Brm Final Project 12th Jan

8/14/2019 Brm Final Project 12th Jan

1/33

SUBMI

SUBMI

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12THJa

TED TO

TED BY

NIn09

:Prof.TRI

SUMEET

GMM SU

MVK

CH

MUKESH

BILLP

COVERAGE

ONPAY

LOCHAN

SINGH

NIL KUM

AITANYA

KANDH

CE

SPR

G

YMENT

LINEMENT

RIPATH

R

L

LLULA

RVICEVIDER

RACEPERIOD

EASYRECHARGE

S

TARIFF PLA

CO

ISD

S

NECTIVITY

1


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ACKNOWLEDGEMENTSWe extend our sincere gratitude to Prof. Trilochan Tripathy without whose support and

inspiration this project would not have been possible. We would like to take this opportunity to

thank our faculty for the genuine pieces of advice which he has given from time to time for the

completion of this project.

.

We would also like to thank all our project mates and fellow students who extended their help

and support whenever required. A special thanks to all who provided their knowledgeable insight

into things of complexity and made them simple and lucid for us.

A sincere thanks to our Professor who guided us to collect the primary data and even to all those

people who have added value to our project work through the questionnaire they had filled on

due course of the project work.

Last but not the least, my warm heartfelt thanks to IBS, Hyderabad for providing us with the

facilities required to do the adequate research and give the project its final shape.


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TABLEOFCONTENTS:

PARTICULARS PAGENO.

EXECUTIVE SUMMARY 4RATIONALE OF CHOOSINGTHE TOPIC 6OBJECTIVE OF PROJECT 7HYPOTHESIS 7APPROACH TO THE PROBLEM 7LITERATURE SURVEY 8DATA COLLECTION METHODOLOGY 9OVERALLMARKET SHARE 10RESEARCH DESIGN 13TOOLS USED 13CLUSTER ANALYSIS 14FACTOR ANALYSIS 19CORRELATION 29REFERENCES 30QUESTIONNAIRE 31


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EXECUTIVESUMMARY

Satisfaction is a persons feelings of pleasure or disappointment resulting from comparing

a products perceived performance in relation to his/her expectations.

According to both marketing theory and practical experience, telecommunications firms should

improve their performance by satisfying their customers, so as to obtain and sustain advantage in

the intensely competitive environment. This is because customer satisfaction results in customer

loyalty and firms with a bigger share of loyal customers profit from increasing repurchase rate,

greater cross buying potential, higher price willingness, positive recommendation behavior

and lower switching tendency.

Here in this research we have tried to understand perceived value proposition and customer

satisfaction towards various cellular operators.

The study on customers was quantitative study of existing customers of all operators. Here in we

have tried to clarify certain factors related to customers satisfaction in current scenario.

Research DesignAdopted:DESCRIPTIVE

Data CollectionApproach:QUESTIONAIRE DISTRIBUTION

SampleSpace: 70

Sampling TechniqueUsed: RANDOM SAMPLING

PrimaryDataCollectionSource:

IBS,Hyderabad

TCS,DeccanPark

STPI,Madhapur

HSBC,Hyderabad


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This is because it was intended to find the market characteristics such as market size, attitude of

the customers towards the different aspects of the service and so on; we adopted questionnaire

approach as a primary data collection technique. Both open ended and closed ended questions

were used in the questionnaire to get better understanding of the problem.

To eliminate all kinds of bias in the research and to attain the true picture we adopted random

sampling in our research work.

Here in order to measure the perceived preference level of the customer and there deviation we

have applied multivariate analysis tools such as factor analysis and cluster analysis.

The outcome of the research work has helped us to identify the parameters that the customers

keep in mind while selecting a cellular service provider.

There are 25 private companies operating in 23 telecom circles and four metro cities, covering

1500 towns across the country. There are two types of mobile service networks namely GSM

(Global System for Mobile) and CDMA (Code Division Multiple Access). The sector at present

is witnessing a fierce competition to acquire maximum number of new subscribers with various

price cuts and life time offers, as it is very difficult to make an existing subscriber switch

services.

With regular price cuts it becomes very difficult for existing players to offer premium quality

services to make their users happy, which then boils down to offering regular skeletal services

but with consistency and inbuilt quality which is the least the consumer expects.

