CASE – 1   Consumer Perception of High-end IT Education

 This case study of recent origin (2001), illustrates the use of free-response questions which permit respondents to give unstructured answers. The responses are given in the form of excerpted quotes from the study at the end of the case. The entire study was bigger in scope and results. These reported results are only for the purpose of illustration and do not constitute the complete analysis.

BACKGROUND

SSI, a computer education centre, has added Internet to its portfolio. Now SSI plans to re-launch its course called Internet in its updated form. The course includes ASP, XML, WAP, .NET and BLUETOOTH, the last one being offered only by SSI’s Internet.

Research Objectives

 To find out

• the deciding factors for taking up a particular High-End I.T. course.
• whether the course contents of Internet are actually in “demand”.
• the strengths and weaknesses of Internet.

Methodology

Collecting information through

• questionnaires
• face-to-face interviews
• telephonic interviews
• internet

Sample Composition

Students of SSI as well as from competing computer education providers (NIIT, Aptech, Radiant, Tata Infotech).

Sample size: 80 (25% SSI + 75% others)

Results from Some Free Response Questions for Students’ Comments

The following are quotations from some students’ comments on the institute, course, and so on.

“Right now the I.T. market in U.S. has gone down. Bluetooth is still in a kind of an infancy stage with no real commercially proven success. There is a lot of investment in the technology. Recently it has hit a few roadblocks—you will see from the info in the links (viz http://www.bluetooth.com/ and

http://www.zdnet.co.uk/news/specials/1999/04/bluetooth/)”

• Computer professional (New Jersey, USA)

“MS (Micro Soft) has come up with the .NET, which works on the Windows 2000 platform. Anything to do with Internet will be ‘hot’. And MS won’t leave it halfway”.

• Faculty (Radiant)

“I did my GNIIT, now I am doing Java at RADIANT. Did not continue there because I wanted to do only Java; and NIIT, though it is very good, has only long-term courses. Want to get into an I.T. career. From what I have heard, Aptech is not up to the mark. Don’t know much about SSI or Internet. .NET is the latest course here.”

• Student (Radiant)

“I am doing Radiant.NET with C#, ASP.NET, XML, SOAP, and so forth because it is the latest after Java”.

• Student (Radiant)

“I joined Radiant because I heard that the course material is very good. Faculty is also good. Finished my Java from there. And I plan to do a post graduate in I.T. NIIT is too expensive. Cost-wise, I guess SSI and Radiant are comparable. Don’t know more about SSI.”

• Student (Radiant)

“I did my Java from TCI because I stay close by (Annanagar). Radiant is more expensive. Also TCI gives me a ‘Government of India’ certificate. I am working as a web page designer. I am being trained in XML and so on by my company itself.”

• Ex-Student (TCI)

“.NET has not yet come into the market. hence we do not have the course. We have C#, XML, WAP.”

• Counselor (NIIT)

 

“Of course NIIT is expensive compared to the other institutes. But when one is focussed on one’s career, one does not crib about money. After interacting with my faculty, I have a very good knowledge about the I.T. world. Now I would not even think of changing. I have a background in BCA and am doing my Java here.”

• Student (NIIT)

“NIIT has got a name that is recognised the world over more than any other institute in India. Hence I prefer to be in NIIT. I plan to work abroad. I am currently doing E-Commerce course in NIIT, which includes XML, ASP, WAP and so forth.”

• Student (NIIT)

“I just know about NIIT. So I am here. Plan to do a short-term course here itself after my GNIIT, which I will finish this year.”

• Student (NIIT)

“I have no background in computers, but I do not find any difficulty in doing my Internet course. NIIT and APTECH are too expensive.”

• Student (SSI)

Question:

1. Write don a brief summary of all the answers given above. How does this differ from the analysis of structured-response questions?

 

CASE – 2   Chi-square Test

Methodology

1. A fictitious data set consisting of thirty respondents was created. The data was mainly constructed to find the relationship between the dependent and independent variable. Age was taken as the independent variable and choice of a drink as dependent variable. Six brands of soft drinks were considered as the different choices for the respondents.

2. The age group coded into six categories as 1 to 6 and the brands of soft drinks were coded into six categories and the codings are as follows:

(a)  Independent variable

          Age                         Coding
<15                                1
16 – 25                          2
26 – 35                          3
36 – 45                          4
46 – 55                          5
>55                                6
(b)  Dependent variable

       Different brands          Coding
Coke                             1
Pepsi                            2
Mirinda                       3
Sprite                           4
Slice                              5
Fruit Juice                   6

 

1. Chi-square test has been used to cross-tabulate and to understand the relationship between the independent and the dependent variable.

2. Calculation of contingency coefficient and the lambda asymmetric coefficient is done to find the strength of the association between the two variables.

