rohit kumbhar_11020541115_market research report on cellular service providers

8/3/2019 Rohit Kumbhar_11020541115_Market Research Report on Cellular Service Providers

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Symbiosis Institute of Telecom Management


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Rohit Kumbhar Marketing & Finance

11020541115


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INDEX

TOPICPAGE NO

1. INTRODUCTION2

2. MARKET RESEARCH PROCESS 3

3. DISCRIMINANT ANALYSIS 5

4. FACTOR ANALYSIS10

5. CHI-SQUARE TEST12

6. CLUSTER ANALYSIS 137. APPENDIX (QUESTIONNAIRE) 20

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MARKET RESEARCH REPORT ON CELLULAR

SERVICE PROVIDERS


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INTRODUCTION

India has growing market for cellular services with rapidly increasingsubscriber base & new players entering market .It has become crucial for serviceproviders to research for consumers changing needs and preferences in thisregard.

Evolution of GSM, CDMA technologies, innovation in features of mobilehandsets has impacted consumer by increasing his choices which in turn hasaltered his needs and preferences. The subscriber base comprises of consumersfrom varied stratas of society. Thus service providers have to bring out thesymphony between services offered, segments of consumer, needs, preferencesetc.

This market research is aimed at finding the consumer behaviour, theirsensitivity towards price and other basic features provided by the cellular serviceproviders. A survey was conducted in the campus of Symbiosis and thequestionnaire was mailed to friends and relatives on email.

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1) Identify & Define Research problem

The research problem is formulated on the basis of latent problems faced &unnoticed opportunities for the cellular service provider.

Research problem: To study consumer behaviour in accordance toparameters like sensitivity to prices, features provided by service providers,segments of market in cellular services domain. The behaviour is analysed invarious aspects.

2) Choose the research design

Research design helps to provide a framework for conducting the research.

There are three basic types of research design:

a. Exploratory Researchb. Descriptive Researchc. Causal Research

Since we are interested in finding the consumer behaviour and theirinclination towards different features offered by the service provider, ourresearch work falls under Descriptive Research. Here we are trying to find outwhich features are perceived to be most important for the promotion of theservice. Based on the nature of the problem and scope of research our design isDescriptive research.

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1) Determine the information needs

The source of data can be secondary or primary for our research we haveopted for primary data. As we want to analyse current consumer behaviour.

2) Designing data collection forms

Survey method is used to collect the data. The survey was carried out withthe help of Questionnaire which is given. The questionnaire was designed withthe objective of solving the research problem.

3) Determining sample size & sample plan

Sample size was aimed as 50 respondents. The number of respondents whogave survey turned out to be 37.Analysis is done on basis of response of these37 respondents.

4) Organize and conduct research

The research was conducted by mailing the questionnaire to students incampus, friends & relatives.

5) Analysis and interpretation of data

The analysis of data was carried out based on the different objectives of sameresearch problem. The analysis done consist of Discriminant Analysis, ClusterAnalysis, Factor Analysis which are discussed in following matter.

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DISCRIMINANT ANALYSIS

Objective -To identify discrimination of different profession based on their,inclination toward the call rates, features like VAS, SMS packages, and QoSissues like Connectivity.

Output

Discriminant Analysis: Profession versus Call Rate, SMS Rate, Connectivity, VAS,Special Offers, Customer Care, GPRS

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discriminant.MPJ


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discriminant.MPJ

Linear Method for Response: Profession

Predictors: Call Rate, SMS Rate, Connectivity, VAS, Special Offers,

Customer Care, GPRS

Group Employed Housewife Student

Count 12 3 22

Summary of classification

True Group

Put into Group Employed Housewife Student

Employed 7 1 3

Housewife 3 2 0

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Student 2 0 19

Total N 12 3 22

N correct 7 2 19

Proportion 0.583 0.667 0.864

N = 37 N Correct = 28 Proportion Correct = 0.757

Squared Distance Between Groups

Employed Housewife Student

Employed 0.00000 2.45445 4.98121

Housewife 2.45445 0.00000 7.16372

Student 4.98121 7.16372 0.00000

Linear Discriminant Function for Groups

Employed Housewife Student

Constant -50.364 -38.174 -58.034

Call Rate 5.739 5.430 8.379

SMS Rate 4.512 3.503 3.590

Connectivity 7.577 6.955 6.946

VAS -0.574 -0.755 -0.921

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Special Offers 2.114 2.228 3.605

Customer Care 5.478 4.325 5.614

GPRS 3.241 2.746 3.337

Summary of Misclassified Observations

Squared

Observation True Group Pred Group Group Distance Probability

2** Housewife Employed Employed 1.684 0.576

Housewife 3.121 0.281

Student 4.455 0.144

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3** Employed Student Employed 12.234 0.106


Student 7.973 0.893

9** Student Employed Employed 7.699 0.588


Student 10.881 0.120

13** Employed Housewife Employed 3.667 0.349


Student 9.877 0.016



Student 10.939 0.027



Student 7.627 0.230



Student 6.831 0.331



Student 14.852 0.004

36** Employed Student Employed 9.133 0.051


Student 3.459 0.864

Analysis Interpretation

1. The Discrimination constant for Employed, Housewives and Students,respectively are -50.364, -38.174 , -58.034.

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2. Students are more price sensitive as the coefficient of the predictor callrate is 8.379 highest for it.