Here in our study, we attempt to measure the satisfaction level of existing subscribers of three

service providers namely Idea, Tata Indicom, Reliance, Vodafone and Airtel in our campus, IBS

Hyderabad as well as in TCS Hyderabad (Deccan Office, Hi-Tec City)


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RATIONALE OF CHOOSINGTHE TOPIC

The most uncertain things in the current competitive world is customers perception towards a

product or service. The most important objective from marketing point of view is to understand

the customer perception towards their product. Telecom Industry is booming, what we can say is

the period of telecom revolution. The number of cellular service providers is increasing and there

exists a wide variety of choice before the customers to choose a particular provider. So it

becomes a daunting task before the cellular players to provide best value proposition and

increased customer satisfaction towards their subscribers.

It is essential from business point of view to analyze the parameters which are essential for the

customers to choose a particular cellular operator. Moreover its important to understand the

factors which are responsible in getting increased revenues for the business and which has helped

in increasing the customer base of the cellular operator.

The Telecom Industry in India is still evolving and there are no signs of consolidation as of now.

Currently the industry is having a subscriber base of 230 million with an estimated 5-6 million

subscribers being added every month. Price war, subscriber base, decreasing ARPUs (Average

revenue per user), Technology issues, target market, value added services, network up gradation

are the issues of major concern to all the players. Government has already given green signals for

FDI participation to be raised to 74% and unified licensing regime is there. With government

decision to implement unified licensing Regime, CDMA players like Reliance, Tata have got

seamless mobility and would be at par with GSM players. GSM operators will be forced to

match the prices along with installing new capacities and upgrading the existing ones. This has

made the companies decrease the tariffs to the lowest in the world that is from the incoming and

outgoing call charges of Rs18/minute (0.45 USD) seven years ago to the present day when then

incoming calls are free and outgoing are charged as low as Rs 1/minute (0.02 USD). Under easy

payment plans you can get a lifetime incoming call for free!

There has been a tremendous growth in the telecom sector in India and doing a research project

to analyze the customer preference towards the cellular service providers is definitely a

knowledge gaining and it would help in understanding the sector much better.


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OBJECTIVE OF PROJECT

Here with the help of this research we are trying to determine the overall perceived value

proposition of the service and satisfaction levels of the customers of different mobile operators.

The major objectives behind this research project were:

To understand current satisfaction level of customers with regard to different attributes of

mobile services.

To determine the preferences of the customers for different mobile service providers.

To ascertain the improvements expected by the customers in services provided.

To find out the reason behind increasing customer base of a cellular service provider.

HYPOTHESIS

Null Hypothesis: There exists no relationship between the variables we surveyed and the overall

customer satisfaction towards a cellular service provider.

Alternative Hypothesis: There exists a relationship between the variables we surveyed and the

overall customer satisfaction towards a cellular service provider.

APPROACHTOTHEPROBLEM

The market research will be done in understanding perceived value proposition and customer

satisfaction of Indian mobile industry. The study on customers will be quantitative study of

existing customers of major operators, operating in Hyderabad. The research design adopted by

us will be descriptive design and we are following questionnaire approach as data collectiontechnique. Closed ended questions will be used in the questionnaire to get better understanding

of the problem.


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LITERATURESURVEY

Level of satisfaction is a persons feeling of contentment or disappointment resulting from

comparing a products performance in relation to his/her expectations. The buying behaviorof the consumer starts with the search of information about the product based on his need. Then

after evaluating the set of option a consumer decided to purchase the product. In the end he

evaluates his decision which then decides his level of satisfaction. As per the insights by

different authors namely, (Philip Kotler, 2000).

According to both marketing theory and practical experience, telecommunications firms should

improve their performance by satisfying their customers, so as to obtain and sustainable

advantage in the intensely competitive environment to get heart and mind share. 4 As ofoperationalising customer centric marketing (Rajendra S Sisodia and Jagdesh Seth) can

help to meet this end. This is because customer satisfaction results in customer loyalty and firms

with a bigger share of loyal customers profit from increasing repurchase rate, greater cross

buying potential, higher price willingness, and lower switching tendency.

As stated in the case study by Fulbagh Singh and Reema Sharma in their research paper

Cellular services And Customer Buying Behavior in Amritsar City, There is a need to change

the image the product for status symbol to product of necessity to increase the clientele base. For

this the cellular company must further reduce the per minute charges and introduce more flexible

plan schemes.


