New Evaluation of business efficiency in the Indian Telecom sector · 2015. 8. 7. · the comparative productivity efficiency for global telecoms (hsiang-chih tsai, chun-mei chen,

INDIAN INSTITUTE OF MANAGEMENT, AHMEDABAD

Evaluation of business efficiency in the Indian Telecom sector

Independent Project (Credited)

Project Guides: Prof. Rekha Jain & Prof. Arnab Laha

By,

Abhijit Kedia

&

Jayant Kaim

March 5, 2010

Analysis of Business Efficiency of Indian Telecom Sector

Indian Institute of Management Ahmedabad

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TABLE OF CONTENTS

Introduction .......................................................................................................................... 5 MEASURES OF PERFORMANCE ............................................................................................................ 5 RECENT DEVELOPMENTS .................................................................................................................... 5 FUTURE TRENDS IN THE INDUSTRY ....................................................................................................... 7

About Data Envelopment Analysis (DEA) ............................................................................. 10 THE CCR MODEL ........................................................................................................................... 11 ALTERNATIVE DEA MODELS ............................................................................................................. 12

Literature Survey of DEA Analysis ........................................................................................ 13 BENCHMARKING TELECOMMUNICATION SERVICE IN INDIA (R M DEBNATH, RAVI SHANKAR, 2008) ................ 13 USING DEA WINDOW ANALYSIS TO MEASURE EFFICIENCIES OF TAIWAN’S INTEGRATED TELECOMMUNICATION INDUSTRY (HSU-HAO YANG, CHENG-YU CHANG, 2009) ....................................................................... 14 THE COMPARATIVE PRODUCTIVITY EFFICIENCY FOR GLOBAL TELECOMS (HSIANG-CHIH TSAI, CHUN-MEI CHEN, GWO-HSHIUNG TZENG, 2006) ........................................................................................................ 16 METHOD FOR FORECASTING TELECOM OPERATORS’ REVENUE: BASED ON DEA REGRESSION (XU JIANG, WANG JINGMIN, 2009) ............................................................................................................................ 17 AN APPLICATION REFERENCE FOR DATA ENVELOPMENT ANALYSIS IN BRANCH BANKING: HELPING THE NOVICE RESEARCHER (NECMI AVKIRAN, 1999) ............................................................................................... 19 MEASURING THE EFFICIENCY OF DECISION MAKING UNITS (CHARNES, COOPER, RHODES, 1978) .................... 20

Factor Analysis of Quality of Service Data ............................................................................ 21 OBJECTIVE AND SCOPE .................................................................................................................... 21 ABOUT FACTOR ANALYSIS ................................................................................................................ 22 DATA ANALYSIS .............................................................................................................................. 24 MULTIPLE REGRESSION ON THE ‘TECHNICAL’ FACTOR ............................................................................. 25

Inter-circle DEA Analysis to measure quality of service......................................................... 30 OBJECTIVE AND SCOPE .................................................................................................................... 30 INPUT PARAMETERS ........................................................................................................................ 30 OUTPUT PARAMETERS ..................................................................................................................... 30 THE MODEL .................................................................................................................................. 31 DATA ANALYSIS .............................................................................................................................. 32

Inter-firm DEA Analysis to compare business efficiencies ..................................................... 34 OBJECTIVES AND SCOPE ................................................................................................................... 34 INPUT PARAMETERS ........................................................................................................................ 34 OUTPUT PARAMETERS ..................................................................................................................... 34 THE MODEL .................................................................................................................................. 35 DATA ANALYSIS .............................................................................................................................. 36

Limitations and further work ............................................................................................... 38 References .......................................................................................................................... 39



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LIST OF EXHIBITS

Exhibit 1: Framework of Indian Telecom Industry ........................................................................ 41

Exhibit 2: Quality performance of Bharti Airtel for the quarter ending September 2009 ........... 42

Exhibit 3: Quality performance of Bharti Airtel for the quarter ending September 2009 ........... 43

Exhibit 4: Quality performance of Idea Cellular for the quarter ending September 2009 .......... 44

Exhibit 5: Factor Analysis of Quality of Service Data for Q2 2010 ................................................ 46

Exhibit 6: Correlation coefficient between Factor 1 and Network Quality parameters .............. 47

Exhibit 7: Quality of Service - Efficiency Scores of Bharti Airtel ................................................... 48

Exhibit 8: Quality of Service - Efficiency Scores of Reliance Communication .............................. 49

Exhibit 9: Four-in-one plot of final regression variables ............................................................... 50

Exhibit 10: Quality of Service - Efficiency Scores of Idea Cellular ................................................. 51

Exhibit 11: Comparison of Quality of Service Efficiency of operators in different circles ............ 52

Exhibit 12: Comparison of Efficiency across various types of circles ........................................... 52

Exhibit 13: Input Parameters for inter-firm DEA Analysis ............................................................ 53

Exhibit 14: Output parameters for Inter-firm DEA Analysis ......................................................... 54

Exhibit 15: Efficiency Scores of Inter-firm DEA Analysis ............................................................... 55

Exhibit 16: Comparison of efficiency scores using Q2 2010 weights ........................................... 56

Exhibit 17: Normalized output weights of Bharti Airtel ............................................................... 57

Exhibit 18: Normalized Output weights for Reliance Communications ....................................... 57



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EXECUTIVE SUMMARY

Indian Telecom is one of most competitive markets in the world. The competition has

made it necessary for the players to become efficient and also innovate in terms of

technology and offerings. The scope of the project is to study efficiency in the market in

terms of current competitiveness of the operational firms with an emphasis on quality

of service. The overall objective of the project is divided into two parts.

In the first part the various benchmarks of quality determined by the Telecom

Regulatory Authority of India (TRAI) are analyzed. It is concluded that these benchmarks

can be categorized in three sets having high correlation amongst each other. Further

analysis reveals that the network quality factor can be expressed without loss of much

information by three parameters instead of eight currently used by TRAI.

The second part of the project involves studying efficiency of the different firms

currently operating in the market. Efficiency can be measured as the output produced

by an entity taking in account its inputs. Data Envelopment Analysis (DEA) is used for

this purpose. DEA in the past has been used in a lot of different arenas like

manufacturing firms, hospitals, banks to study relative efficiencies. DEA uses a multi

output, multi input linear programming model to evaluate relative efficiencies of

different decision making units (DMUs) in a competitive scenario. In this project, the

DMUs are taken at a circle level – each circle being an independent DMU; as well as at

the national level – each operator being a DMU. The efficiency scores obtained from the

circle-level analysis reveal that higher order circles (Circles A and Metros) are inefficient

when compared to lower order circles (Circles C and D). The national-level analysis

reveals that efficiency of a DMU is impacted by a change in strategy of a firm as well as

inorganic expansion.

Finally, the limitations of the models are discussed along with scope for further research

on the subject.



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INTRODUCTION

When the government decided to open the Indian Cellular industry to private players in

1995, little did it anticipate the success and growth that was to follow. The subscriber

base has increased from around 10 million in 2002 to about 480 million in 2009. India is

seeing an addition of over 12 million subscribers every month and continues to be one

of the fastest growing cellular markets in the world. It is estimated that India would

have about 559 million subscribers by 2011 (Mookerji, 2008). Some like Narayana (2008)

have gone on to demonstrate that the growth in telecom services has played a major

role in the stupendous growth of the Indian economy over the past decade.

Measures of Performance

The common measures of performance in the Telecom Industry are:

Average Revenue per User (ARPU) is the total revenue divided by number of

subscribers.

Average Minutes of Usage (MoU) is the total number of minutes per month

divided by number of users.

Average Revenue per Minute (RPM) is the total revenue divided by number of

minutes of usage per month.

As the industry is still in the growth phase and with spectrum auction due in 2010, the

availability of capital is critical for the service providers. Hence, financial measures like

interest coverage, debt-equity ratio and P/E multiples are also important for evaluating

their performance.

Recent Developments

The Telecom Industry in India, although de-licensed, works under a tight regulatory

framework managed by different government, regulatory and quasi-judicial institutions

(Exhibit 1). The most influential among them are the Telecom Regulatory Authority of



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India (TRAI) and the Department of Telecom (DoT). These institutions are extremely pro-

active in dealing with the dynamic nature of the industry. Hence, while examining the

recent trends and future direction of the industry, the role of these institutions cannot

be ignored.

Declining ARPU and per-second billing

A total of 15.41 million subscribers were added during the month of January 2009 as

against 10.81 million subscribers in December 2008 (TRAI, Press release No 16/2009).

Net additions of this magnitude have been unprecedented. However, the impressive

growth has been accompanied by falling ARPU which has affected both the top-line and

the profitability of all service providers.

In February 2009, DoT issued 120 telecom licenses across circles to nine firms (Rediff

News, 2008). Due to this, between six to eight operators will be competing in each

circle. This intense competition has led to further decline in ARPU and RPM. Already

some new entrants have announced a tariff rate of as low as 1 paise per two second,

making Indian telecom services one of the cheapest in the world.

