neither have the unconstrained demand nor the customer s preference list But we have Historical Data, which is full of relevant information With the Schedule, the Bookings (PNRs), the Authorizations and other Business Rules (e.g. advance purchase), we know what was offered for every Itinerary / Departure Date / Booking Date When combined with web-scraping information, we can re-create what the competition was offering, also by Itinerary / Departure Date / Booking Date A good customer-behaviour choice model can interpret the relationships between the offering and the observed bookings and produce reliable estimates for each product s unconstrained demand as well as the substitution probabilities between alternatives 12

13 Choice Modeling Example of Substitution Graph H Y A Y A Y A When Buy-up within a cabin (or fare family) is considered B B B C C C D D D The primary demand for a day of week/time interval e.g. Monday morning 13

14 Choice Modeling Example of Substitution Graph H Y A Y A Y A When Buy-across between flights is considered B B B C C C D D D The primary demand for a day of week/time interval e.g. Monday morning 14

15 Choice Modeling Example of Substitution Graph H Y Y Y When competition is considered A A A Virtual competitor B B B C C C D D D The primary demand for a day of week/time interval e.g. Monday morning 15

16 Choice Modeling Example of Substitution Graph H Virtual competitor Y A B C D Y A B C D Y A B C D If D is closed on the first flight in the morning, the potential demand for class D can either: 1) Buy-up to class C 2) Switch to next flight 3) Not buy at all 4) * For higher buckets, switch to another cabin The primary demand for a day of week/time interval e.g. Monday morning 16

17 Choice Modeling Example of Substitution Matrices Substitution Matrices : where the price sensitivity is high and where it is lower The whole booking horizon From 8 days before departure to 0 C B A Y 0h0-5h h46-6h h46-7h h46-8h h46-9h h46-10h h46-11h C B A Y 0h0-5h h46-6h h46-7h h46-8h h46-9h h46-10h Average Cumulative Buy-Ups 10h46-11h

18 Using a Choice Model for Forecasting A choice model provides: Potential demand for : A customer segment A given market (O&D pair and some or all POS) A given time departure window (containing one or more possible itineraries) A given booking period (the booking horizon is separated in periods) Choice probabilities for all possible alternatives We use the choice probabilities to simulate the repartition of potential demand on available choice alternatives The resulting atomic forecasts are therefore dependent of what is available in the choice set By modifying the choice set, we can run the simulation again and observe how demand is expected to react to the modifications, for instance the closing of a booking class 18

19 Using a Choice Model for Forecasting - Validation We always have a validation phase for the demand model, in which a calibrated model is used to forecast bookings on dates that were kept out of the historical training set The observed availabilities are used to determine the customer choice sets The observed bookings are compared to the predicted ones 19

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26 Using a Choice Model for Forecasting - Benefits A more realistic, more inclusive demand model Forecasts are no-longer independent, but reflect current offering => Greater optimization potential A flexible, highly-customizable framework Product attributes and substitution patterns can be configured => Better fit with the particularities of each operator s business model A wealth of valuable metrics Aggregate forecasts Atomic forecasts Choice and substitution probabilities Cross-elasticity measures => Richer decision-support information and KPI s What-if analysis and simulation => Exploring the commercial value of different scenarios 26

27 Building Bridges to other Airline Business Processes A choice-based demand model like the one we described is used as an oracle in the RM optimization phase but can also be very useful in other airline business processes: 1) Pricing : Adding/removing/setting price points (fares) on a given market Matching (or not) the competition 2) Marketing : Deciding what characteristics to associate with each product (product line design) 3) Scheduling and Fleet Management : Evaluating spill and re-capture in the fleet assignment problem Estimating the opportunity cost of ad-hoc fleet adjustments Cabin-sizing 27

28 Appia: A New-Generation Revenue Optimization System Appia is the software implementation of ExPretio s choice-based Revenue Optimization technology for network travel and transportation industries Currently exists in two configuration: railway and airline Appia represents a new generation of Revenue Optimization systems 28

29 Contact Information Dr. Anne Mercier Senior Scientist ExPretio Technologies 200 Laurier West, Suite 400 Montreal, Quebec CANADA H2T 2N8 29

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