Airline Group Revenue Management Analytics

CONFIDENTIALITY 

Airline Group Revenue Management Analytics 


Whitepaper 

Innovated By 

27 August, 2009 

Authors: 

Saktipada Maity (Consultant) 

Antony Alex (Business Analyst) 

This document is confidential and no reproduction or use thereof is permitted without the consent of 

Wipro. 

Wipro Confidential 1


Table of Contents 

1 ABSTRACT.......................................................................................................................................................... 3 

2 INTRODUCTION ................................................................................................................................................. 4 

2.1 GROUP BOOKING PROCESS MAP .................................................................................................................... 5 

3 NEED FOR RMS .................................................................................................................................................. 5 

4 BUSINESS BENEFITS ......................................................................................................................................... 6 

5 PROBLEM DESCRIPTION .................................................................................................................................. 6 

6 MODEL FORMULATION ................................................................................................................................... 7 

6.1 DEFINITION .................................................................................................................................................... 7 

6.2 MATHEMATICAL MODEL ............................................................................................................................ 8 

7 SOLUTION APPROACH ................................................................................................................................... 11 

7.1 SOLUTION FLOW ......................................................................................................................................... 11 

7.2 SOLUTION FEATURES SUMMARY ............................................................................................................ 11 

8 HOW WIPRO AT YOUR SERVICE ................................................................................................................... 12 

9 APPENDIX ......................................................................................................................................................... 13 


1 Abstract 


The process of Revenue Management (RM) for airline industry is currently divided into two 

sections on the basis of the Booking Patterns, as Group Booking Process (GBoS) and Individual 

Booking Process (IBP). 

Most airlines today manage group bookings through their specific group booking help desk and 

a separate group booking enquiry page on their web site. The entire process involves manual 

intervention from both agency and airline side – right from enquiry/ request for group booking 

through approval, booking, modifications to the itinerary, payment and ticketing. Travel agents 

need to approach airline every time they need any change in group. Complexity of system 

interaction between GDS, Booking / Reservation, Revenue Management, Space Control and 

CRS renders it difficult to be manually managed. Entire process is resource-intensive, time- 

consuming, error-prone and expensive – right from enquiry to final billing. The turn-around 

time from enquiry to acceptance a group request take approximately two to three days. The 

problem is further compounded by unfulfilled tasks on PNR resulting in inventory being blocked 

which can otherwise be sold at a higher fare thus maximizing the revenue quotient for the 

airlines. 

Keeping the above factors in mind, Wipro offers an integrated Revenue Management System 

(RMS) that provides decision support for optimal space allocation and pricing of group request 

executed using a Group Booking Solution (GBoS) as user interface to airline as well as travel 

agent. The heart of RMS, the Optimization Engine, will take cues from the existing airline RM 

application in effectively managing the group inventory using advanced optimization 

techniques. This is done with respect to analyzing historical data of group bookings, identifying 

the bid price control (MAF: Minimum Acceptable Fare), agency’s group booking activities 

(bookings, revenue accounted, actions on PNR, cancellations and no-shows) and also analyzing 

utilization rates of the group bookings and many more. 

In industry, various products are available in the market in the form of Group Booking and 

Revenue Management applications which work in conjunction as a group booking solution for 

the airlines. Both differ from the e-booking portal, GBoS which Wipro offers in terms of 

extensive features that airlines can make use of. Extensive backlogs of manual requests being 

queued in the application, labored analysis of the agency itinerary requests, complex decision 

makings are some of the issues attributes to the existing solutions in the market. Inclusion of 

Optimization Engine (heart of RMS) will make the solution complete, offering end-end booking 

backed by strong analytics with the ability to provide results on a real-time basis thus reducing 

the manual intervention associated with the Group Booking process. 

The conceptual paper mainly addresses the RMS, GBoS features, Optimization Engine Modeling 

and Solution Approaches. 


