Airline Group Revenue Management Analytics
Airline Group Revenue Management Analytics
Airline Group Revenue Management Analytics
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CONFIDENTIALITY<br />
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
Whitepaper<br />
Innovated By<br />
27 August, 2009<br />
Authors:<br />
Saktipada Maity (Consultant)<br />
Antony Alex (Business Analyst)<br />
This document is confidential and no reproduction or use thereof is permitted without the consent of<br />
Wipro.<br />
Wipro Confidential 1
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
Table of Contents<br />
1 ABSTRACT.......................................................................................................................................................... 3<br />
2 INTRODUCTION ................................................................................................................................................. 4<br />
2.1 GROUP BOOKING PROCESS MAP .................................................................................................................... 5<br />
3 NEED FOR RMS .................................................................................................................................................. 5<br />
4 BUSINESS BENEFITS ......................................................................................................................................... 6<br />
5 PROBLEM DESCRIPTION .................................................................................................................................. 6<br />
6 MODEL FORMULATION ................................................................................................................................... 7<br />
6.1 DEFINITION .................................................................................................................................................... 7<br />
6.2 MATHEMATICAL MODEL ............................................................................................................................ 8<br />
7 SOLUTION APPROACH ................................................................................................................................... 11<br />
7.1 SOLUTION FLOW ......................................................................................................................................... 11<br />
7.2 SOLUTION FEATURES SUMMARY ............................................................................................................ 11<br />
8 HOW WIPRO AT YOUR SERVICE ................................................................................................................... 12<br />
9 APPENDIX ......................................................................................................................................................... 13<br />
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1 Abstract<br />
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
The process of <strong>Revenue</strong> <strong>Management</strong> (RM) for airline industry is currently divided into two<br />
sections on the basis of the Booking Patterns, as <strong>Group</strong> Booking Process (GBoS) and Individual<br />
Booking Process (IBP).<br />
Most airlines today manage group bookings through their specific group booking help desk and<br />
a separate group booking enquiry page on their web site. The entire process involves manual<br />
intervention from both agency and airline side – right from enquiry/ request for group booking<br />
through approval, booking, modifications to the itinerary, payment and ticketing. Travel agents<br />
need to approach airline every time they need any change in group. Complexity of system<br />
interaction between GDS, Booking / Reservation, <strong>Revenue</strong> <strong>Management</strong>, Space Control and<br />
CRS renders it difficult to be manually managed. Entire process is resource-intensive, time-<br />
consuming, error-prone and expensive – right from enquiry to final billing. The turn-around<br />
time from enquiry to acceptance a group request take approximately two to three days. The<br />
problem is further compounded by unfulfilled tasks on PNR resulting in inventory being blocked<br />
which can otherwise be sold at a higher fare thus maximizing the revenue quotient for the<br />
airlines.<br />
Keeping the above factors in mind, Wipro offers an integrated <strong>Revenue</strong> <strong>Management</strong> System<br />
(RMS) that provides decision support for optimal space allocation and pricing of group request<br />
executed using a <strong>Group</strong> Booking Solution (GBoS) as user interface to airline as well as travel<br />