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DATACOLLECTIONMETHODOLOGY

PrimaryandSecondaryDataSources

Questionnaire Method was used containing Close Ended Questions.

Secondary Data Sources of Customer Satisfaction Report of CRISIL and other journal

and research papers were used to discern customer satisfaction factors of service

providers in the Hyderabad telecom circle.

Samplingtechniques

The sampling technique used in the study is Convenience sampling. More specifically

Judgmental sampling technique which lets the researcher judge whether the respondent will truly

give his response without any bias. Convenience Sampling has been administered on 100

respondents. Data was analyzed on the survey conducted on the ICFAI Hyderabad Campus.

ScalingTechniques

Likert scale to measure customer satisfaction. The scale is balance .and has responses

ranging from Not at all essential- 1, and if they feel the factor is Absolutely essential

mark 7

Multivariate Analysis tool using SPSS to measure perceived value proposition using tools

like

Factor Analysis

Cluster Analysis


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13

RESEARCHDESIGN

Descriptive Design

The research design chosen for the study was descriptive. This was because it was intended

to find the market characteristics such as market size, attitude of the customers towards the

different aspects of the service and so on.

.

Information Needed For the Research

Since the primary research was already conducted on customers, the information needs were

known well. The study required the following information to be extracted for doing the final

analysis.

What are the present satisfaction levels of the customers with respect to different

aspects of service provided?

What are the preferences of the customer with regard to the service provided?

Which aspects of the service have more predominant effect on the overall

Satisfaction levels of the customers?

TOOLSUSED

SPSS13.0forWindows

MicrosoftExcel

MicrosoftWord

The above mentioned tools were used in the completion of the project. Using SPSS 13.0 we

analysed the data through factor analysis and cluster analysis. Again using MS Excel we did the

required graphs to analyse the trend in the growth of prepaid or postpaid subscribers in the state

of Andhra Pradesh.


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CLUSTERANALYSIS

(IntroductionofClusterAnalysisandItsApplicationInThisSurvey)

Cluster analysis is an exploratory data analysis tool for solving classification problems. Its

object is to sort cases (people, things, events, etc) into groups, or clusters, so that the degree of

association is strong between members of the same cluster and weak between members of

different clusters. Each cluster thus describes, in terms of the data collected, the class to which

its members belong; and this description may be abstracted through use from the particular to the

general class or type.

K-means

clustering

The K-means algorithm assigns each point to the cluster whose center (also called centroid) is

nearest. The center is the average of all the points in the cluster that is, its coordinates are the

arithmetic mean for each dimension separately over all the points in the cluster...

Example: The data set has three dimensions and the cluster has two points: X = (x1, x2, x3) and

Y = (y1, y2, y3). Then the centroid Z becomes Z = (z1, z2, z3), where z1 = (x1 + y1)/2 and z2 =

(x2 + y2)/2 and z3 = (x3 + y3)/2.


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ANALYSISOFCLUSTERSINTHISSURVEY

Cluster Analysis was applied to this survey in order to segment our respondents base by service

usage patterns. If customers can be classified by usage, the company can offer more attractive

packages to its customers.

IterationHistory(a)

Iteration Change in Cluster Centers

1 2

1 7.107 9.0072 .813 .475

3 .025 .012

4 .001 .000

(a) Iterations stopped because the maximum number of iterations was performed. Iterations

failed to converge. The maximum absolute coordinate change for any center is .000. The current

iteration is 4. The minimum distance between initial centers is 20.664.

The iteration history shows the progress of the clustering process at each step. In early iterations,

the cluster centers shift quite a lot. By the 4th iteration, they have settled down to the general

area of their final location, and the last two iterations are minor adjustments.


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ANOVATABLE

The ANOVA table indicates which variables contribute the most to your cluster solution.

Variables with large F values provide the greatest separation between clusters.