2 Introduction 


The major difference between a group and a normal booking is the passenger count of the 

requested itinerary. As defined by the airlines, any availability requests for ten or more 

passengers will be classified as group space request. The next major difference between the 

two is the time frame over which they are received. The individual bookings are not realized 

until 8 weeks prior to the departure, group requests are routinely received as many as eight to 

nine months prior or may be even more. The reason why group bookings are committed at a 

reasonable period prior to departure is that the inventory being allocated as ‘group booked 

space’ if released due to cancellations or group reductions at a later data, say close to 

departure may not find enough demand hence increasing the sunk costs for the airlines. 

There are two primary steps of group booking process: Ah-hoc and Series. 

Ad-hoc requests are made for one specific group flying on a specific itinerary. Typically, travel 

agents or tour operators will make ad-hoc requests to an airline after receiving the request 

from the third party or they may request a group of seats on speculation, based on anticipated 

demand. 

Series request on the other hand is long term in nature, involving many groups traveling on 

many dates. Typical requests of this kind is made by the cruise lines, military and tour 

wholesalers who book similar itineraries over several weeks rather than individually. We will 

have very less opportunities to improve group revenue for contract request. So, we would 

generally concentrate on the Ad-hoc booking process. 

Three important parameters that characterize the Ad-hoc Group Booking Process are: 

• Number of group request 

• Size of group request 

• Utilization rate 

The gamut of Group Revenue Management Problem can be divided into various segments for 

optimized decision support of Airline Revenue Management Analyst: 

• Group Size Allocation problem 

• Group Pricing 

• Discount Allocation 

• Overbooking 

Keeping the above requirements in mind, Wipro offers an integrated Group Revenue 

Management System (RMS) that provides decision support for optimal pricing of group request 

automatically using a Group Booking Portal (GBoS) as a user interface to airline as well as 

travel agent. The heart of RMS, the Optimization Engine, will take cues from the existing airline 

RM application in effectively managing the group inventory using advanced optimization 

techniques. 

The main objectives of Revenue Management System (RMS) with respect to Group Booking 

are: 

• To build RMS functionalities around the core areas like Group Booking, Discount 

Allocation, Overbooking, Pricing and categorizing a Travel 

• Reduced manual intervention by automating the computations around the analysis. 

• To build mathematical models around RMS that will collect data from the Group Booking 

portal and airline revenue management system. 


2.1 Group Booking Process Map 


Based on understanding by Wipro, collated and prepared by the Domain team 

3 Need for RMS in Group Booking 

Revenue Management Analytics uses sophisticated procedures balancing supply and demand to 

maximize revenue and profit for the airline. Revenue Management has generated billions of 

dollars for airlines, hotels, car rentals, cruise lines, retailers and manufacturers. RMS is mainly 

concerned with the demand management decisions and the methodology and the systems 

required to support the process. RMS supports in reality supports three crucial decisions. They 

are: 

§ Capacity Control decision - Much of relevance here in the airline industry as the capacity 

(or the seat inventory) is perishable. Deciding on the point whether to accept the offer; 



how to allocate capacity across different segments; when to withhold selling but offer 

them at a later point of time 

§ Pricing decision – Mainly used as a lever to control the revenue. Pricing differentiation 

across product categories; Special prices arranged on mutually agreed terms; Individual 

prices; Discounted pricing for the patronisers are some of the few 

§ Marketing and Sale decisions – Selling format to be adopted based on market timings 

and the type of audience; Terms of trade that can be put on offer; Segmentation and 

Positioning the products and services e.g. Bundling 

4 Business Benefits 

Utilizing the RMS, Clients expect to experience significant improvements in revenue and 

customer service, coupled with booking efficiency and reduced manual intervention. 

RMS will remove the need for manually looking into the itineraries selected by the end 

users (agency users) for analysis and decision making by the revenue team. Enables to 

accurately estimate demand based on the current booking trend. 

Models will analyze the requests online and provide real-time results thus reducing the 

overall turnaround time. 

Optimizes the revenue management operation for group booking with the sole objective 

of increasing the revenues. 

Reduces the number of empty seats that fly for an airline on the day of departure by 

using the concept of overbooking, which helps to increase the revenue. 

To provide extensive features that supports the decision making of the airlines backed 

by strong analytics. 