Continuing war for Spectrum

Spectrum is the scarcest natural resource in the wireless telecommunications industry.

Every operator needs a specified bandwidth – which is determined by technology – to

provide service. As the total bandwidth is limited the DoT, in collaboration with other

Government bodies, allocates or auctions the spectrum. The process has been marked

by constant flip-flop by the authorities leading to a delay in nation-wide launch of 3G

services. Barely a month after ministry of finance pitched for doubling the auction price,

DoT indefinitely postponed the auction for 3G licenses and drastically lowered the

estimated revenue from Rs. 300 billion to Rs. 200 billion. The auction date has now been

fixed at April 9, 2010.



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Reduction in Interconnect Charges

TRAI also released its much awaited changes to the Interconnect Usage Charges (IUC),

slashing them from 30 paise/minute to 20 paise/minute for domestic calls. Incumbent

GSM operators will now be at a disadvantage as they would lose revenues on calls made

to their subscribers. Although the regulator has not explicitly asked for the benefits of

reduction to be passed on to the consumers, the competitive scenario would make it

inevitable. As a result, the ARPU would decline further.

Number Portability becomes a reality

Another issue that brings out strong opinions from incumbent and new operators is

related to number portability. On March 9, 2009, DoT selected two US firms to run

mobile number portability services (Kurup, 2009). This brought an end to the

uncertainty prevailing over whether the facility will be provided or not. From September

2009, the mobile users in India belonging to large cities and states will be able to retain

their mobile number even after switching to another service provider. The facility will be

extended to all circles within a year. Once again, incumbent operators will be at a

disadvantage as new entrants can introduce aggressive pricing and promotion schemes

to poach their customers.

Future Trends in the Industry

We have identified Value Added Services (VAS), Third-Generation Technology (3G), and

entry of foreign players including Mobile Virtual Network Operators (MVNO) as the

trends in the industry that will shape its future. For each one of them, we will examine

the opportunities, key drivers, challenges and the role of the regulatory policies and

institutions.

VAS and 3G Technology

It is no secret that the ARPU of telecom services providers in India has shown a

continuous decline in the past few years. The dip in revenues from voice-based services

has been accompanied by a steady increase in revenues from VAS. The VAS market in



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India accounts for more than 10% of the operator’s revenue and was estimated to be

worth Rs. 59 billion in 2006-07. It is estimated that the revenue will increase to Rs. 250

billion by 2010 (TRAI Recommendations, 2009).

Currently, Short-Message-Services (SMS) accounts for 44% of the VAS revenues (Cygnus,

2009). In future this is expected to change; the key drivers for VAS will be Location

Based Services, mobile music and videos, m-commerce and user-generated content. All

these features need better bandwidth and hence are dependent on the rollout of 3G

services.

TRAI, on February 13, 2009 clarified that no separate licenses will be required for VAS,

much to the relief of content providers and operators (TRAI Recommendations, 2009).

However, confusion still prevails on the 3G policy that will be adopted by the regulators.

Not only will VAS provide a source of revenue for telecom service providers but it will

also benefit other players in the value chain. VAS can turn out to be a major source of

revenues for media houses, mobile software developers and content aggregators.

However, India’s diversity in terms of language, culture and literacy makes it impossible

to provide uniform service to all users. To ensure acceptance of VAS in India, the

content has to be localized. VAS service providers need to think beyond entertainment

services like music and videos.

Entry of MVNO and Foreign Players

The stupendous growth in India has led to many foreign players knocking at its doors.

Due to the scarcity of spectrum, these firms are looking for strategic alliances with

existing operators. One such strategy is Mobile Virtual Network Operator (MVNO) in

which an entrant does not own network infrastructure or spectrum and utilizes the

resources of existing operators.

MVNOs in India will soon become a reality with DoT accepting the recommendations of

TRAI on the introduction of MVNO service. According to these recommendations, there

will be no limit on the number of MVNOs attached to a network operator. However, an



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MVNO cannot be attached to multiple operators. The MVNOs will be treated at par with

other providers in matters of regulation like Foreign Direct Investment (FDI) limits and

mergers and acquisitions.

MVNOs will further augment the growth of Telecom Industry in India and are expected

to create synergies between content-providers and operators. By utilizing the unused

bandwidth of operators, MVNOs will increase efficiency and productivity. These factors,

along with a possible increase in subscriber base due to specialized services provided by

MVNOs, will enhance the profitability of telecom operators.



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ABOUT DATA ENVELOPMENT ANALYSIS (DEA)

Data envelopment analysis is a nonparametric method used for the estimation of

production frontiers. The technique involves using empirical data to estimate the

production frontiers and then measure each of the competing units (decision making

units (DMUs)) against the measured frontier.

Efficiency is usually defined as some measure of output divided by input. However, in

the case of a multiple output and multiple input scenario, the relative weightage of the

outputs with respect to each other or even the relative weightage of inputs with respect

to each other becomes a problem as there are no set methods to determine them. The

main advantage of DEA over other methods is that there is no need to determine

weightages by the model user. Also, the technique is similar to linear programming so

large number of variables and constraints can be handled by the model.

The difference from other parametric approaches is that DEA uses variable weights

instead of fixed weights like other models. Hence in a sense only relative efficiency is

measured. The model runs like a linear program, where for each DMU attempt is made

to maximize the efficiency adjusting the relative weights of inputs and outputs. Hence

the weights can be thought as relative importance given to each and every component

by a particular DMU to each input and output.

The weights are derived directly from the data. Moreover, the weights are chosen in a

manner that assigns best (maximizing the output to input ratio) set of weights to each

DMU. Hence the main problem to solve in this model is to find out these weights for

each of the DMUs. The constraints used in the model follow

All weights should be non negative.

The resulting ratio must lie between zero and one



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In maximizing the efficiency of a particular DMU, the same set of weights are

used for each of the DMU, hence the above two rules are followed by all.

The CCR Model

The most basic version of the model is known as the CCR (initially proposed by Charnes,

Cooper and Rhodes) model. This model works in the following way. For each DMU,

virtual input and virtual output weights, (vi and ui) are to be determined.

Virtual input = v1x10 + v2x20 + …. vmxm0

Virtual output = u1y10 + u2y20 + …. umyn0

where xi0 is the ith input for the DMU ‘0’ and yio is the i

th output for the DMU ‘0’.

Now, efficiency can be seen as the ratio Virtual Output/ Virtual Input

As the input and output are in linear form, to maximize efficiency, the weights are to be

determined. Hence a linear programming model is now employed.

The optimal weights vary from one DMU to another and are derived from the data.

Hence each DMU is given a chance to maximize its efficiency irrespective of the weights.

The term DMU in the model is used generically. It can be any entity responsible for

converting inputs into outputs whose efficiencies are to be evaluated. In managerial

applications, DMUs are competing firms like hospitals, banks, libraries and

manufacturing units. In our context, DMUs are the telecom operators like Airtel,

Reliance etc. The model allows measurement of different inputs and outputs in different

units till the units are consistent throughout as the dimensional adjustments are made

in the weights.



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Alternative DEA Models

The basic CCR model has two variants, input oriented and output oriented. The

difference is that the input oriented model tries to minimize the usage of minimize the

use of inputs given a reference level of outputs whereas the output oriented model tries

to maximize the production of outputs given the reference level of inputs.

The CCR model is the most basic form of the technique. With time more advanced

versions have been developed. For instance, CCR works under the assumption of

constant returns to scale of activities. However this assumption can be modified to

include alternate production possibility sets like increasing or diminishing returns to

scale. The BCC (Banker Charnes Cooper) model incorporates this non linearity in its

formulation and hence in problems where the scale of operation of the DMUs may

differ widely the use of BCC model may be more appropriate. The BCC technical

efficiency can be decomposed into scale efficiency and mix efficiency. The first

component arises due to scale of operations and the second due to usage of inputs. The

second (mix) efficiency is the same as the technical efficiency in the CCR model.

Other forms of the model like Additive and Free Disposal Hull (FDH) have also been

developed. The additive model tries to combine both the input oriented and output

oriented variants into a single set of equations. The free disposal hull model on the

other hand allows a very liberal definition of the production possibility frontier. The

incremental addition is that points lying outside the production possibility frontier are

strictly not allowed in this case.

However in our case, the very basic model has been used. The CCR model was found to

be sufficient as the scale of operation of our DMUs does not vary by such amount so as

to cause a significant difference due to difference in scale of operations. Hence the CCR

model was thought to be sufficient and all applications of DEA refer to usage of the CCR

model itself.



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LITERATURE SURVEY OF DEA ANALYSIS

Benchmarking telecommunication service in India (R M Debnath, Ravi Shankar, 2008)

This paper uses Data Envelopment Analysis (DEA) to compare the relative efficiencies of

mobile service providers in India. Given the growth of telecommunication sector in India

with increasing competition due new entrants, the authors find it imminent to

benchmark the service providers. Benchmarking may help operators to improve their

service levels.