ANOVA

Cluster Error

F Sig.Mean Square df Mean Square df

Tariff Plan 29.126 1 2.315 68 32.579 .001

Customer care 39.226 1 2.149 68 18.251 .000

GPRS/Internet 52.557 1 2.481 68 21.183 .000

Connectivity/congestion 5.085 1 2.804 68 1.813 .183

Dealer Support 50.631 1 2.087 68 24.256 .000

Easy Recharge/Bill

Payment 16.481 1 2.982 68 5.526 .022

Grievance Handling 4.601 1 2.408 68 1.911 .171

Roaming Facility 25.952 1 3.433 68 7.560 .008

ISD 17.443 1 2.564 68 6.804 .011

Grace Period 33.748 1 2.743 68 12.302 .001

MMS 2.084 1 2.999 68 .695 .407Sing tones &

Ringtones28.388 1 3.028 68 9.376 .003

Conferencing Facility 63.929 1 2.027 68 31.537 .000

Missed Call Intimation 34.310 1 3.370 68 10.180 .002

Closed User Group 20.651 1 2.372 68 8.706 .004

Bundled Packages 56.413 1 2.562 68 22.017 .000

Other Promotional

Schemes 15.410 1 2.684 68 5.741 .019

Privacy Agreement 38.925 1 2.664 68 14.612 .000Coverage 36.643 1 2.299 68 15.938 .000

Online Payment 26.198 1 2.810 68 9.323 .003


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In our case, we see that Tariff Plan contributes the maximum to the cluster solution i.e. the

respondents have given utmost importance to the Tariff plan. Other important factors

contributing which are considerably contributing to the cluster are Customer Care, GPRS,

Bundled packages, Dealer Support, Conferencing.

The F test is used only for descriptive purposes because the clusters have been chosen to

maximize the differences among cases in different clusters. The observed significance levels are

not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster

means are equal.

FINALCLUSTERCENTERS

Thevaluesinthetablearethemeansforeachvariablewithineachfinalcluster

Final Cluster Centers

Cluster

1 2

Tariff Plan 6 4

Customer care 5 4

GPRS/Internet 5 3Connectivity/congestion 5 4

Dealer Support 5 3Easy Recharge/Bill Payment 5 4

Grievance Handling 5 4

Roaming Facility 5 4

ISD 5 4

Grace Period 4 4

MMS 4 4

Sing tones & Ringtones 5 4

Conferencing Facility 5 3

Missed Call Intimation 5 3

Closed User Group 5 5

Bundled Packages 5 3

Other Promotional Schemes 4 3

Privacy Agreement 5 4

Coverage 5 4

Online Payment 5 4


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It can be observed from the above table that the Tariff Plan has been given more importance by

the cluster 1 respondents rather than cluster 2.Similarly Customer care, Coverage, easy

Recharge, ISD, Conferencing have also been given more importance by Cluster 1 respondents

rather than Cluster 2 respondents.

Whereas variables like Grace Period, MMS, Closed User Group (CUG) is being equal

importance by customers belonging to both the clusters.

All in all the respondents of Cluster 1 can be viewed as more Tech Savvy and Sensitive towards

different services provided by mobile service providers. And the respondents of cluster 2 are not

as proactive towards using new technology and advanced communication services.

NUMBEROF

CASES

IN

EACH

CLUSTER

Number of Cases in each Cluster

This table shows how many cases were assigned to each cluster. In this survey 31 respondents

belonged to Cluster 1 and 39 have been found to be in Cluster 2.

Limitations

The findings are based on a survey conducted in Hyderabad so the results obtained might be

different from the behavior displayed from a more general sample.

This result cannot be generalized to all geographical regions in which the telecom service

providers operate.

There may be a bias in furnishing information from the respondents.

Cluster 1 31.000

2 39.000

Valid 70.000

Missing .000


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FACTORANALYSIS

The survey we conducted was on the customer satisfaction and preference level for various

mobile service providers.

The factors we considered for the same are :-

1. Tariff Plan

2. Customer Care

3. GPRS/Internet

4. Connectivity/Congestion

5. Dealer Support

6. Easy- Recharge/Bill Payment

7. Grievance Handling

8. Roaming Facility

9. ISD

10.Grace Period

11.MMS

12.Sing Tones & Ringtones

13.Conferencing Facility

14.Missed Call Information

15.Closed User Group

16.Bundled Packages

17.Other Promotional Schemes

18.Private Agreement

19.Coverage

20.Online Payment


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We have performed factor analysis on these variables. The various outputs through SPSS is

mentioned below,

KMOANDBARTLETT'STESTTable 1.3

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy..708

Bartlett's Test of

Sphericity

Approx. Chi-Square 397.049

Df 190

Sig. .000

Source: Output Sheet (appendix)

KMO test and Bartletts Test is basically the test of assumptions. The standard value for KMO

test is that it should be greater than 0.70 and is inadequate if less than .50. The underlying

assumptions behind the KMO test is that it indicates there is sufficient item for each factor. Here

in our test the value of KMO is .708 which shows that we have sufficient item for each factor.