5 Problem Description 

Most airlines today manage group bookings through their specific group booking help desk and 

a separate group booking enquiry page on their web site. The decision on accepting the request 

(anticipating the likelihood of utilization of the requested group space), calculation of the price 

ensuring the value (price) arrived at breaches the displacement/opportunity cost had the same 

inventory been allocated for a future request, discounted value to be computed for special 

travel products can be complex based on the number of requests, cancellation and travel 

agencies. The entire process involves manual intervention from the airline revenue department 

rendering the whole process a tedious and error prone. 

Instead we can conceptualize an optimal group revenue management system that takes 

care of: 

1. Provides an optimal airline seat allocation scheme to travel agents or tour operators for 

their group booking requests. 

a. Number of seats for group booking process 

b. Number of group bookings request 

c. Size of each group booking request 

d. Group Price per seat 

e. Refundable deposits 



2. Provides an optimal control mechanism on group booking process 

a. Demand pattern 

b. Booking pattern 

c. Cancellation pattern 

d. “No Show” pattern 

e. Overbooking 

f. Group booking start date 

g. Group booking end date 

h. Time between negotiation phase and placement of non-refundable deposits 

i. Time between non-refundable deposits and actual purchase of tickets 

j. Time between actual purchase of tickets and day of departure 

Thus from the above discussion it is quite clear that the RMS comprises of different modules of 

separate problems which are to be addressed in a structured manner to come to a reasonably 

good solution. 

6 Model Formulation 

6.1 Definition 

Group booking process generally starts about twelve months prior to the flight departure. Let 

the time span from the start of the booking to the actual flight departure be divided into 

suitable intervals as T = t, t-1…1,0. Where, t=0 denotes that the flight has actually departed. 

For each time points we have the demand for the seats, average fare for the seats and the 

actual request that comes for the group booking. To illustrate further consider the following: 

t=T, diT 

FiT, µiTr 

Let, 

The Set be defined as: 

I = Class of fares for an airline. 

K= the total time interval starting from the start of group booking till the departure of the flight 

Let the Variables be defined as 

At each time point there are three components to group booking process: 

dij = Demand that is estimated from the historical data at the time point ‘j’ for the ‘i’th class. 

Fij = Average fare that is to be estimated from the historical data at time point ‘j’ for class ‘i’. 

µijr = the group request that comes at the time point ‘j’ for the class ‘i’. 

Ci = the number of reserved seats for group booking for the class ‘i’. 

g 

∑ = 

Ci 

t=j, dij 

Fij, µijr 

Thus i 1 = C, where C= total seats reserved for group booking process and ‘g’ denotes the 

total booking class. 

Wipro Confidential 7 

t=T, di0 

Fi0, µi0r

g = the number of class that an aircraft has. 


Fik = denotes the average fare for the i th class at a particular time interval ‘k’ for the i th class. 

C = the group booking limit for the i th class. 

C1kr = Opportunity cost to the airline when the traveler books for a certain number of seats but 

pays the advance for a fraction of it. 

P1kr= Fraction of originally booked tickets at k th time point. 

C2kr = Opportunity cost to the airline when the traveler does not pay for all the seats for which 

advance was paid. 

P2kr = Fraction of the un-purchased seats at k th time point. 

C3kr = Opportunity cost to the airline for the seats which are purchased but the passenger does 

not show up. 

P3kr = Fraction of the tickets actually purchased kth time point. 

Decision Variable: 

xikr denotes the number of seats that will be allotted to a request (r) of size µikr at time point 

‘k’. 

Here we are assuming that a group request of µ at a particular time point comes for the i th 

class. 

6.2 Mathematical Model 

The base model that would be used for optimization can be given as: 

Z = MAX∑ r 

Subject to 

∑ k 

g 

{(∑ 

i= 

1 

Fik 

xik 

xikr


Model output of P gives the group size. Solving the objective function of (P) gives the maximum 

revenue that can be earned at k th point of time where k K. 