DEA is used to evaluate relative efficiencies of a group of Decision making units (DMUs)

in their use of multi-input to produce multiple outputs where the form of production is

neither known nor specified. As a consequence the DEA score is not known by an

absolute standard, but defined relative to the other DMUs in the specific data set under

consideration. The standard DEA model form has been used in this paper.

The model for a DMU used in this paper can be described as

Both the inputs and outputs used in the study were technical parameters. The four

inputs being used were no: of faults, call success rate, call drop rate and good voice

Service access delay

Complaints per 100 bills issued

Complaints resolved within 4

weeks Period of all refunds

Number of subscribers

No. of faults

Call drop rate

Call Success rate

Good voice

quality

DMU

Input Output



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quality. The outputs monitored were service access delay, complaints per 100 bills,

complaints resolved within four weeks, period of all refunds and number of subscribers.

The data was taken from the latest report published by Indiastat.com.

We believe that the study done in this piece of work is purely technical with both the

input and output parameters being technical only and parameters like management

skill, use of capital not coming into play. Also, the decision between parameters used as

input and output is also debatable with call success rate, number of faults, call drop rate

and good voice quality being used as input parameters. All the technical parameters

used are published by TRAI together hence taking one set from them as input and

another as output would be meaningless.

Two variants of the DEA model have been used in the study. The CCR model (developed

by Charnes et al, 1978) and the BCC model (developed by Banker et al, 1984). The

difference being that the BCC model takes into account variable returns to scale. Due to

two different DEA models used, the authors were able to decompose efficiency into the

product of pure technical efficiency and scale efficiency. The scale efficiency was used as

a measure of firm’s success in choosing an optimal set of inputs with a given set of

input-output prices or costs. The final result was calculation of the scale efficiency of

different firms in different circles.

The paper finally marks out the firms with efficiencies less than one. Most of the

operators with efficiency less than one are those who have scale efficiencies less than

one, hence are operating under disadvantageous conditions.

Using DEA window analysis to measure efficiencies of Taiwan’s integrated

telecommunication industry (Hsu-Hao Yang, Cheng-Yu Chang, 2009)

The paper studies the optimality of the evolution of Taiwan’s telecommunication

industry. Mergers and acquisitions in Taiwan’s telecom industry led to three firms

occupying more than 80% of the market share. An approach similar to Debnath &



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Shankar (2008) is followed where DEA is applied under constant and varying returns to

scale and efficiency is measured for the period 2001-2005.

DEA window analysis is used to determine efficiency trends of the DMUs over time.

Window analysis treats a DMU in a particular time period as one unit and compares it

with its own and other DMUs’ performance in other time periods. As a result the trends

in efficiency for the DMUs involved can also be studied. The data used in the study was

obtained from the firms’ public financial statements as required by regulatory

authorities.

The input and output parameters were determined according to data availability and

those used in past similar studies. Finally, the input variables used were assets,

operating costs and operating expenses. The output variables used were operating

revenues, mobile phone subscribers and mobile phone calls.

The DMU model can be represented as

The CCR and BCC models used combined produce technical efficiency, pure technical

efficiency, and scale efficiency. On the basis of these three types of efficiencies, three

major findings were obtained. First, the acquisitions did help improve their scale

efficiencies but worsened pure technical efficiency in the short term. Secondly, adjusting

operations strategies also helped firms to maintain their scale efficiencies within

marginal variability. Thirdly, by observing the case of CHT, which was a state-owned

Operating revenues

Mobile phone calls

Number of subscribers

DMU

Assets

Operating Costs

Op. Expenses

Input Output



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agency and is the largest firm but the worst performer in terms of each type of

efficiency, the government’s determination to promote privatization deserves credit.

Here, the mobile phone call parameter becomes redundant as operating revenue s and

number of subscribers can indicate ARPU, which can be used as a proxy for the average

number of calls made by subscribers for each operator. A quality of network aspect is

missing which would be a measure of the quality of the network, connection etc. Capital

expenditure should also have been included in t he input parameters to measure the

investments made in the network.

The comparative productivity efficiency for global telecoms (Hsiang-Chih Tsai, Chun-

Mei Chen, Gwo-Hshiung Tzeng, 2006)

This paper studies the productivity efficiency of 39 Forbes 2000 ranked leading global

telecom operators. The study combines three different methods of relative efficiency

calculation to compare the global telecoms. The DMU representing model can be

depicted as:

The classical efficiency measure is calculated by both the CCR and BCC methods

(constant and varying returns to scale). The input variables used are total assets, capital

expenditure and number of employees. The output variables used are revenue, EBIDTA

and operating profit. The A&P efficiency measure which ranks the decision making units

has also been applied. The third variant used is the efficiency achievement measure

DMU

Total Assets

CapEx

No. of Employees

Revenues

EBIDTA

Operating Profit

Input Output



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which takes in consideration the efficiency ratio of all DMUs to calculate and find a set

of common weight based so that the efficiency ratio of all DMUs calculated accordingly

becomes better as the ratio gets larger.

Regional trends emerge due to the global study which have been identified and

reported. Asia-Pacific operators score relatively higher on efficiency than counterparts

in other areas. Also, state owned telecom companies score higher at the global level

than private owned. The reason pointed out for this is government protection. The

paper acknowledges the loss in revenue of the firms by loss by emergence of substitutes

like VoIP. The firms have added VAS as a substitute which though has not been able to

make up for the loss in revenue.

Again, once revenue and EBIDTA have been taken as the output parameters, using

operating profit is unnecessary as indirectly operating profit comes into the picture

through EBIDTA. Some parameters like voice quality referring to the quality service

should have been included touching the service aspect of the operation. In terms of input

parameters, years of operation should have been there since in growing markets, longer

service is directly related to better presence and subscriber base.

Method for Forecasting Telecom Operators’ Revenue: Based on DEA Regression (Xu

Jiang, Wang Jingmin, 2009)

In this paper, input-output efficiencies of 31 provinces in China have been obtained by

setting up a Chinese Telecommunications Operators model for revenue forecasts

adopting DEA regression analysis and score calculation, and the empirical research has

been explored according to them.

DEA regression analysis is divided into two phases: First, the DEA analysis; and second,

the regression analysis. The data in each of the provinces and cities as a decision-making

unit, there are 31 decision-making units totally. The operating income is put as the

output variable, and the reach number of carrier frequency, power consumption, the



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engine room area with communication capacity are set as the as input variables. Using

these input and output variables, the efficiency scores are determined as the first stage.

The number of carrier frequency is taken to measure the company’s network capacity;

power consumption is the reflection of the cost as well as the energy consumption of

the company and the engine room area is the consumption of places of production

operations. Hence the DMU model is:

As the second stage, optimal adjustments are made to provinces which do not meet the

efficiency 1 criteria. Then a regression is carried out on the results thus obtained to

come up with a regression model which can be used to forecast the income of telecom

operators in terms of the above mentioned three input variables.

Our view is that proxy or secondary variables have been used instead of the direct

variables which could have been used in this piece of work diluting the accuracy and

bringing in unnecessary assumptions. For example taking power consumption as a proxy

for operating costs is unnecessary as operating costs themselves could have been taken

in the equation. Using power consumption brings in the assumption that all firms follow

a very similar operation schedule in terms that power consumption is linearly

proportional to operating costs. Similarly, engine room area, a measure of area

productivity is unnecessary as the area usage may not be similar.

DMU

Operating Income

No. carrier frequency

Power consumption

Engine room area



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An application reference for data envelopment analysis in branch banking: helping the

novice researcher (Necmi Avkiran, 1999)

This paper uses DEA to determine the efficiency of bank branches with respect to other

bank branches. DEA is used as it does not assume a particular production technology or

correspondence. By using DEA, a bank’s efficiency can be assessed based on other

observed performance. As an efficient frontier technique, DEA identifies the

inefficiency in a particular DMU by comparing it to similar DMUs regarded as efficient

rather than trying to associate a DMU’s performance with statistical averages that may

not be applicable to that DMU.

The four critical success factors (CSFs) identified for banks are:

1. service delivery and quality;

2. sales;

3. expense control;

4. loss control

The paper addresses the first two of these CSFs through the selected inputs and

outputs. The inputs have further been identified as controllable and uncontrollable.

a) Uncontrollable Inputs

1. Average family income

2. Number of small business establishments

3. Presence of competitors

b) Controllable Inputs

1. Number of teller windows in a branch

2. Number of staff in the branch, full time

3. Staff conduct

c) Outputs (all discretionary)

1. Total new deposit accounts

2. Total new lending accounts



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3. Total new investment centre referrals

4. Fee income

The optimization can be done as under two different objectives: output maximization

and input minimization. Insights into the operations of the branches are obtained as a

result. For example the branches losing ground steadily are likely to become candidates

for downsizing or closing down. DEA can help in identification of over allocation of

resources and hence result in optimal allocation and hence is a powerful tool for

managerial decision making.