3. Sample size is taken as thirty.

4. Analysis of cross-tabulation.

5. SPSS software package for the cross tabulation analysis.

Problem

This is a bivariate problem. The basic intention of the problem is to understand the relationship between AGE and BRAND PREFERENCE of different brands of soft drinks.

Input Data Table

Serial No.	Age	AGECODE	SOFT DRINK	DRINK CODE
1	<15	1	FRUIT JUICE	6
2	<15	1	SPRITE	4
3	<15	1	MIRINDA	3
4	<15	1	PEPSI	2
5	<15	1	FRUIT JUICE	6
6	16-25	2	COKE	1
7	16-25	2	SLICE	5
8	16-25	2	COKE	1
9	16-25	2	PEPSI	2
10	16-25	2	MIRINDA	3
11	26-35	3	SLICE	5
12	26-35	3	SPRITE	4
13	26-35	3	FRUIT JUICE	6
14	26-35	3	PEPSI	2
15	26-35	3	SLICE	5
16	36-45	4	MIRINDA	3
17	36-45	4	FRUIT JUICE	6
18	36-45	4	FRUIT JUICE	6
19	36-45	4	SLICE	5
20	36-45	4	PEPSI	2
21	46-55	5	COKE	1
22	46-55	5	SPRITE	4
23	46-55	5	SLICE	5
24	46-55	5	FRUIT JUICE	6
25	46-55	5	SLICE	5
26	>55	6	MIRINDA	3
27	>55	6	COKE	1
28	>55	6	COKE	1
29	>55	6	PEPSI	2
30	>55	6	FRUIT JUICE	6

Output Data

Age by Drink Preference

Age

Drink Preference	Code	<15	16-25	26-35	36-45	46-55	>55	Total
Coke	1	0	2 33.32%	0	0	1 20%	1 40%	5 16.67%
Pepsi	2	1 20%	1 16.67%	1 25%	1 20%	0	1 20%	5 16.67%
Mirinda	3	1 20%	1 16.67%	0	1 20%	0	1 20%	4 13.33%
Sprite	4	1 20%	0	1 25%	0	1 20%	0	3 30%
Slice	5	0	1 16.67%	2 50%	1 20%	2 40%	0	6 40%
Fruit Juice	6	2 40%	1 16.67%	0	2 40%	1 20%	1 20%	7 23.33%
Total		5 100%	6 100%	4 100%	5 100%	5 100%	5 100%	30 100%

 

Chi-Square	Value	DF	Significance
Pearson	18.22857	25	.08325
Likelihood Ratio	25.52646	25	.04332
Mantel-Haenszel test for linear association	.13961	1	.07086

Minimum Expected Frequency ─.500

Cells with Expected Frequency <5─36 of 36 (100.0%)

Approximate Statistics	Value	ASE 1	VAL/ASE 0	Significance
Contigency Coefficient	.61479			.08325*1
Lambda:
Symmetric	.18750	.08892	1.99754
With ‘DRINK CODE’ dependent	.21739	.12757	1.56813
With ‘AGE CODE’ dependent	.16000	.07332	2.14834
Goodman & Kruskal Tau:
With ‘DRINK CODE’ dependent	.12432	.03912		.08412*2
With ‘AGE CODE’ dependent	.12152	.02580		.08580*2

*1  Pearson Chi-square probability

*2  Based on Chi-square approximation

Number of Missing Observations: 0

Analysis

In a Chi-square test, for a 90 per cent confidence level, if the significance level is greater than or equal to 0.1, it signifies that there is no association between the two variables in the cross-tabulation and if significance level is less than 0.1, then it signifies that there is a significance relationship between the selected variables.

The result of the cross-tabulation

From the output tables, the Chi-square test read a significance level of 0.08325 at 90 percent confidence level. For 90 per cent, significance level is 0.1, that is (1─0.9), so the above result shows that at 0.08 (which is less than 0.1), there is a significant relationship between the two variables. At 95 per cent confidence level, significance level being 0.05, and the above output giving a significance level of 0.08 which is greater than 0.05, there is no relationship between the variables:

If contingency coefficient value is greater than +0.5 then the variables are strongly associated. In the above case the contingency coefficient value being 0.6 which is greater than 0.5, hence the variables are strongly associated.

The asymmetric lambda value (with DRINKCODE dependent) 0.21739 means that 21.7% of error is reduced in predicting brand preference when age is known.

From the above result we can conclude that there is a significant relationship between AGE (independent variable) and BRAND PREFERENCE (dependent variable), of the respondents.

Thus we can conclude that the age of the respondent plays an important role in the purchasing intention of a particular brand of soft drink.

Question

Case 2:  Conduct Chi-square test to cross-tabulate and to understand the relationship between the independent and the dependent variable. Also calculate contingency coefficient and the lambda asymmetric coefficient to find the strength of the association between the two variables. Take Sample size as thirty. Analysis of cross-tabulation using  SPSS software package would be required.