agent. The heart of RMS, the Optimization Engine, will take cues from the existing airline RM<br />
application in effectively managing the group inventory using advanced optimization<br />
techniques. This is done with respect to analyzing historical data of group bookings, identifying<br />
the bid price control (MAF: Minimum Acceptable Fare), agency’s group booking activities<br />
(bookings, revenue accounted, actions on PNR, cancellations and no-shows) and also analyzing<br />
utilization rates of the group bookings and many more.<br />
In industry, various products are available in the market in the form of <strong>Group</strong> Booking and<br />
<strong>Revenue</strong> <strong>Management</strong> applications which work in conjunction as a group booking solution for<br />
the airlines. Both differ from the e-booking portal, GBoS which Wipro offers in terms of<br />
extensive features that airlines can make use of. Extensive backlogs of manual requests being<br />
queued in the application, labored analysis of the agency itinerary requests, complex decision<br />
makings are some of the issues attributes to the existing solutions in the market. Inclusion of<br />
Optimization Engine (heart of RMS) will make the solution complete, offering end-end booking<br />
backed by strong analytics with the ability to provide results on a real-time basis thus reducing<br />
the manual intervention associated with the <strong>Group</strong> Booking process.<br />
The conceptual paper mainly addresses the RMS, GBoS features, Optimization Engine Modeling<br />
and Solution Approaches.<br />
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2 Introduction<br />
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
The major difference between a group and a normal booking is the passenger count of the<br />
requested itinerary. As defined by the airlines, any availability requests for ten or more<br />
passengers will be classified as group space request. The next major difference between the<br />
two is the time frame over which they are received. The individual bookings are not realized<br />
until 8 weeks prior to the departure, group requests are routinely received as many as eight to<br />
nine months prior or may be even more. The reason why group bookings are committed at a<br />
reasonable period prior to departure is that the inventory being allocated as ‘group booked<br />
space’ if released due to cancellations or group reductions at a later data, say close to<br />
departure may not find enough demand hence increasing the sunk costs for the airlines.<br />
There are two primary steps of group booking process: Ah-hoc and Series.<br />
Ad-hoc requests are made for one specific group flying on a specific itinerary. Typically, travel<br />
agents or tour operators will make ad-hoc requests to an airline after receiving the request<br />
from the third party or they may request a group of seats on speculation, based on anticipated<br />
demand.<br />
Series request on the other hand is long term in nature, involving many groups traveling on<br />
many dates. Typical requests of this kind is made by the cruise lines, military and tour<br />
wholesalers who book similar itineraries over several weeks rather than individually. We will<br />
have very less opportunities to improve group revenue for contract request. So, we would<br />
generally concentrate on the Ad-hoc booking process.<br />
Three important parameters that characterize the Ad-hoc <strong>Group</strong> Booking Process are:<br />
• Number of group request<br />
• Size of group request<br />
• Utilization rate<br />
The gamut of <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> Problem can be divided into various segments for<br />
optimized decision support of <strong>Airline</strong> <strong>Revenue</strong> <strong>Management</strong> Analyst:<br />
• <strong>Group</strong> Size Allocation problem<br />
• <strong>Group</strong> Pricing<br />
• Discount Allocation<br />
• Overbooking<br />
Keeping the above requirements in mind, Wipro offers an integrated <strong>Group</strong> <strong>Revenue</strong><br />
<strong>Management</strong> System (RMS) that provides decision support for optimal pricing of group request<br />
automatically using a <strong>Group</strong> Booking Portal (GBoS) as a user interface to airline as well as<br />
travel agent. The heart of RMS, the Optimization Engine, will take cues from the existing airline<br />
RM application in effectively managing the group inventory using advanced optimization<br />
techniques.<br />
The main objectives of <strong>Revenue</strong> <strong>Management</strong> System (RMS) with respect to <strong>Group</strong> Booking<br />
are:<br />
• To build RMS functionalities around the core areas like <strong>Group</strong> Booking, Discount<br />