The assumption of Bartletts test of sphericity is that it should be significant(less than .005)

indicating that correlation matrix is significantly different from identity matrix in which

correlations between the variables is zero. Here in our test significant value we have got is .000:

this means that the variables are correlated highly enough to provide a reasonable basis for factor

analysis. As per our analysis the approx chi square value is 397.049 and degree of freedom is 190.

This shows that data are fit for factor analysis.


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Table 1.4

Communalities

Initial Extraction

Tariff Plan 1.000 .688

Customer care 1.000 .697

GPRS/Internet 1.000 .646

Connectivity/congestion 1.000 .552

Dealer Support 1.000 .488

Easy Recharge/Bill

Payment1.000 .703

Grievance Handling 1.000 .749

Roaming Facility 1.000 .666

ISD 1.000 .796

Grace Period 1.000 .752

MMS 1.000 .663

Sing tones & Ringtones 1.000 .540

Conferencing Facility 1.000 .672

Missed Call Intimation 1.000 .720

Closed User Group 1.000 .744

Bundled Packages 1.000 .663

Other Promotional

Schemes1.000 .673

Privacy Agreement 1.000 .575

Coverage 1.000 .677

Online Payment 1.000 .708

Extraction Method: Principal Component Analysis.

Source: Output Sheet (Appendix)

Communality, h2, is the squared multiple correlation for the variable as dependent using the

factors as predictors. The communality measures the percent of variance in a given variable

explained by all the factors jointly and may be interpreted as the reliability of the indicator.


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Communality for a variable is computed as the sum of squared factor loadings for that variable

(row). Recall r-squared is the percent of variance explained, and since factors are uncorrelated,

the squared loadings may be added to get the total percent explained, which is what communality

is. For full orthogonal PCA(the one used in our analysis), the initial communality will be 1.0 for

all variables and all of the variance in the variables will be explained by all of the factors, which

will be as many as there are variables. The "extracted" communality is the percent of variance in

a given variable explained by the factors which are extracted, which will usually be fewer than

all the possible factors, resulting in coefficients less than 1.0.

When an indicator variable has a low communality (>=.25), the factor model is not working well

for that indicator and possibly it should be removed from the model. Low communalities across

the set of variables indicate the variables are little related to each other. However, if the

communality exceeds 1.0, there is a spurious solution, which may reflect too small a sample or

the researcher has too many or too few factors.


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Table 1.5

Total Variance Explained


Basically the total variance explained table tells about the variance explained by each factor.

Eigen values refer to the variance explained or accounted for. The % of variance shows the

percent of variance for each component before and after rotation. Cumulative variance basically

shows the percentage of variance shown by the variables whose Eigen value is greater than 1.

Here the Total Variance Explained table shows how the variance is divided among the 20

possible factors. We found seven factors have Eigen values (a measure of explained variance)

greater than 1.0 which is a common criterion for a factor to be useful. When the Eigen value is

less than 1.0, this means that the factor explains less information than a single item would have

explained. Here the first factor shows the highest variance i.e. it explained 24.291 % of the total

variance explained. If we sum up all the seven factors it basically explained 66.863 % of the total

variance. The computer would have looked for the best seven-factor solution by "rotating" seven

TotalVarianceExplained

Component InitialEigenvalues ExtractionSumsofSquaredLoadings RotationSumsofSquaredLoadings

Total %ofVariance Cumulative% Total %ofVariance Cumulative% Total %ofVariance Cumulative%1.00 4.86 24.29 24.29 4.86 24.29 24.29 2.93 14.64 14.642.00 1.84 9.18 33.47 1.84 9.18 33.47 2.20 11.00 25.643.00 1.61 8.03 41.51 1.61 8.03 41.51 1.89 9.44 35.074.00 1.53 7.66 49.17 1.53 7.66 49.17 1.74 8.70 43.775.00 1.36 6.79 55.96 1.36 6.79 55.96 1.69 8.43 52.206.00 1.15 5.75 61.71 1.15 5.75 61.71 1.54 7.70 59.907.00 1.03 5.15 66.86 1.03 5.15 66.86 1.39 6.96 66.868.00 0.91 4.54 71.409.00 0.81 4.07 75.47