Minimum Acceptable Fare (MAF) Calculation 

In accepting a group request of size ‘S’, an airline potentially displaces upto S individual 

passengers. Since group fares are often discounted below the individual passenger fares in the 

same booking class, the decision of whether or not to accept a group request is directly 

dependent on individual passenger demand for each flight leg in the group’s itinerary. The 

group request should be accepted as long as the revenue generated from the group is greater 

than the expected revenue of the individual passengers the group displaces. This is generally 

termed as the Total Expected Revenue of Displaced Passengers (TERDP). 

Minimum Acceptable Fare (MAF) = TERDP/ (Group request size) 

Where TERDP is calculated using the Network Based Displacement Cost Model. 

Let us define Z(C) to be optimal objective value function (I) using the initial set of group 

booking limit C. 

Now consider an ad hoc group request size S. 

Define Z(C-S) to be optimal objective function solving (I) where the capacity constraints of 

each aircraft where the group will travel is decreased by the size request S. 

The value Z(C-S) is the best solution to the problem given that one has accepted the group 

request and S seats are no longer available for further passenger booking. 

We define the difference of the objective functions Z(C) and Z(C-S) to be D(S) which is defined 

as TERDP. Thus D(S) represents total expected revenue of displaced passengers. In 

algorithmic form the following can be proposed: 

Step 1: Find Z(C) using the linear mathematical programming formulation (P) for the given 

network. 

Step2: Reformulate the mathematical program to calculate Z(C-S), where the capacity 

constraints used in step 1 reduced by S where the group travels. The reformulated model is 

given as: 

Z = MAX {(∑ 

i= 

Subject to 

g 

1 

Fikxik 

xik

Step3: Find D(S) = Z(C) – Z(C-S). 

Step4: MAF = D(S)/S. 


The steps are to be executed in a looping procedure where the next starting point would be 

Z(C-S). The procedure would continue till all the seats are used up or group request period 

ceases whichever may be early. 

Discounted Allocation 

As described in the introductory phase the airlines may be interested to allow discounts to 

agencies to attract more bookings and thus increase the revenue. The discounts are allowed for 

the agencies that have good reputation in the market. Thus the airlines may be interested to 

estimate the reputation of an agency based on some factors which are listed as: 

1. Payment history: This denotes how timely the travel agents pay their dues be it the non 

refundable amount or the actual payment of the fare. 

2. Proportion of bookings: This factor actually denotes the proportion of the seats that the 

traveler buys to the total number of seats they actually commit to buy. 

3. Market reputation: This forms an important guideline for the airlines to provide discounts 

especially to the agents which are not regular customers for the particular airline. Good 

market reputation would often be a driving factor to provide discounts. 

4. Familiarity of the agents: this is also a driving factor for the airlines to provide discounts. 

Agents who are regularly book tickets from a particular airline are more likely to get better 

discounts and offers. 

Keeping the above factors a “score” can be generated for each agent that books tickets from 

the particular airline. This score would guide the airlines to provide discounts and offers on the 

“MAF” which is calculated using TERDP. The scoring is done using a Multiple Logistic regression 

model, which is an extension of the Logistic regression with more than one covariate. The 

decision variables are generally qualitative variables which would be the scoring variable in our 

case. 

Overbooking 

In group booking scenario cancellations and “no shows” are quite common. This results in few 

of the flight seats to fly empty. Thus the airline authorities would like to keep the provision of 

booking excess to the capacity in order to maximize the revenue and reduce the number of 

seats that actually fly empty. A point to note here is that aggressive overbooking might result 

in more passengers than the physical capacity at the time of departure. Such a situation leads 

to passengers being denied boarding a flight. 

Thus an overbooking factor should be calculated. Let as mentioned number of seats allotted for 

group booking be ‘C’. 

Over booking factor (OF) = 1/ (1-NS) 

Where NS = Average no-show rate. 

As described in (I) and aggregate of p1, p2 and p3 would give an estimate of Utilization factor 

say ‘u’. Thus compliment of ‘u’ would give an estimate of the NS. 



Thus OF*C gives the number of overbooking group seats. 