Allocation, Overbooking, Pricing and categorizing a Travel<br />
• Reduced manual intervention by automating the computations around the analysis.<br />
• To build mathematical models around RMS that will collect data from the <strong>Group</strong> Booking<br />
portal and airline revenue management system.<br />
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2.1 <strong>Group</strong> Booking Process Map<br />
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
Based on understanding by Wipro, collated and prepared by the Domain team<br />
3 Need for RMS in <strong>Group</strong> Booking<br />
<strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong> uses sophisticated procedures balancing supply and demand to<br />
maximize revenue and profit for the airline. <strong>Revenue</strong> <strong>Management</strong> has generated billions of<br />
dollars for airlines, hotels, car rentals, cruise lines, retailers and manufacturers. RMS is mainly<br />
concerned with the demand management decisions and the methodology and the systems<br />
required to support the process. RMS supports in reality supports three crucial decisions. They<br />
are:<br />
§ Capacity Control decision - Much of relevance here in the airline industry as the capacity<br />
(or the seat inventory) is perishable. Deciding on the point whether to accept the offer;<br />
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how to allocate capacity across different segments; when to withhold selling but offer<br />
them at a later point of time<br />
§ Pricing decision – Mainly used as a lever to control the revenue. Pricing differentiation<br />
across product categories; Special prices arranged on mutually agreed terms; Individual<br />
prices; Discounted pricing for the patronisers are some of the few<br />
§ Marketing and Sale decisions – Selling format to be adopted based on market timings<br />
and the type of audience; Terms of trade that can be put on offer; Segmentation and<br />
Positioning the products and services e.g. Bundling<br />
4 Business Benefits<br />
Utilizing the RMS, Clients expect to experience significant improvements in revenue and<br />
customer service, coupled with booking efficiency and reduced manual intervention.<br />
RMS will remove the need for manually looking into the itineraries selected by the end<br />
users (agency users) for analysis and decision making by the revenue team. Enables to<br />
accurately estimate demand based on the current booking trend.<br />
Models will analyze the requests online and provide real-time results thus reducing the<br />
overall turnaround time.<br />
Optimizes the revenue management operation for group booking with the sole objective<br />
of increasing the revenues.<br />
Reduces the number of empty seats that fly for an airline on the day of departure by<br />
using the concept of overbooking, which helps to increase the revenue.<br />
To provide extensive features that supports the decision making of the airlines backed<br />
by strong analytics.<br />
5 Problem Description<br />
Most airlines today manage group bookings through their specific group booking help desk and<br />
a separate group booking enquiry page on their web site. The decision on accepting the request<br />
(anticipating the likelihood of utilization of the requested group space), calculation of the price<br />
ensuring the value (price) arrived at breaches the displacement/opportunity cost had the same<br />
inventory been allocated for a future request, discounted value to be computed for special<br />
travel products can be complex based on the number of requests, cancellation and travel<br />
agencies. The entire process involves manual intervention from the airline revenue department<br />
rendering the whole process a tedious and error prone.<br />
Instead we can conceptualize an optimal group revenue management system that takes<br />
care of:<br />
1. Provides an optimal airline seat allocation scheme to travel agents or tour operators for<br />
their group booking requests.<br />
a. Number of seats for group booking process<br />
b. Number of group bookings request<br />
c. Size of each group booking request<br />
d. <strong>Group</strong> Price per seat<br />
e. Refundable deposits<br />
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<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
2. Provides an optimal control mechanism on group booking process<br />
a. Demand pattern<br />
b. Booking pattern<br />
c. Cancellation pattern<br />
d. “No Show” pattern<br />
e. Overbooking<br />
f. <strong>Group</strong> booking start date<br />
g. <strong>Group</strong> booking end date<br />
h. Time between negotiation phase and placement of non-refundable deposits<br />