The paper also points out the shortcomings in the DEA approach, where the limit of

efficiency is the leader in the sample; it is quite possible for a data point outside the

sample to have a much higher efficiency. Secondly, the model is overly dependent on

the quality of data collected; hence the data collected should be free of errors.

Measuring the efficiency of decision making units (Charnes, Cooper, Rhodes, 1978)

This paper was concerned with developing measures of ‘decision making efficiency’ in

multiple input and multiple output scenarios. Rather than defining an absolute measure,

the authors defined efficiency in relative terms, scaling the most efficient unit as having

efficiency 1 and then calculating the efficiency of the other decision making units

correspondingly.

The model was formulated as a linear programming model, with efficiency taken as

weighted sum of outputs over the weighted sum of inputs subject to the constraint that

efficiency of all competing units is less than or equal to one.



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FACTOR ANALYSIS OF QUALITY OF SERVICE DATA

Objective and Scope

TRAI reports 15 parameters of quality of service every quarter for all the operators.

These parameters are classified into two types – network related quality variables and

customer service variables. So far, the data is only used for reporting purpose. However,

it is our belief that with increasing competition quality of service will become a

determining factor for the customers. Already, some telecom players are highlighting

the superior quality of their network in their advertisements. In addition to this, there is

also a possibility that TRAI could impose certain penalty or deterrent for operators

having poor quality of service.

The present method of reporting makes it extremely difficult to interpret the data due

to its sheer size and complexity. Moreover, there is some redundancy in the data. For

example, the network quality data are the following:

BTSs Accumulated downtime (not available for service) (%age)

Worst affected BTSs due to downtime (%age)

Call Set-up Success Rate (within licensee's own network)

SDCCH/ Paging Chl. Congestion (%age)

TCH Congestion (%age)

Call Drop Rate (%age)

Worst affected cells having more than 3% TCH drop (call drop) rate (%age)

Connection with good voice quality

The reasons for poor performance of an operator in these dimensions could be

Large number of subscribers per MHz of spectrum

Inadequate number of towers

Temporary shut-down of base-stations due to power-failure etc.



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Many of these factors are related to each other. For instance, higher channel congestion

would naturally lead to poor voice quality as well as more number of call-drops.

Similarly a greater down-time of base stations would also affect the call-drops and

number of successful calls. Our hypothesis is that because of the inherent correlation

between these parameters, a reduced number of factors can be used to represent the

data effectively. This would enable in better reporting and interpretation.

For the purposes of this analysis, we have taken quality of service data for the quarter

ending September 2009 as the reference. Data from the three listed operator – Bharti

Airtel, Idea Cellular and Reliance Communication are used.

About Factor Analysis

Factor analysis is a statistical method used to describe variability among

observed variables in terms of fewer unobserved variables. The observed variables are

modelled as linear combinations of the factors. The information obtained about the

interdependencies is used to reduce the number of variables.

Factor analysis is related to principal component analysis (PCA) but not identical.

Because PCA performs a variance-maximizing rotation of the variable space, it takes into

account all variability in the variables. In contrast, factor analysis estimates how much of

the variability is due to common factors (communality). The two methods become

essentially equivalent if the error terms in the factor analysis model (the variability not

explained by common factors, see below) can be assumed to all have the same variance.

Suppose we have a set of p observable random variables, x1, x2, . . . xp with means µ1, µ2,

. . . µp .

Suppose for some unknown constants lij and k unobserved random variables Fj, where i

goes from 1 to p and j ranges from 1 to k and k < p

we have



23

xi - µi = l1iF1 + . . . + likFk + εi

where εi is independently distributed error terms with zero mean and finite variance,

which may not be the same for all of them.

Here Let Var(εi) = ψi, so

Cov(ε) = Diag(ψi , . . . , ψp) = Ψ and E(ε) = 0

In matrix terms, we have

x - µ = LF + ε

Also we will impose the following assumptions on F.

1. F and ε are independent.

2. E(F) = 0

3. Cov(F) = I

Any solution for the above set of equations following the constraints for F is defined as

the factors, and L as the loading matrix.

Suppose Cov(x) = Σ. Then note that from the conditions just imposed on F, we have

Cov( x - µ ) = Cov(LF + ε)

or

Σ = LCov(F)LT + Cov(ε)

or

Σ = LLT +ψ

Note that for any orthogonal matrix Q if we set L = LQ and F = QTF, the criteria for being

factors and factor loadings still hold. Hence a set of factors and factor loadings is

identical only up to orthogonal transformations.



24

So, by the use of factor analysis, covariance between the independent variables can be

checked and redundancy removed.

Data Analysis

The quality of service data is provided in Exhibit 2-Exhibit 4. The results from the factor

analysis of the data are provided in Exhibit 5. The first three factors represent 71% of

the data provided. This indicates that the raw data – comprising of 12 variables – can be

adequately represented by only three factors. The three factors can be interpreted as:

Factor 1: Network quality factor – The highlighted cells of the table show the

underlying parameters that are represented in the factor. These are:

o BTSs Accumulated downtime (not available for service) (%age)

o Worst affected BTSs due to downtime (%age)

o Call Set-up Success Rate (within licensee's own network)

o SDCCH/ Paging Chl. Congestion (%age)

o TCH Congestion (%age)

o Call Drop Rate (%age)

o Worst affected cells having more than 3% TCH drop (call drop) rate

(%age)

o Connection with good voice quality

Clearly all these are related to the quality of network provided by the operator.

Quality of billing/metering factor: This factor has only one underlying parameter

– metering and billing credibility.

Customer care quality factor: This factor has three underlying parameters

o Accessibility of call centre/ customer CARE

o Percentage of calls answered by the operators (voice to voice) within 60

seconds

o %age requests for Termination / Closure of service complied within 7

days



25

The next step of the analysis is to determine if it is possible to record and report only

one of the network parameters as a proxy for the factor. This would make both

reporting and interpretation easier for TRAI. The proxy parameter can be found by

correlating all the 8 network quality parameters with factor 1. The results are provided

in Exhibit 6. Call set-up success rate gives a correlation coefficient of approximately 90%

with the factor and hence it can be used as a proxy for the factor with much loss of

information. An alternative could be to use multiple regression techniques to determine

the set of parameters that best explains the network quality.

Multiple regression on the ‘technical’ factor

The factor analysis done had shown eight of the technical parameters being recombined

into one single factor, which could be interpreted as the technical factor. The eight

constituents are:


Worst affected BTSs due to downtime (%age)


SDCCH/ Paging Chl. Congestion (%age)

TCH Congestion (%age)

Call Drop Rate (%age)



A multiple regression was run with the one technical factor as the dependent variable

and these eight parameters as independent variables. The objective was to eliminate

multi-collinearity, as earlier these eight had been found to be highly correlated. Minitab

15 was used for this purpose. The variance inflation factor was measured with each trial.

The steps of the regression are:

1. All factors included.



26

The regression equation is

dependent = - 0.000000 + 0.759 a + 0.856 b - 0.939 c + 0.914 d + 0.926 e + 0.859 f +

0.792 g - 0.763 h

Predictor Coef SE Coef T P VIF

Constant 0 0 * * a 0.759 0 * * 4.187

b 0.856 0 * * 5.812

c -

0.939 0 * * 11.369

d 0.914 0 * * 13.077

e 0.926 0 * * 10.416

f 0.859 0 * * 4.707

g 0.792 0 * * 3.196

h -

0.763 0 * * 2.406

2. Variable ‘d’ was seen to be having the highest Variance inflation factor, hence it

was removed from the set of independent variables.

The results of the regression hence run are:


dependent = 0.178 + 0.628 a + 0.894 b - 1.15 c + 1.33 e + 0.674 f + 0.802 g - 0.726 h


Constant 0.17788 0.02827 6.29 0 a 0.6285 0.04579 13.73 0 3.98

b 0.89415 0.01015 88.13 0 5.329

c -1.15473 0.023 -50.21 0 7.269

e 1.33287 0.05931 22.47 0 8.002

f 0.674 0.07269 9.27 0 4.52

g 0.802251 0.004886 164.19 0 3.108

h -0.72644 0.01908 -38.07 0 2.351



27

3. Now, variable ‘e’ is the one having the highest Variance Inflation factor. Hence ‘e’ was

removed from the dataset. Then,


dependent = 0.517 + 0.714 a + 0.926 b - 1.53 c + 1.31 f + 0.778 g - 0.698 h


Constant 0.51664 0.04907 10.53 0 a 0.71375 0.09363 7.62 0 3.952

b 0.9261 0.02061 44.93 0 5.224

c -1.52602 0.03283 -

46.48 0 3.518

f 1.3143 0.1372 9.58 0 3.825

g 0.777919 0.009777 79.56 0 2.955

h -0.6981 0.03907 -

17.87 0 2.341

4. Still, one of the variables was seen to be having a variance inflation factor of

greater than 4. Hence ‘b’ was removed from the independent variable data set and a

new regression was run. Now, the regression equation is

dependent = 1.04 + 3.80 a - 2.10 c + 0.671 f + 0.869 g - 0.654 h

5. Variable ‘f’ now was seen to be having a relatively high VIF with p value also

significant. So, ‘f’ was removed. The regression equation is

dependent = 1.15 + 3.82 a - 2.16 c + 0.891 g - 0.703 h


Constant 1.0447 0.1768 5.91 0 a 3.7978 0.2363 16.07 0 1.828

c -2.1035 0.1121 -

18.77 0 2.979

f 0.6706 0.5064 1.32 0.187 3.784

g 0.86929 0.03549 24.5 0 2.828

h -0.6542 0.1449 -4.51 0 2.339



28


Constant 1.1536 0.1568 7.36 0 A 3.8165 0.2364 16.14 0 1.821

C -2.1594 0.1041 -

20.75 0 2.556

G 0.89131 0.03142 28.37 0 2.207

H -0.7031 0.1405 -5 0 2.187

So, now the variables left are:





R2 with these four factors was found out to be R2 = 98.1%

6. Next, another variable, ‘c’ was removed in order to get the best available result for

the technical factor in terms of three independent variables.