To start of initially the estimates of p1, p2 and p3 are to be calculated using the historical data. 

7 Solution Approach 

7.1 Solution Flow 

7.2 Solution Features summary 

S. No Factors Description Drivers 

1 Determining the 

MAF 

3 Discounted Fares Discounted percentage 

determination to be applied 

on fares 

i)MAF for a given O&D, Date Displacement cost 

calculation, Revenue 

optimization and Inventory 

Management 

ii)Extending MAF for multileg 

journey 

iii)Self-adjustable MAF due 

to nearing departure time 

/cancellations/increased 

demand 

Customer loyalty 

management 



Estimating discounts to be 

based on product type 

(Leisure/Student/Corporate) 

and on agency performance 

4 Re-routing Requests that cannot be 

accommodated can be rerouted 

for an alternative 

date 

e.g. +/- one day to the 

departure date (determine 

off-peak periods from 

historical data) 

5 Auto-split i)System will split the group 

count across itineraries 

ii)Computing the number of 

passengers to be split 

across itineraries 

6 Utilization Rates To be computed for a given 

O&D, date , itinerary, 

agency, season etc. 

7 Overbooking Determine % of 

overbooking level and 

encourage booking in case 

of anticipated group space 

cancellations from the 

Agents 

8 How Wipro at your Service 

Wipro Confidential 12 

Nearing departure time/nonavailability/ 

higher 

displacement cost 

Customer loyalty ensured 

that no group space requests 

are rejected on the basis of 

group counts 

Track agency performance, 

revising demand when for a 

given O&D and date 

utilization rates change 

across group bookings 

previously made 

Reduces Unsold Inventory 

Wipro is primarily an IT services company. In its continuous effort to be innovative and 

productive we have also built a number of Product frameworks, re-usable components, tools 

and IPs for the benefit of our clients. This is a part of our effort to reduce our clients total cost 

of ownership of the product/service and enhance our domain skills around our clients’ industry. 

This is as a part of several Centers of Excellence (COE) created where-in dedicated teams work 

on building frameworks for the benefit of our clients. Following this we have developed the 

Booking Engine framework interfaced directly with the CRS for Flight Booking, Group Booking 

and Airline ancillary solution. Our intent is to communicate that Wipro is not a product company 

where-in we derive our revenue from software product sales, but an IT services company which 

brings value to the client by building and using our re-usable frameworks and components


Wipro Delivers 

1. Across industries especially the airline sector, IT faces renewed pressure from business to 

prove their value and it is vital for any partners to understand that the true value they must 

produce is business value, not technology value. Wipro, as a transformational outsourcing 

partner, works with out clients to help focus their technology initiatives on the critical 

business drivers and objectives, while consistently driving toward value based business 

solutions. 

2. Wipro provides significant thought leadership in the effective use of both transformational 

outsourcing and IT as business value engines. In addition to the cost reduction benefits, 

our vast experience as a leader in outsourcing enables us to provide our customers with 

cross-functional process improvements, business process insights, system standardization, 

system documentation, and delivery of operational excellence across managed platforms 

9 Appendix – definitions 

Overbooking – Accepting more reservations than the actual capacity of the airline. This is 

used as a hedge against likely cancellations from the passengers. 

MAF - Any fare above the MAF is likely to have a negative displacement cost i.e. the fare that is 

accepted by the airline can more than offset the likelihood of selling the same in the future 

Group Booking – As per airline terminology any requests for booking with the count of 

passengers exceeding 10 or above is classified as a group space request 

Utilization Rate – It is the ratio of the Actual arrivals for departure to the initial Group Space 

blocked 

Class of Service – The accommodation of passengers at different fare levels is achieved by 

specifying it through a Fare Class 

Feeders – A subset of the whole lot travelling from a different destination to the group origin 

point. Airlines cater this special group booking service through ‘Feeders’ 

No-shows – A person reserved on a flight neither travels or cancels the reservation made 

PNR – Passenger Name Record is a unique identifier held in the Computerized Reservation 

System containing the itinerary of an individual or a group of passengers

Airline Group Revenue Management Analytics

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?