3. Employed are more sensitive for SMS rate.4. Everyone is highly sensitive for the seamless connectivity.5. Students are most sensitive to the special offers, customer care and GPRS.6. The behavior of House wife and the Employed are mostly similar, whereas

the difference between housewife and students is much more in terms of the consumer behavior, as shown in the squared distance matrix.

FACTOR ANALYSIS

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Objective

To find the underlying factors consumer seeks while choosing operators cellularservice.

A sample size of 30 respondents were asked to indicate their preference on scaleof 1 to 5( 1 being lowest and 5 being highest).

Appropriateness of factor model was tested with the help of Bartletts test of sphericity &Kaiser-Meyer-Olkin statistics. The KMO value if greater than 0.5,factor analysis is considered as appropriate technique for analyzing correlationmatrix. Here it is 0.49 which is slightly below 0.5 we take factor analysis toanalyze correlation matrix.

Referring to the results of above two tests we see that variables considered arenot highly correlated to each other.

Analysis

1) Method of Factor Analysis

Principal components analysis was chosen where total variance in the data isconsidered. Referring to the table of initial eigen values we find the first factoraccounts for 26.504 % of total variance.

2) Determining number of factors

Determination is done based on eigen values and scree plot. Retaining the

factors with eigen value greater than 1 we find the number factors to beconsidered is 3.Based on scree plot we observe the gradual trail off i.e screeoccurs after first three factors.

Hence the number of factors to be considered is 3.

3) Rotate factors

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The factor loadings are not easily interpretable we rotate the factors and getrotated factor matrix. We observe variables which have high loading in particularfactor have less loading in remaining factors. Example SMS rate have high loadin factor 2 i.e. 0.581 and low on others i.e. 0.031 in factor 1 and 0.107 in factor 3

4) Interpretation of factors

Grouping variables with high loads in each factor we find factor 1 comprisesof call rate, special offer and customer care. Factor 2 comprises of sms rates,connectivity, VAS. Factor 3 comprises of GPRS. Clubbing together we term factor1 as TALK, factor 2 as ENTERTAINMENT, factor 3 as INTERNET.

From the analysis we observe consumers seek 3 factors i.e. Factor1 (TALK)where call rates, special offers customer care are considered, Factor 2(ENTERTAINMENT) where vas services, connectivity is considered and Factor 3(INTERNET) where GPRS applications are considered

Attachment:

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output.spvdata.sav


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Chi Square Test

Ho: Type of technology used by the service provider does not have a significanteffect on the preference of the service provider among the customers.

Ha : Type of technology used by the service provider has an significant effect onthe preference of the service provider among the customers.

The value of P obtained from the chi square test is 0.0, which is less than thevalue of alpha taken to be 0.05 for any practical purpose. We can reject the nullhypothesis. This implies that the Type of technology used by the serviceprovider has a significance effect on the preference of the service provideramong the customers.

Inference:

For the data obtained from the survey we can see that the customers have a

clear inclination towards the GSM service providers.

This can be due to several reasons few of which we were able to identify were:

Non availability of good phones in CDMA technology CDMA phones are expensive compared to the GSM phones Once phones bought customers feel bound to the service provider as SIM

cannot be changed in CDMA handsets. Lack of flexibility in changing the handset

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tech_pref.MPJ


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Cluster Analysis

To segment the customers on the basis of their desired features, whichthey look for in the cellular service from various brands like Airtel, Idea, Reliance,Vodafone etc.

Cluster analysis was done to segment the customers, so as to providethem with better packages to create delight among them and retain them in longrun.

Cluster analysis is a major technique for classifying a mountain of information into manageable meaningful piles. It is a data reduction tool thatcreates subgroups that are more manageable than individual datum.

In our Research we have taken 7 variables and conducted a survey on 37

people. The respondents were asked to rank the parameters on a likert scale of 1-5 where:

1 was of least importance and 5 is highest importance feature

The parameters which were considered were:

Call rates Sms rates Connectivity VAS Special Offer Customer care GPRS

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On the data obtained from the respondents we ran a K-mean Cluster analysis.

cluster_proj.MPJ

The output of the analysis was:

Final Cluster Centres

Cluster

1 2 3 4

CALLRATE 4 3 4 3

SMSRATE 4 4 4 3

CONNECTIVITY 4 4 4 4

VAS 3 3 3 1

SPECIALOFFER 4 3 4 2

CUSTOMERCARE

3 4 4 3

GPRS 4 4 2 2

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ANOVA

Cluster Error

F Sig.Mean

Square df Mean

Square df

CALLRATE 4.589 3 .626 33 7.329 .001

SMSRATE 1.781 3 .744 33 2.394 .086

CONNECTIVITY .666 3 .667 33 .999 .405

VAS 2.124 3 .937 33 2.267 .099

SPECIALOFFER 3.597 3 .561 33 6.415 .002

CUSTOMERCARE

.675 3 .823 33 .820 .492

GPRS 18.381 3 .558 33 32.926 .000

The F tests should be used only for descriptive purposes because the clusters

have been chosen to maximize the differences among cases in differentclusters. The observed significance levels are not corrected for this and thuscannot be interpreted as tests of the hypothesis that the cluster means areequal.