The R2 value was now found out to be: 92.7%

7. Now, the outliers and the influential observations were removed from the data set.

Around 10% of the data was found to be as being an influential observation or being an

outlier. The R2 now was found out to be : 95.8%

The four in one plot of the final regression is shown in Exhibit 9. Within reasonable

extent, it can be seen that the normality, heteroscedasticity and linearity assumptions

are being satisfied. The ‘p’ value of the regression was found out to be zero, hence the

regression is significant.

So finally, the regression equation is:

dependent = - 0.353 + 3.73 a + 0.937 g - 1.34 h



29

where,

dependent = technical factor score

a = BTSs Accumulated downtime (not available for service) (%age)

g = Worst affected cells having more than 3% TCH drop (call drop) rate (%age)

h = Connection with good voice quality

with an adjusted R2 of 95.8%

Hence it is recommended that these three factors should be measured and are

sufficient to indicate an operator’s technical score, all eight parameters need not be

recorded.



30

INTER-CIRCLE DEA ANALYSIS TO MEASURE QUALITY OF SERVICE

Objective and Scope

The objective of the DEA analysis is to measure the relative efficiency of every operator

in various circles of operations. Efficiency is measured in terms of quality of service

provided by the operators – technical quality (network availability, congestion rates etc.)

and customer service quality (billing, customer response etc.). We believe that with

increasing competition in the telecom industry, quality of service will become a

differentiation factor.

Our hypothesis is that the cellular service providers may not be making the optimal use

of the available resources in all circles. The analysis is carried out for three major

operators – Bharti Airtel, Idea Cellular and Reliance Communication for the quarter July

– September 2009.

Input Parameters

No. of Subscribers

Average Revenue per User (ARPU)

Spectrum Usage Charges (in million rupees)

Output Parameters

Network Related Parameters

o Accumulated downtime for BTS

o Worst affected BTSs due to network downtime

o Call set-up success rate – (No. of calls successful/No. of calls tried)

o SDDCH/Paging Channel Congestion

o TCH Congestion

o Call drop rate

o No. of cells having more than 3% call-drops



31

o Connections with good voice quality

Customer Service Parameters

o Metering and Billing credibility (for pre-paid customers)

o Resolution of complaints in billing, charging or validity

o Accessibility of call centres or customer care

o No. of calls answered within 60 seconds

o No. of connections terminated (on request) within 7 working days

The Model

The model assumes that the goal of a firm in a circle i.e. a decision making unit (DMU) is

to maximize profits under resource constraints. The scarcest resource in the telecom

services industry is spectrum, due to which spectrum utilization is a useful benchmark

for comparing operations. An efficient use of spectrum would be to get the maximum

throughput per MHz of spectrum. Throughput in telecom industry is characterized by

minutes of usage of all subscribers. This information was not available in the public

domain, but we used two proxy variables for the same. If the tariffs across circles are

nearly constant – as was the case during September 2009 – ARPU and number of

subscribers will provide an estimate of the minutes of usage in the network. To obtain

the minutes of usage per MHz of spectrum, the next step was to estimate the

bandwidth of spectrum used of each of the three operators in each circle.

No. of

subscribers ARPU

Spectrum

Charges

DMU Network Quality

Customer Service

Quality



32

It must be noted that the present regulation imposed a variable spectrum charges of 2-

4% on the Adjusted Gross Revenue (AGR). Therefore, it would appear that spectrum

charges is a redundant variable if ARPU and number of subscribers are already

incorporated in the model. However, the spectrum charges vary with extent of

spectrum. The present spectrum usage charges stand at 2% of AGR for 4.4 MHz, 3% of

AGR for 6.2 MHz and 4% of AGR for 8 and 10 MHz. Therefore, the spectrum charges are

a useful input to estimate the MHz of spectrum currently used by the operators.

The DEA analysis was carried out for all 22 circles in India – Chennai and Tamil Nadu

combined to form one circle. Certain circles where the operators were a new entrant –

less than 2 quarters of operations – were ignored for analysis as capacity utilization

would not have been optimum in these cases. The data collected was for the quarter

ending September 2009 (Exhibit 2-Exhibit 4).

Data Analysis

The efficiency scores of each of the three operators are provided in Exhibit 10. A higher

score implies more efficient operations i.e. better quality of service given the input.

Exhibit 11 plots the comparison of business efficiencies of operators in circles where all

of them provide services. The following observations could be derived from the

obtained results

Efficiency decreases with increase in order of the circles: As explained previously,

India is divided into 22 circles for the purpose of telecom services. These circles

are classified into 4 categories:

o Metros: Delhi, Mumbai and Kolkata (Chennai has been merged with

Tamil Nadu for reporting purposes)

o Circle A: Andhra Pradesh, Gujarat, Karnataka, Maharashtra and Tamil

Nadu

o Circle B: Haryana, Kerala, Madhya Pradesh, Punjab, Rajasthan, UP East,

UP West and West Bengal



33

o Circle C: Bihar, Himachal Pradesh, Jammu & Kashmir, Orissa and North

Eastern States

The division is done using various economic criteria like per-capita GDP among

others. As shown in Exhibit 12, efficiency increases as we move from Metros to

Circles A to Circles B and Circles C. For instance, nearly 40% of all operations in

Metros have efficiency below 0.5. For Circles C, all operations have an efficiency

greater than 0.5.

Further discussion is required on this interesting result. It is natural to assume

that as Metros and ‘A’ Circles contribute the majority of the revenues for

telecom operators, they would focus on providing the most efficient services in

these circles. However, the results obtained are contradictory. It appears that

the operators want to maximize the spectrum usage in these circles – as

additional spectrum is expense to procure and may not be available. This had led

to far more quality of service issues in these circles compared to Circles B and C.

In Circles B and C, there are fewer operators, spectrum is easier to obtain and

the network usage (minutes of usage) is lower than Circles A and Metros. Hence,

the reported technical glitches and customer service complaints are fewer.



34

INTER-FIRM DEA ANALYSIS TO COMPARE BUSINESS EFFICIENCIES

Objectives and Scope

The objective of the DEA analysis is to measure the relative efficiency of every operator

across time at the national level. Efficiency is measured in terms of making the best use

of resources at the firm’s disposal. The analysis is carried out temporally to understand

the shift in strategy of the company i.e. the change is weights attributed to each input

and output parameter.

For the purpose of this analysis we have chosen three listed telecom firms in India –

Bharti Airtel, Idea Cellular and Reliance Communications. We have used the data for five

quarters from quarter ending June 2008 till quarter ending September 2009.

Input Parameters

Operating expenditure of a firm in the quarter (includes cost of service, sales,

general, marketing and administrative expenses)

Capital expenditure of a firm in the quarter

No. of towers (rented or owned)

No. of employees

Total available capital (estimate of the average enterprise value of the firm

during the quarter)

Output Parameters

No. of subscribers

ARPU

Factors of quality parameters obtained from factor analysis



35

The Model

A telecom firm has various levers at its disposal; levers that shape the strategy of the

firm. A firm may choose to outsource most of its activities – like billing, customer service

and rent towers instead of owning them. A firm may also choose to incur capital

expenditure instead – hire employees and roll out towers and base stations. A firm may

also choose its expansion path – it may expand very fast into other circles of areas

within a circle or it may be conservative and only increase the number of towers when

demand becomes greater than the capacity of towers. Therefore, both capital

expenditure and operating expenditure should be taken into account while

understanding the strategy of a firm.

Choosing the output parameters was relatively straightforward. A firm would ideally

want to have a large number of high ARPU subscribers. It would also want better

network coverage and quality as well as no billing or customer service complaints.