i. Time between non-refundable deposits and actual purchase of tickets<br />
j. Time between actual purchase of tickets and day of departure<br />
Thus from the above discussion it is quite clear that the RMS comprises of different modules of<br />
separate problems which are to be addressed in a structured manner to come to a reasonably<br />
good solution.<br />
6 Model Formulation<br />
6.1 Definition<br />
<strong>Group</strong> booking process generally starts about twelve months prior to the flight departure. Let<br />
the time span from the start of the booking to the actual flight departure be divided into<br />
suitable intervals as T = t, t-1…1,0. Where, t=0 denotes that the flight has actually departed.<br />
For each time points we have the demand for the seats, average fare for the seats and the<br />
actual request that comes for the group booking. To illustrate further consider the following:<br />
t=T, diT<br />
FiT, µiTr<br />
Let,<br />
The Set be defined as:<br />
I = Class of fares for an airline.<br />
K= the total time interval starting from the start of group booking till the departure of the flight<br />
Let the Variables be defined as<br />
At each time point there are three components to group booking process:<br />
dij = Demand that is estimated from the historical data at the time point ‘j’ for the ‘i’th class.<br />
Fij = Average fare that is to be estimated from the historical data at time point ‘j’ for class ‘i’.<br />
µijr = the group request that comes at the time point ‘j’ for the class ‘i’.<br />
Ci = the number of reserved seats for group booking for the class ‘i’.<br />
g<br />
∑ =<br />
Ci<br />
t=j, dij<br />
Fij, µijr<br />
Thus i 1 = C, where C= total seats reserved for group booking process and ‘g’ denotes the<br />
total booking class.<br />
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t=T, di0<br />
Fi0, µi0r
g = the number of class that an aircraft has.<br />
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
Fik = denotes the average fare for the i th class at a particular time interval ‘k’ for the i th class.<br />
C = the group booking limit for the i th class.<br />
C1kr = Opportunity cost to the airline when the traveler books for a certain number of seats but<br />
pays the advance for a fraction of it.<br />
P1kr= Fraction of originally booked tickets at k th time point.<br />
C2kr = Opportunity cost to the airline when the traveler does not pay for all the seats for which<br />
advance was paid.<br />
P2kr = Fraction of the un-purchased seats at k th time point.<br />
C3kr = Opportunity cost to the airline for the seats which are purchased but the passenger does<br />
not show up.<br />
P3kr = Fraction of the tickets actually purchased kth time point.<br />
Decision Variable:<br />
xikr denotes the number of seats that will be allotted to a request (r) of size µikr at time point<br />
‘k’.<br />
Here we are assuming that a group request of µ at a particular time point comes for the i th<br />
class.<br />
6.2 Mathematical Model<br />
The base model that would be used for optimization can be given as:<br />
Z = MAX∑ r<br />
Subject to<br />
∑ k<br />
g<br />
{(∑<br />
i=<br />
1<br />
Fik<br />
xik<br />
xikr
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
Model output of P gives the group size. Solving the objective function of (P) gives the maximum<br />
revenue that can be earned at k th point of time where k K.<br />
Minimum Acceptable Fare (MAF) Calculation<br />
In accepting a group request of size ‘S’, an airline potentially displaces upto S individual<br />
passengers. Since group fares are often discounted below the individual passenger fares in the<br />
same booking class, the decision of whether or not to accept a group request is directly<br />
dependent on individual passenger demand for each flight leg in the group’s itinerary. The<br />
group request should be accepted as long as the revenue generated from the group is greater<br />
than the expected revenue of the individual passengers the group displaces. This is generally<br />
termed as the Total Expected <strong>Revenue</strong> of Displaced Passengers (TERDP).<br />
Minimum Acceptable Fare (MAF) = TERDP/ (<strong>Group</strong> request size)<br />
Where TERDP is calculated using the Network Based Displacement Cost Model.<br />
Let us define Z(C) to be optimal objective value function (I) using the initial set of group<br />
booking limit C.<br />
Now consider an ad hoc group request size S.<br />
Define Z(C-S) to be optimal objective function solving (I) where the capacity constraints of<br />
each aircraft where the group will travel is decreased by the size request S.<br />
The value Z(C-S) is the best solution to the problem given that one has accepted the group<br />