10.00 0.77 3.87 79.3511.00 0.65 3.27 82.6112.00 0.62 3.12 85.7313.00 0.50 2.50 88.2314.00 0.46 2.30 90.5315.00 0.43 2.14 92.6716.00 0.36 1.79 94.4617.00 0.34 1.68 96.1318.00 0.31 1.56 97.7019.00 0.27 1.34 99.0420.00 0.19 0.96 100.00

:PrincipalComponentAnalysis.


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factors. The remaining 13 factors are not showing any variance due to their weakly correlated

nature.

For this we will use an orthogonal rotation (varimax). This means that the final factors will be as

uncorrelated as possible with each other. As a result, we can assume that the Information

explained by one factor is independent of the information in the other factors. We rotate the

factors so that they are easier to interpret. Rotation makes it so that, as much as possible,

different items are explained or predicted by different underlying factors, and each factor

explains more than one item. This is a condition called simple structure. One thing to look for in

the Rotated Matrix of factor loadings is the extent to which simple structure is achieved.


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The Rotated Factor Matrix table, which contains these loadings, is key for understanding the

results of the analysis. Here the computer have sorted the 20 factors into seven overlapping

group of items each which has a loading of 0.40 or higher (without considering the sign).

2019181716151413121110987654321

Component Number

5

4

3

2

1

0

Eigenvalue

Scree Plot


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Table 1.6

Rotated Component Matrix (a)

Components

1 2 3 4 5 6 7

Grace Period .792

Customer care .747

GPRS/Internet .741

Tariff Plan .619 .401

Bundled Packages .729

Online Payment .718

Privacy Agreement .639

Dealer Support .450 .456

Conferencing Facility .442 .439

Grievance Handling .777Sing tones & Ringtones .601

Roaming Facility .566 .478

Easy Recharge/Bill

Payment.741

Coverage .639 .443

ISD .848

Other Promotional

Schemes.761

MMS .806

Connectivity/congestion .505

Missed Call Intimation .741

Closed User Group .451 -.518

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 9 iterations.



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Table 1.7

Critical Factors

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7

Economic

Pack/Pricing

Customer

Friendliness

Customer

Support

Back

Accessibility Travel packEasy to

Connect

Add on

feature

Tariff PlanPrivacy

Agreement

Grievance

Handling

Easy

Recharge/Bill

Payment

ISD MMSMissed Call

Information

Grace PeriodOnline

Payment

Sing Tones

& Ring

Tones

Coverage

Other

Promotion

al Schemes

Connectivity/

Congestion

GPRS/InternetBundled

Packages

Roaming

Facility

Customer Care

Source: Data Analysis

Factor 1: Economic Pack/Pricing-

The rotated Matrix revealed that the respondents perceive this particular factor to be the most

important factor, with the highest explained variance of 14.64%. Four out of twenty service

features load on significantly to this factor. We have named this factor as economy pack as it

consists of various GPRS/Internet (free sms, free calls, grace period etc) along Grace Period

(Period after expiry of the validity). Tariff plan as per the convenience of customers and proper

connectivity, thus can be said to be the most crucial factors influencing the decision of mobile

service users while selecting particular service providers.

Factor 2: Customer Friendliness

This has come out as second most important factor with rotated explained variance of 11.00%.

Three types of feature were loaded on to this factor. Privacy Agreement, (No sharing of

Customer Data with other agencies), Online Payment (Payment of Post-Paid Bills through


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Internet in collaboration with various Banks) and Bundled Packages (various packages like

Closed User Group etc.)

Factor 3: Customer Support Back

Three types of featured are loaded on this factor. This factor consists of facilities like Grievance

Handling, Sing Tones & Ring Tones and Roaming Facility. The factor accounts for 9.44% of the

variance. Facilities like Sing Tones & Ring Tones are a boost for corporate and business

customers.