Hence, in the model we have chosen five output parameters – three factors obtained

from factor analysis, no. of subscribers and ARPU. While choosing the number of

subscribers, we had the option of choosing a stock variable i.e. the aggregate number of

subscribers at the end of the quarter or a flow variable i.e. number of subscribers added

during the quarter. We chose the flow variable for two reasons – firstly, all other input

and output parameters are flow and secondly, all the operators have entered the

market at different times due to which have different number of subscribers.

Opex

Capex

DMU

No. of

Subscribers

ARPU

Quality Factors



36

Data Analysis

The input and output parameters are provided in Exhibit 13 and Exhibit 14. Efficiency

scores are provided in Exhibit 15. A cursory look at the table indicates that the average

efficiency has been greater in the latter quarters compared to the earlier ones. In other

words, operators have become more efficient in quarters of financial year 2009-10. The

efficiency scores of the last quarter (Q2 2010) also indicate that while Idea Cellular and

Reliance Communications are at 100% efficiency, Bharti Airtel has scored only 84.2%.

However, this does not necessarily mean that Bharti Airtel is more inefficient because it

has a largest subscriber base of the lot leading to quality issues; note that 3 out of 5

output parameters are related to quality. As demonstrated in the earlier DEA analysis,

number of subscribers affects the quality standards. There are two other ways of

analyzing the efficiency scores and the weights attributed to the input and output

parameters:

Comparing efficiency scores using the same weights

As explained previously, the set of weights used by a firm is a direct indicator of the

business strategy adopted by it. In other words the efficiency is given by

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑖 = 𝑉𝑖𝑗𝑋𝑖𝑗

5𝑗=1

𝑊𝑖𝑘𝑌𝑖𝑘2𝑘=1

It would be useful to analyze the efficiency of other telecom operators as well as the

efficiency of the same telecom operator using set of weights (Vij and Wik) in the most

recent quarter – Q2 2010. This would provide two insights – whether the firm has

become more efficient than previously and how do the competitors fare had they

followed the same strategy.

The results obtained using the Q2 2010 strategy as standard are shown in Exhibit 16. .

The following observations can be drawn:

The most interesting observation can be drawn by looking at the efficiency

scores of Reliance Communication using the weights obtained in Q2 2010. There



37

is a clear demarcation between the scores obtained in the first three quarters

and those obtained in the last three. The inflexion point is at Q4 2009 (January-

December 2009) which is the same time Reliance launched its GSM services. This

was a clear change in strategy and is reflected in the efficiency scores as well.

Like Reliance Communications, Idea Cellular has also posted low efficiency scores

in two quarters – Q2 2009 and Q3 2009. Its scores in other quarters (including

Q1 2009) are 10-40% higher. It appears that this was due to the acquisition of

Spice by Idea Cellular which could have diverted the management focus as well

as funds needed for capital expansion.

Understanding the shift in strategy

The second method of analyzing the results obtained from DEA analysis involves

tracking the weights temporally for one of the firms to understand if there has been a

discernable shift in its business strategy. Exhibit 17 captures the quarterly normalized

weights assigned to Bharti Airtel while Exhibit 18 does the same for Reliance

Communications. The shift in Reliance’s business strategy since the launch of GSM

services is quite apparent from the graph. Before Q4 2009, Reliance had largely CDMA

services and was more focussed on quality of service rather than subscriber addition.

Post the launch of GSM service, Reliance has focussed more on net additions of

subscribers. It is also interesting to note that Reliance hardly has any weight on ARPU

compared to Bharti. Whether this is a consequence of a business strategy or the fact

that Bharti has high ARPU customers compared to Reliance is a matter of contention.



38

LIMITATIONS AND FURTHER WORK

In this project, we have made an effort to benchmark telecom circles as well as

operators on various parameters of business efficiency like subscriber acquisition,

quality of service and ARPU. For this, we have used input data available publicly. The

scope of analysis and research could be further enriched if some proprietary data was

available. For example, we used spectrum charges as a substitute for the amount of

spectrum held by an operator in the inter-circle DEA Analysis. Ideally, bandwidth of

spectrum available and number of towers in a circle should have been used to have a

better estimate of efficiency scores. In addition, if capital and operating expenditures in

each circle were available, it would have further improved the DEA model.

The DEA analyses conducted was for the purposes of benchmarking and not predictive.

As suggested by Zhou et al, it is possible to determine best practices in the industry and

suggest solutions of business development. Our model could be further extended to

more operators and the ones with the highest efficiency scores would establish the best

practices in the telecom industry. The operators with lower efficiency scores could then

be suggested ways for reengineering or improvement.



39

REFERENCES

Asmild, M., Paradi, J. C., Reese, D. N., Tam, F. (2007), “measuring Overall efficiency and

effectiveness using DEA”, European Journal of Operations Research, Vol. 178, pp. 305-

321

Charnes, Cooper, Lewin, Seiford, “Data Envelopment Analysis – Theory, Methodology

and Applications” Kluwer Academic Publications 1997

Charnes, A., Cooper, W. W. & Rhodes, E. (1978), “Measuring the efficiency of decision

making units”, European Journal of Operational Research, Vol. 2, pp. 429-444

Cygnus Vol 903, 2009. Retrieved on January 13, 2010 from

http://site.securities.com/doc_pdf?pc=IN&doc_id=210517237&auto=1&query=telecom

%3A&db=en_1y_d&hlc=en&range=365&sort_by=Date

Debnath, R. M. & Shankar R. (2008), “Benchmarking Telecommunication service in

India”, Benchmarking: An International Journal, Vol. 15 No. 5, pp. 584-598

Greasley, A. (2005), “Using Simulation and DEA in Guiding Operating Units to Improved

Performance, The Journal of Operations Research Society, Vol. 56, No. 6, pp727-731

The Indian Telecom Service Performance Indicators, July-September 2009, Telecom

Regulatory Authority of India, 7th January 2010

Kurup, R. (March 5, 2009). DoT selects Telcordia, Syniverse for MNP tech. Business

Standard. Retrieved January 13, 2010 from http://www.business-

standard.com/india/news/dot-selects-telcordia-syniverse-for-mnp-tech/350970/

Mookerji, N. (2008, July 9) Indian mobile user base may touch 559m by 2011. Retrieved

January 16, 2010 from http://www.dnaindia.com/report.asp?newsid=1176514

Narayana, M. R. (2008, February) Telecommunication Services and EconomicGrowth:

Evidence from India. Retrieved January 16, 2010 from

www.e.u-tokyo.ac.jp/cirje/research/dp/2008/2008cf545.pdf

http://site.securities.com/doc_pdf?pc=IN&doc_id=210517237&auto=1&query=telecom%3A&db=en_1y_d&hlc=en&range=365&sort_by=Datehttp://site.securities.com/doc_pdf?pc=IN&doc_id=210517237&auto=1&query=telecom%3A&db=en_1y_d&hlc=en&range=365&sort_by=Datehttp://www.e.u-tokyo.ac.jp/cirje/research/dp/2008/2008cf545.pdf



40

Norman, Stoker, “Data Envelopment Analysis – The Assessment of Performance”, John

Wiley and Sons 1991.

Rediff News. (January 11, 2008). DoT issues 9 LoIs, earns Rs 6,500 cr licence fee.

Retrieved January 13, 2010 from http://in.rediff.com/money/2008/jan/11telecom.htm

TRAI, Press Release No. 16/2009. Retrieved January 13, 2010 from

http://www.trai.gov.in/WriteReadData/trai/upload/PressReleases/649/pr20feb09no16.

pdf

TRAI Recommendations on Growth of Value Added Services and Regulatory Issues

(February 13, 2009). Retrieved January 13, 2010 from

http://www.trai.gov.in/WriteReadData/trai/upload/Recommendations/108/recom13fe

b09.pdf

Tsai, H. C., Chen, C. M, Tzeng G. H. (2006), “The comparative productive efficiency for

global telecoms”, International Journal of Production Economics, Vol. 103, pp. 509-526

Yang, H., Chang, C. (2009), “Using DEA window analysis to measure efficiencies of

Taiwan’s integrated telecommunication firms”, Telecommunications Policy, Vol. 33, pp.