request and S seats are no longer available for further passenger booking.<br />
We define the difference of the objective functions Z(C) and Z(C-S) to be D(S) which is defined<br />
as TERDP. Thus D(S) represents total expected revenue of displaced passengers. In<br />
algorithmic form the following can be proposed:<br />
Step 1: Find Z(C) using the linear mathematical programming formulation (P) for the given<br />
network.<br />
Step2: Reformulate the mathematical program to calculate Z(C-S), where the capacity<br />
constraints used in step 1 reduced by S where the group travels. The reformulated model is<br />
given as:<br />
Z = MAX {(∑<br />
i=<br />
Subject to<br />
g<br />
1<br />
Fikxik<br />
xik
Step3: Find D(S) = Z(C) – Z(C-S).<br />
Step4: MAF = D(S)/S.<br />
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
The steps are to be executed in a looping procedure where the next starting point would be<br />
Z(C-S). The procedure would continue till all the seats are used up or group request period<br />
ceases whichever may be early.<br />
Discounted Allocation<br />
As described in the introductory phase the airlines may be interested to allow discounts to<br />
agencies to attract more bookings and thus increase the revenue. The discounts are allowed for<br />
the agencies that have good reputation in the market. Thus the airlines may be interested to<br />
estimate the reputation of an agency based on some factors which are listed as:<br />
1. Payment history: This denotes how timely the travel agents pay their dues be it the non<br />
refundable amount or the actual payment of the fare.<br />
2. Proportion of bookings: This factor actually denotes the proportion of the seats that the<br />
traveler buys to the total number of seats they actually commit to buy.<br />
3. Market reputation: This forms an important guideline for the airlines to provide discounts<br />
especially to the agents which are not regular customers for the particular airline. Good<br />
market reputation would often be a driving factor to provide discounts.<br />
4. Familiarity of the agents: this is also a driving factor for the airlines to provide discounts.<br />
Agents who are regularly book tickets from a particular airline are more likely to get better<br />
discounts and offers.<br />
Keeping the above factors a “score” can be generated for each agent that books tickets from<br />
the particular airline. This score would guide the airlines to provide discounts and offers on the<br />
“MAF” which is calculated using TERDP. The scoring is done using a Multiple Logistic regression<br />
model, which is an extension of the Logistic regression with more than one covariate. The<br />
decision variables are generally qualitative variables which would be the scoring variable in our<br />
case.<br />
Overbooking<br />
In group booking scenario cancellations and “no shows” are quite common. This results in few<br />
of the flight seats to fly empty. Thus the airline authorities would like to keep the provision of<br />
booking excess to the capacity in order to maximize the revenue and reduce the number of<br />
seats that actually fly empty. A point to note here is that aggressive overbooking might result<br />
in more passengers than the physical capacity at the time of departure. Such a situation leads<br />
to passengers being denied boarding a flight.<br />
Thus an overbooking factor should be calculated. Let as mentioned number of seats allotted for<br />
group booking be ‘C’.<br />
Over booking factor (OF) = 1/ (1-NS)<br />
Where NS = Average no-show rate.<br />
As described in (I) and aggregate of p1, p2 and p3 would give an estimate of Utilization factor<br />
say ‘u’. Thus compliment of ‘u’ would give an estimate of the NS.<br />
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<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
Thus OF*C gives the number of overbooking group seats.<br />
To start of initially the estimates of p1, p2 and p3 are to be calculated using the historical data.<br />
7 Solution Approach<br />
7.1 Solution Flow<br />
7.2 Solution Features summary<br />
S. No Factors Description Drivers<br />
1 Determining the<br />
MAF<br />
3 Discounted Fares Discounted percentage<br />
determination to be applied<br />
on fares<br />
i)MAF for a given O&D, Date Displacement cost<br />
calculation, <strong>Revenue</strong><br />
optimization and Inventory<br />
<strong>Management</strong><br />
ii)Extending MAF for multileg<br />
journey<br />
iii)Self-adjustable MAF due<br />
to nearing departure time<br />
/cancellations/increased<br />
demand<br />
Customer loyalty<br />
management<br />
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Estimating discounts to be<br />