From the Anova table following components have significant contribution increating clusters ( assuming alpha=0.05)

Call rates Special Offer GPRS

In this GPRS has the maximum contribution whereas Connectivity has the leastin making clusters. This shows that GPRS is the most sporadic in nature. Fewrespondents were used GPRS heavily, while others cared least about this feature,

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they were using the basic voice based service. Call connectivity which includesease of getting connected and fewer call drops was the most desired featureacross different segments.

Number of Cases ineach Cluster

Cluster 1 13.000

2 8.000

3 14.000

4 2.000

Valid 37.000

Missing .000

Analysis:

The first cluster is formed by customer no. 1,3,4,6,10,15,16,17,20,22,24,32,33

The fist cluster consists of maximum number of students who are

Highly price sensitive Want low call as well as sms rates Give high importance to tariff packages/special offers like free night

calling

Low concern about customer care Give high importance to a good GPRS connection

The S econd Cluster is formed by customer no. 2,5,7,8,9,13, 23,28

This segment majorly consists of working class or employed people, who are:

More importance to call connectivity Comparative low importance to call rate compared to first segment

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High importance to customer care Medium importance to VAS and Special offers.

The Third Cluster is formed by customer no.11,14,19,21,25,26,27,29,30,31,34,35,36,37

Its a mixed group of students, working class and house wife, who require a gooddegree of all the features except VAS to which they have given moderateimportance.

The Fourth Cluster is formed by customer no. 12,18

This is a fairly small group, which has given quite low importance to most of thefeatures except connectivity. This group also consist of a house wife who doesnot use any of the feature and the cell phone is used just to receive incomingcall, so a high importance to connectivity.

Figure 1

Service Provider wise Market Share as on 31.12.2011 (Source: TRAI)

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The most preferred connection was Idea, as most of the respondents werefrom the Symbiosis, Lavale campus. Idea Cellular offers CUG to the students and

professors in the campus which makes inter-college calls free.

Airtel is the 2 nd most preferred service provider, favourite among workingprofessionals and students in various other campuses.

Factor responsible for choosing the service provider

The graph shows that India being the price sensitive market call rates andSMS rates are the most importance feature while choosing an connection, VASstill is a minor factor in the decision making process.

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MULTIDIMENSIONAL SCALING

Multidimensional scaling (MDS) can be considered to be an alternative tofactor analysis. In general, the goal of the analysis is to detect meaningfulunderlying dimensions that allow the researcher to explain observed similaritiesor dissimilarities (distances) between the investigated objects. In factor analysis,the similarities between objects (e.g., variables) are expressed in the correlationmatrix. With MDS, you can analyse any kind of similarity or dissimilarity matrix,in addition to correlation matrices. The following simple example maydemonstrate the logic of an MDS analysis. Suppose we take a matrix of distancesbetween major US cities from a map. We then analyse this matrix, specifyingthat we want to reproduce the distances based on two dimensions. As a result of the MDS analysis, we would most likely obtain a two-dimensional representationof the locations of the cities, that is, we would basically obtain a two-dimensionalmap. In general then, MDS attempts to arrange "objects" (major cities in thisexample) in a space with a particular number of dimensions (two-dimensional inthis example) so as to reproduce the observed distances

Objective : To test brand similarity between different telecom operators brands.

File attached

MDS.sav

MDS_out.spv

Analysis

We found that Airtel, Vodafone and Idea were close to each other inthe mind of the respondents.

While BSNL, Aircel, Indicom are brands that consumers prefer least. The order of preference for consumers is Airtel>Vodafone> Idea>

Docomo> Reliance> Aircel>Indicom>BSNL. Kruskals stress ideally should be less than 0.1 and for our survey it

is 0.0523

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R.Sq should be greater than 0.8 and in our analysis it comes out tobe 0.960

Appendix

QUESTIONNAIRE FOR SURVEY

MOBILE CONNECTION SURVEY

Name : ______________________

Profession : ___________________

Gender :_________________

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1. Age?

18-22 23-28 29-32 33+

2. Which type of mobile technology do you use?

GSM CDMA

3. Which service operator are you using?

Airtel Vodafone Reliance Idea BSNL

If others please specify

4. Which type of connection do you use?

Prepaid Postpaid

5. What is the monthly expenditure of your mobile?

1000

6. Rate the importance of the features of the following in the operator, on scaleof 1-5 (1 =lowest and 5 =highest):-

1. Call Rate _______________

2. SMS Rate _______________

3. Connectivity _______________

4. VAS _______________

5. Special Offers _______________

6. Customer Care _______________

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7. GPRS _______________

7. Are you satisfied with your operator?

a) Yesb) Noc) Neither

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rohit kumbhar_11020541115_market research report on cellular service providers

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