98-108

Quarterly reports and annual reports were obtained from the websites of Bharti Airtel

Ltd, Reliance Communications Ltd. and Idea Cellular Ltd.

http://in.rediff.com/money/2008/jan/11telecom.htmhttp://www.trai.gov.in/WriteReadData/trai/upload/PressReleases/649/pr20feb09no16.pdfhttp://www.trai.gov.in/WriteReadData/trai/upload/PressReleases/649/pr20feb09no16.pdf



41

Exhibit 1: Framework of Indian Telecom Industry



Exhibit 2: Quality performance of Bharti Airtel for the quarter ending September 2009

Circles (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n)

AP 0.18% 0.53% 96.74% 0.62% 1.30% 1.44% 11.59% 95.34% 0.10% 0.00% 100% 97.72% 92.00% 99.00%

Assam 1.87% 12.59% 95.76% 0.69% 1.68% 2.01% 16.37% 90.76% 0.01% 0.00% 100% 98.19% 49.00% 99.00%

Bihar 1.25% 10.98% 93.91% 2.10% 1.72% 1.79% 13.45% 96.27% 0.00% 0.00% 100% 95.30% 75.00% 100%

Chennai 0.15% 0.71% 98.23% 0.20% 0.12% 1.08% 4.19% 98.12% 0.00% 0.00% 100% 95.14% 93.00% 99.00%

Delhi 0.31% 1.37% 98.89% 0.19% 0.17% 1.03% 4.55% 95.39% 0.03% 0.00% 100% 97.72% 86.00% 87.00%

Gujarat 0.12% 1.02% 98.36% 0.32% 0.40% 1.60% 15.33% 97.74% 0.06% 0.00% 100% 98.89% 90.00% 95.00%

HP 0.19% 0.43% 98.30% 0.25% 0.29% 1.14% 6.96% 97.66% 0.00% 0.00% 100% 99.42% 91.00% 97.00%

HR 0.25% 0.23% 97.91% 0.36% 0.52% 1.46% 10.67% 96.78% 0.00% 0.00% 100% 98.30% 70.00% 99.00%

J&K 0.29% 1.36% 97.40% 0.52% 0.68% 1.57% 12.27% 96.27% 0.02% 0.00% 100% 100% 83.00% 100%

Kolkata 0.22% 1.39% 98.99% 0.14% 0.09% 0.87% 3.59% 96.97% 0.03% 0.00% 100% 99.81% 69.00% 95.74%

Kerala 0.08% 0.24% 98.62% 0.17% 0.20% 1.14% 11.53% 98.19% 0.00% 0.00% 100% 96.46% 78.00% 99.00%

Karnataka 0.96% 5.16% 96.29% 0.92% 1.39% 1.82% 14.48% 94.54% 0.05% 0.00% 100% 96.03% 72.00% 97.00%

MH 0.90% 1.61% 97.30% 0.48% 0.70% 1.47% 16.28% 93.83% 0.14% 0.03% 100% 98.29% 94.00% 90.00%

MP 0.40% 2.00% 98.34% 0.21% 0.42% 1.44% 15.03% 95.90% 0.08% 0.00% 100% 98.88% 94.00% 88.00%

Mum 0.40% 1.19% 97.84% 0.11% 0.23% 0.99% 5.29% 97.50% 0.07% 0.00% 100% 98.52% 79.00% 89.00%

NE 10.26% 44.98% 88.41% 4.28% 4.92% 2.96% 25.78% 87.38% 0.01% 0.00% 100% 99.83% 58.00% 99.00%

Orissa 0.23% 1.34% 97.39% 0.35% 0.43% 1.64% 12.36% 97.87% 0.08% 0.00% 100% 97.31% 60.00% 94.00%

Punjab 0.18% 0.62% 98.07% 0.21% 0.26% 1.45% 12.35% 97.47% 0.00% 0.00% 100% 97.70% 84.00% 99.00%



Raj 0.49% 1.50% 96.12% 0.94% 1.17% 1.69% 14.38% 93.18% 0.04% 0.00% 100% 97.29% 85.00% 100.0%

TN 0.26% 0.79% 96.64% 0.80% 0.82% 1.10% 11.79% 96.14% 0.00% 0.00% 100% 95.14% 93.00% 99.00%

UPE 0.67% 4.05% 95.38% 1.07% 1.66% 2.05% 19.49% 91.34% 0.02% 0.01% 100% 83.28% 88.00% 98.00%

UPW 0.45% 2.22% 96.87% 0.73% 1.35% 1.17% 11.31% 95.73% 0.11% 0.00% 100% 95.10% 81.00% 99.00%

WB 0.42% 2.91% 96.28% 1.09% 1.12% 1.59% 15.91% 96.87% 0.02% 0.01% 100% 99.81% 69.00% 90.00%

Exhibit 3: Quality performance of Bharti Airtel for the quarter ending September 2009


AP 0.13% 0.87% 99.44% 0.00% 0.08% 0.77% 1.78% 99.54% 0.10% 0.03% 100% 93.00% 84.00% 100.00%

Assam 0.15% 1.32% 97.04% 0.55% 1.71% 0.85% 0.27% 96.00% 0.02% 0.07% 100% 86.00% 99.00% 100.00%

Bihar 0.46% 1.23% 98.64% 0.00% 0.72% 1.13% 1.26% 96.89% 0.10% 0.03% 100% 85.00% 83.00% 100.00%

Chennai 0.13% 0.56% 99.59% 0.00% 0.12% 0.69% 1.35% 99.00% 0.07% 0.01% 100% 90.00% 82.00% 100.00%

Delhi 0.12% 0.80% 99.35% 0.00% 0.19% 0.75% 1.88% 99.40% 0.10% 0.02% 100% 88.00% 84.00% 100.00%

Gujarat 0.12% 0.74% 99.48% 0.00% 0.13% 0.63% 1.03% 99.84% 0.10% 0.03% 100% 91.00% 75.00% 100.00%

HP 0.22% 1.23% 99.41% 0.00% 0.42% 1.02% 2.75% 98.13% 0.10% 0.01% 100% 93.00% 75.00% 100.00%

HR 0.20% 1.27% 99.12% 0.00% 0.31% 1.16% 1.15% 97.29% 0.10% 0.02% 100% 89.00% 80.00% 100.00%

Kolkata 0.17% 0.00% 99.49% 0.00% 0.23% 0.81% 1.73% 98.85% 0.11% 0.04% 100% 88.00% 78.00% 100.00%

Kerala 0.15% 0.28% 99.56% 0.00% 0.11% 0.78% 1.42% 98.97% 0.10% 0.04% 100% 95.00% 88.00% 100.00%

Karnataka 0.17% 0.54% 99.43% 0.00% 0.11% 0.75% 1.43% 99.46% 0.10% 0.03% 100% 95.00% 90.00% 100.00%

MH 0.17% 0.27% 99.42% 0.00% 0.16% 0.76% 0.60% 99.01% 0.10% 0.02% 100% 87.00% 81.00% 100.00%



MP 0.25% 0.82% 99.29% 0.00% 0.14% 0.73% 0.96% 98.58% 0.10% 0.04% 100% 89.00% 91.00% 100.00%

Mum 0.33% 0.58% 99.65% 0.00% 0.09% 0.84% 0.58% 97.92% 0.10% 0.04% 100% 92.00% 88.00% 100.00%

NE 0.18% 1.51% 97.48% 0.49% 1.21% 0.82% 0.22% 96.00% 0.01% 0.03% 100% 89.00% 90.00% 100.00%

Orissa 0.14% 0.31% 99.62% 0.00% 0.33% 0.90% 0.47% 99.17% 0.11% 0.08% 100% 94.00% 91.00% 100.00%

Punjab 0.16% 0.78% 98.33% 0.00% 0.24% 0.87% 1.35% 99.61% 0.10% 0.01% 100% 90.00% 75.00% 100.00%

Raj 0.18% 0.63% 98.88% 0.00% 0.20% 0.88% 0.87% 98.88% 0.11% 0.02% 100% 89.00% 89.00% 100.00%

TN 0.15% 0.51% 99.51% 0.00% 0.10% 0.76% 0.71% 98.04% 0.09% 0.01% 100% 90.00% 72.00% 100.00%

UPE 0.24% 0.96% 99.08% 0.00% 0.43% 0.94% 0.62% 98.88% 0.10% 0.01% 100% 87.00% 78.00% 100.00%

UPW 0.24% 0.40% 99.23% 0.00% 0.28% 1.04% 1.73% 99.48% 0.10% 0.03% 100% 89.00% 75.00% 100.00%

WB 0.28% 1.31% 99.14% 0.00% 0.24% 1.21% 1.30% 97.91% 0.10% 0.03% 100% 88.00% 78.00% 100.00%

Exhibit 4: Quality performance of Idea Cellular for the quarter ending September 2009


AP 0.04% 0.03% 99.92% 0.38% 0.41% 0.73% 4.80% 96.35% 0.03% 0.01% 100% 99.83% 98.00% 100.00%

Bihar 1.09% 1.02% 99.58% 0.64% 1.48% 1.40% 4.26% 95.74% 0.23% 0.01% 100% 36.30% 76.00% 75.00%

Delhi 0.08% 0.08% 99.08% 0.15% 0.57% 0.72% 2.42% 98.32% 0.00% 0.01% 100% 97.63% 77.00% 99.00%

Gujarat 0.07% 0.27% 99.43% 0.21% 0.14% 1.30% 8.52% 96.37% 0.05% 0.02% 100% 99.44% 98.00% 99.86%

HP 0.00% 0.00% 99.80% 0.14% 0.25% 1.86% 20.29% 96.99% 0.00% 0.02% 100% 99.42% 87.00% 80.00%

HR 0.16% 0.87% 99.87% 0.21% 0.40% 1.23% 10.11% 96.63% 0.03% 0.03% 100% 99.81% 88.00% 100.00%

Kerala 0.04% 0.08% 99.78% 0.18% 0.32% 1.14% 4.61% 96.47% 0.07% 0.01% 100% 99.02% 94.00% 100.00%