based on product type<br />
(Leisure/Student/Corporate)<br />
and on agency performance<br />
4 Re-routing Requests that cannot be<br />
accommodated can be rerouted<br />
for an alternative<br />
date<br />
e.g. +/- one day to the<br />
departure date (determine<br />
off-peak periods from<br />
historical data)<br />
5 Auto-split i)System will split the group<br />
count across itineraries<br />
ii)Computing the number of<br />
passengers to be split<br />
across itineraries<br />
6 Utilization Rates To be computed for a given<br />
O&D, date , itinerary,<br />
agency, season etc.<br />
7 Overbooking Determine % of<br />
overbooking level and<br />
encourage booking in case<br />
of anticipated group space<br />
cancellations from the<br />
Agents<br />
8 How Wipro at your Service<br />
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Nearing departure time/nonavailability/<br />
higher<br />
displacement cost<br />
Customer loyalty ensured<br />
that no group space requests<br />
are rejected on the basis of<br />
group counts<br />
Track agency performance,<br />
revising demand when for a<br />
given O&D and date<br />
utilization rates change<br />
across group bookings<br />
previously made<br />
Reduces Unsold Inventory<br />
Wipro is primarily an IT services company. In its continuous effort to be innovative and<br />
productive we have also built a number of Product frameworks, re-usable components, tools<br />
and IPs for the benefit of our clients. This is a part of our effort to reduce our clients total cost<br />
of ownership of the product/service and enhance our domain skills around our clients’ industry.<br />
This is as a part of several Centers of Excellence (COE) created where-in dedicated teams work<br />
on building frameworks for the benefit of our clients. Following this we have developed the<br />
Booking Engine framework interfaced directly with the CRS for Flight Booking, <strong>Group</strong> Booking<br />
and <strong>Airline</strong> ancillary solution. Our intent is to communicate that Wipro is not a product company<br />
where-in we derive our revenue from software product sales, but an IT services company which<br />
brings value to the client by building and using our re-usable frameworks and components
<strong>Airline</strong> <strong>Group</strong> <strong>Revenue</strong> <strong>Management</strong> <strong>Analytics</strong><br />
Wipro Delivers<br />
1. Across industries especially the airline sector, IT faces renewed pressure from business to<br />
prove their value and it is vital for any partners to understand that the true value they must<br />
produce is business value, not technology value. Wipro, as a transformational outsourcing<br />
partner, works with out clients to help focus their technology initiatives on the critical<br />
business drivers and objectives, while consistently driving toward value based business<br />
solutions.<br />
2. Wipro provides significant thought leadership in the effective use of both transformational<br />
outsourcing and IT as business value engines. In addition to the cost reduction benefits,<br />
our vast experience as a leader in outsourcing enables us to provide our customers with<br />
cross-functional process improvements, business process insights, system standardization,<br />
system documentation, and delivery of operational excellence across managed platforms<br />
9 Appendix – definitions<br />
Overbooking – Accepting more reservations than the actual capacity of the airline. This is<br />
used as a hedge against likely cancellations from the passengers.<br />
MAF - Any fare above the MAF is likely to have a negative displacement cost i.e. the fare that is<br />
accepted by the airline can more than offset the likelihood of selling the same in the future<br />
<strong>Group</strong> Booking – As per airline terminology any requests for booking with the count of<br />
passengers exceeding 10 or above is classified as a group space request<br />
Utilization Rate – It is the ratio of the Actual arrivals for departure to the initial <strong>Group</strong> Space<br />
blocked<br />
Class of Service – The accommodation of passengers at different fare levels is achieved by<br />
specifying it through a Fare Class<br />
Feeders – A subset of the whole lot travelling from a different destination to the group origin<br />
point. <strong>Airline</strong>s cater this special group booking service through ‘Feeders’<br />
No-shows – A person reserved on a flight neither travels or cancels the reservation made<br />
PNR – Passenger Name Record is a unique identifier held in the Computerized Reservation<br />
System containing the itinerary of an individual or a group of passengers<br />
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