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
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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
Analysis of Business Efficiency of Indian Telecom Sector
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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.
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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
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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:
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
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.
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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:
The regression equation is
dependent = 0.178 + 0.628 a + 0.894 b - 1.15 c + 1.33 e + 0.674 f + 0.802 g - 0.726 h
Predictor Coef SE Coef T P VIF
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
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3. Now, variable ‘e’ is the one having the highest Variance Inflation factor. Hence ‘e’ was
removed from the dataset. Then,
The regression equation is
dependent = 0.517 + 0.714 a + 0.926 b - 1.53 c + 1.31 f + 0.778 g - 0.698 h
Predictor Coef SE Coef T P VIF
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
Predictor Coef SE Coef T P VIF
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
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Predictor Coef SE Coef T P VIF
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:
BTSs Accumulated downtime (not available for service) (%age)
Call Set-up Success Rate (within licensee's own network)
Worst affected cells having more than 3% TCH drop (call drop) rate (%age)
Connection with good voice quality
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
Analysis of Business Efficiency of Indian Telecom Sector
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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.
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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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
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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
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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
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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.
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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
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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
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Indian Institute of Management Ahmedabad
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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
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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.
Analysis of Business Efficiency of Indian Telecom Sector
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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.
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Indian Institute of Management Ahmedabad
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Exhibit 1: Framework of Indian Telecom Industry
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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%
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Circles (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n)
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%
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Circles (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n)
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%
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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%
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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
Analysis of Business Efficiency of Indian Telecom Sector
Indian Institute of Management Ahmedabad
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