Karnataka 0.11% 0.46% 98.11% 0.08% 0.43% 1.39% 4.58% 97.43% 0.03% 0.06% 100% 99.80% 97.00% 100.00%

MH 0.59% 1.85% 98.23% 0.90% 1.37% 1.47% 10.59% 97.19% 0.05% 0.12% 100% 98.94% 99.00% 98.00%

MP 0.74% 1.94% 98.08% 0.86% 1.30% 1.73% 13.72% 95.39% 0.01% 0.02% 100% 97.95% 85.00% 100.00%

Mum 0.05% 0.18% 99.15% 0.07% 0.20% 0.89% 8.94% 97.80% 0.11% 0.08% 100% 98.30% 83.00% 99.64%

Orissa 0.10% 0.35% 98.88% 0.18% 0.49% 1.16% 4.85% 96.57% 0.00% 0.32% 100% 98.66% 90.00% 100.00%

Punjab 0.06% 0.61% 98.86% 0.05% 0.43% 0.79% 9.23% 97.96% 0.02% 0.01% 100% 87.00% 89.00% 100.00%

Raj 0.23% 0.24% 99.63% 0.30% 0.24% 1.25% 13.49% 97.75% 0.06% 0.02% 100% 99.36% 93.00% 100.00%

TN 0.04% 0.00% 98.76% 0.12% 0.18% 0.72% 8.19% 98.85% 0.03% 0.00% 99.90% 96.16% 100.00% 100.00%

UPE 0.37% 0.41% 99.75% 0.30% 0.95% 0.95% 7.04% 96.61% 0.02% 0.01% 100% 98.83% 96.00% 100.00%

UPW 0.30% 1.47% 99.82% 0.47% 1.31% 1.25% 8.00% 99.30% 0.06% 0.01% 100% 93.12% 94.00% 99.96%



Exhibit 5: Factor Analysis of Quality of Service Data for Q2 2010

Variable Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7

BTSs Accumulated downtime (not available for service) (%age) 0.759 -0.222 0.281 0.133 -0.32 -0.239 0.246

Worst affected BTSs due to downtime (%age) 0.856 -0.168 0.134 0.085 -0.252 -0.217 0.11

Call Set-up Success Rate (within licensee's own network) -0.939 -0.022 0.135 0.089 0.007 0.071 0.178

SDCCH/ Paging Chl. Congestion (%age) 0.914 0.004 -0.107 -0.162 0.012 -0.171 -0.224

TCH Congestion (%age) 0.926 0.018 0.022 -0.167 0.03 -0.149 -0.19

Call Drop Rate (%age) 0.859 0.104 0.113 0.086 0.257 0.191 -0.119

Worst affected cells having more than 3% TCH drop rate (%age) 0.792 0.086 -0.107 0.174 0.289 0.298 0.051

Connection with good voice quality -0.763 0.12 -0.207 -0.12 -0.225 -0.273 -0.314

Metering and billing credibility - pre paid 0.43 0.174 -0.695 -0.405 -0.199 0.127 0.274

Accessibility of call centre/ customer CARE 0.162 0.543 -0.309 0.71 -0.252 -0.011 -0.064

%age of calls answered by the operators (voice to voice) within 60 sec -0.049 0.778 0.139 -0.124 0.358 -0.435 0.199

%age requests for Termination/Closure of service within 7 days 0.129 0.619 0.47 -0.288 -0.396 0.363 -0.065

Variance 6.0634 1.4257 1.0135 0.9066 0.7555 0.6975 0.4291

% Var 0.505 0.119 0.084 0.076 0.063 0.058 0.036

Cumulative % var 0.505 0.624 0.708 0.784 0.847 0.905 0.941



47

Exhibit 6: Correlation coefficient between Factor 1 and Network Quality parameters

BTSs Accumulated downtime (not available for service) (%age) 0.729598

Worst affected BTSs due to downtime (%age) 0.860046

Call Set-up Success Rate (within licensee's own network) -0.89207

SDCCH/ Paging Chl. Congestion (%age) 0.870217

TCH Congestion (%age) 0.863465

Call Drop Rate (%age) 0.845623

Worst affected cells having more than 3% TCH drop (call drop) rate (%age) 0.880793

Connection with good voice quality -0.76267



48

Exhibit 7: Quality of Service - Efficiency Scores of Bharti Airtel

Circle ARPU (INR) Spectrum Charges (INR) Efficiency Score

AP 267.9 354.4 1.000

Assam 243.9 43.4 0.993

Bihar 193.9 232.9 1.000

Delhi 537.4 253.0 0.468

Gujarat 217.9 90.6 0.998

HP 316.7 25.9 1.000

HR 227.2 27.8 1.000

J&K 314.8 50.2 0.886

Kolkata 276.9 80.1 0.799

Kerala 292.3 57.2 0.781

Karnataka 301.8 386.6 0.689

MH 235.0 176.9 1.000

MP 205.7 99.2 1.000

Mum 471.2 151.7 0.814

NE 297.2 28.2 1.000

Orissa 195.0 91.7 1.000

Punjab 328.6 148.8 0.712

Raj 215.4 216.2 0.979

TN 377.4 350.1 0.610

UPE 192.3 168.0 1.000

UPW 217.1 58.2 1.000

WB 163.6 66.9 1.000



49

Exhibit 8: Quality of Service - Efficiency Scores of Reliance Communication

Circles No. of

Subscribers

ARPU

Efficiency Scores

AP 6,047,777 123.1 0.812

Assam 1,480,694 175.9 1.000

Bihar 6,471,721 114.3 1.000

Delhi 4,765,710 211.9 0.488

Gujarat 4,815,759 101.8 0.857

HP 1,080,946 119.1 1.000

HR 2,077,552 83.1 1.000

Kolkata 3,262,426 142.8 0.827

Kerala 2,955,854 147.7 0.768

Karnataka 4,796,870 124.9 0.769

MH 5,710,351 111.4 0.801

MP 7,201,126 109.5 1.000

Mum 5,074,915 211.2 0.677

NE 471,717 139 1.000

Orissa 2,415,929 128.6 1.000

Punjab 2,000,811 106.3 0.909

Raj 3,789,338 91.6 1.000

TN 4,701,454 139.2 0.604

UPE 6,482,869 94.1 1.000

UPW 4,985,488 91.5 1.000

WB 3,788,149 84.3 1.000



50

Exhibit 9: Four-in-one plot of final regression variables

420-2-4

99.9

99

90

50

10

1

0.1

Standardized Residual

Pe

rce

nt

-1.3-1.4-1.5-1.6-1.7

3.0

1.5

0.0

-1.5

-3.0

Fitted Value

Sta

nd

ard

ize

d R

esid

ua

l

3210-1-2

40

30

20

10

0

Standardized Residual

Fre

qu

en

cy

140

130

120

110

1009080706050403020101

3.0

1.5

0.0

-1.5

-3.0

Observation Order

Sta

nd

ard

ize

d R

esid

ua

l

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for dependent



51

Exhibit 10: Quality of Service - Efficiency Scores of Idea Cellular

Circles ARPU (INR) Spectrum Charges (INR) Efficiency Scores

AP 221.7 148.8 0.497

Bihar 111.0 16 1.000

Delhi 319.9 94.1 0.316

Gujarat 196.3 75.7 0.545

HP 245.9 2.2 1.000

HR 205.3 26.9 0.893

Kerala 237.2 13.02 0.463

Karnataka 198.1 32.3 0.609

MH 220.8 199.4 1.000

MP 181.3 124.9 1.000

Mum 249.0 11.6 0.946

Orissa 117.8 2.4 1.000

Punjab 227.0 74.9 0.545

Raj 137.3 20.3 0.857

TN 60.6 3.1 1.000

UPE 161.6 35.8 0.684

UPW 203.6 120.6 0.807



52

Exhibit 11: Comparison of Quality of Service Efficiency of operators in different circles

Exhibit 12: Comparison of Efficiency across various types of circles

0.000

0.200

0.400

0.600

0.800

1.000

1.200

RCOM Idea

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Metros Circle A Circle B Circle C

Pe

rce

nta

ge o

f C

ircl

e O

pe

rati

on

s

= 0.5 = 0.9



53

Exhibit 13: Input Parameters for inter-firm DEA Analysis

DMU Capex (INR mm) Operating Expense (INR mm)

Bharti Airtel Q1 2009 20,936 47932

RCOM Q1 2009 13653 14578

Idea Cellular Q1 2009 32,508 24564

Bharti Airtel Q2 2009 20,936 50,834

RCOM Q2 2009 13653 16,969

Idea Cellular Q2 2009 32,508 26,497

Bharti Airtel Q3 2009 20,936 54429

RCOM Q3 2009 13653 20337

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