Factor 4: Accessibility

Two types of features are loaded on this factor and together they account for 8.70% of the

variance. Easy Re-charge/Bill Payment and Coverage has become one of the most crucial factors

to measure the accessibility of the customers. If a company is not able to handle the coverage of

customers then it is bound to lose the market share and its reputation in the market.

Factor 5: Travel Pack

It has been revealed that the fifth most important factor with explained variance of 8.43% the

various features which are loaded on this factor are ISD and Other Promotional Schemes. ISD

stands for International Subscriber Dialing, which is major source of Revenue for the company.

Other Promotional Schemes are very important factors for instant revenue for the company.

Factor 6: Easy to Connect

The rotated matrix reveals that the factor easy to connect accounts for 7.70% of the variance.

Two types of features are loaded on this factor. These features are MMS and

Connectivity/Congestion. This is very beneficial for frequently travelling customers.

Connectivity is also loaded on this factor, which is one of the most important factor when it

comes to a place (Dontanapalli, ICFAI), which is outside city limits.


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The above chart clearly shows that all the 20 different factors that we have taken is

positivelycorrelatedwiththeoverallsatisfactionlevelofthecustomers.

SowecansaythattheNullHypothesiswhichwehave takenshouldberejectedratherthe

Alternative

Hypothesis

should

be

accepted.

From the table we have found out that customer satisfaction towards cellular service

providersdependsmostlyonthecustomercareprovidedbytheoperators.Thisisbecause

bothcustomersatisfactionlevelsandcustomercarearefoundtobehighlycorrelated.

REFERENCES

www.google.com

www.crisil.com

www.auspi.in

www.coai.org

Rajendra S Sisodia and Jagdesh Seth (2004)4 As of operationalising customer centric

marketing.

Fulbagh Singh and Reema Sharma( Sep 2008) Cellular services And Customer Buying

Behavior in Amritsar City.


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QUESTIONNAIRE

Customer Satisfaction & Preference towards Mobile Service Providers

Name of the Respondent: Gender: Age:

Annual Income : < 1 Lakh < 3 Lakh More than 3 Lakh

Q1.) How many mobile connections do you have?

One Two Three More than Three

Q2.) Which mobile service do you use?

Airtel Idea BSNL Reliance Vodafone

Q3.) Is the connection

Postpaid Pre-paid Both

Q4.) Which is the best quality service Provider you know?

Airtel Idea BSNL Reliance Vodafone

RATINGS: If you feel the factor is Not at all essential mark 1, and if you feel the factor is

Absolutely essential mark 7. If your feelings are less strong mark one of the numbers in the middle.

There is no right or wrong answer-all we are interested in is the number that truly reflects your feelings

regarding the weight-age given to the factor.

Q4.) How satisfied are you with your service provider

RATING 1 2 3 4 5 6 7

Satisfaction Level


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Q5.)Rate your operator on following features.RATING 1 2 3 4 5 6 7

Tariff Plan

Customer care

GPRS/Internet

Connectivity/congestion

Dealer Support

Easy Recharge/Bill Payment

Grievance Handling

Roaming Facility

ISD

Grace Period

MMS

Sing tones & Ringtones

Conferencing Facility

Missed Call Intimation (when mobile is switchedoff)

Closed User Group

Bundled Packages *

Other Promotional Schemes**

Privacy Agreement+

Coverage

Online Payment

*Connection with a handset/1000 calls free etc.

**SMS /STD/LOCAL Pack


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+No promotional SMS/Calls coming from your service provider

Q6) How long have you been using the connection?

< 3 months < 6 months 1 year > 1 year

Q7) Are you attracted by features of other cellular service providers?

Yes No

Q8) Does your service connection has any tie ups with banks for easy monthly payments?

Yes No

Q9) Which Technology provides better connectivity and voice clarity?

GSM CDMA

Q10) Are you willing to continue with the existing cellular service provider?

Yes No Not Sure

Q11) When 3G services are launched would you switch over to a different cellular service

provider?

Yes No Not Sure

Q12) Would you recommend any of your colleagues or friends to use the current cellular service

provider that you are using?

Yes No Not Sure

Q13) Will you change your cellular service provider in-case of suggestions from your colleagues

or friends?

Yes No Not Sure

Q14) How many other members in your family use the connection provided by the cellular

provider as you are using?

One Two Three More than Three

Brm Final Project 12th Jan

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