CASE – 3   Tamarind Menswear 

Given below is a preliminary questionnaire for retailers and consumers of a recently launched menswear brand. Can you list down the research objectives for both questionnaire? Can you modify the given questionnaires to a final draft?

TAMARIND QUESTIONNAIRE FOR RETAILERS

1. Do you have Tamarind? Yes/No
2. What do you think about it?
3. Is there place in the market for one more readymade garment company?
4. What kind of products does Tamarind have? Are they good?
5. Is it a threat to any existing brand? If yes, which one?
6. If it is not a available, what is your view about advertising so heavily before the product is launched?
7. Are people coming and asking for Tamarind?
8. The range of clothes with the retailer.
9. Price range.
10. Name of the shop and so on.

TAMARIND QUESTIONNAIRE FOR CONSUMERS

1. Which ads do you recall?
2. Which garment ads do you recall?
3. Have you seen the Tamarind ad?
4. What do you remember from the ads?
5. Do you like the ad? Why?
6. What is the main message?
7. What kind of clothes are Tamarind?
8. What do you think will be the price range?
9. Will you buy it? Why?

 

CASE – 4    Logistics Regression

 A pharmaceutical firm that developed particular drug for women wants to understand the characteristics that cause some of them to have an adverse reaction to a particular drug. They collect data on 15 women who had such a reaction and 15 who did not. The variables measured are:

1. Systolic Blood Pressure
2. Cholesterol Level
3. Age of the person
4. Whether or not the woman was pregnant (1 = yes)

The dependent variable indicates if there was an adverse reaction (1 = yes)

TABLE 1

BP	Cholesterol	Age	Pregnant	DrugReaction
100	150	20	0	0
120	160	16	0	0
110	150	18	0	0
100	175	25	0	0
95	250	36	0	0
110	200	56	0	0
120	180	59	0	0
150	175	45	0	0
160	185	40	0	0
125	195	20	1	0
135	190	18	1	0
165	200	25	1	0
145	175	30	1	0
120	180	28	1	0
100	180	21	1	0
100	160	19	1	1
95	250	18	1	1
120	200	30	1	1
125	240	29	1	1
130	172	30	1	1
120	130	35	1	1
120	140	38	1	1
125	160	32	1	1
115	185	40	1	1
150	195	65	0	1
130	175	72	0	1
170	200	56	0	1
145	210	58	0	1
180	200	81	0	1
140	190	73	0	1

SPSS Output

TABLE 2 Model Summary

Step	-2Log likelihood	Cox & Snell R Square	Nogelkerke R Square
1	21.84 (a)	.482	.643

Estimation terminated at iteration number 7 because parameter estimates changed by less than .001.

TABLE 3 Hosmer and Lemeshow Test

Step	Chi-Square	df	Sig
1	4.412	8	.818

The lack of significance of the Chi-Squared test indicates that the model is a good fit

TABLE 4  Classification Table

Observed		Predicted
		DrugReaction	Percentage Correct
		0                      1
Step 1          DrugReaction       Overall Percentage	0   1	11                     4   2                    13	73.3   86.7   80.0

The cut value is .500.

The classification table shows that the model makes a correct prediction 80% of the time overall. Of the 15 women with no reaction, the model correctly identified 11 of them as not likely to have one. Similarly, of the 15 who did have a reaction, the model correctly identifies 13 as likely to have one.

TABLE 5  Variables in the Equation

	B	S.E.	Wald	df	Sig	Exp (B)
Step 1 (a) BP	-.018	.27	.463	1	.496	.982
Cholesterol	.027	.025	1.182	1	.277	1.027
Age	.265	.114	5.404	1	.20	1.304
Pregnant	8.501	3.884	4.790	1	0.29	4918.147
Constant	-17.874	10.158	3.096	1	0.78	.000

Variable(s) entered on Step 1: BP, Cholesterol, Age, Pregnant.

Since BP and Cholesterol show up as not significant, one can try to run the regression again without those variables to see how it impacts the prediction accuracy. Since the sample size is low, one cannot assume that they are insignificant. Wald’s test is best suited to large sample sizes.

The prediction equation is:

Log (odds of a reaction to drug) = ─17.874─0.018(BP) + (Cholesterol) + 0.265 (Age) + 8.501 (Pregnant)

As with any regression, the positive coefficients indicate a positive relationship with the dependent variable.

TABLE 6   Predicted Probabilities and Classification

BP	Cholesterol	Age	Pregnant	Drug Reaction	Pred_Prob	Pred_Class
100	150	20	0	0	.00003	0
120	160	16	0	0	.00001	0
110	150	18	0	0	.00002	0
100	175	25	0	0	.00023	0
95	250	36	0	0	.03352	0
110	200	56	0	0	.58319	1
120	180	59	0	0	.60219	1
150	175	45	0	0	.01829	0
160	185	40	0	0	.00535	0
125	195	20	1	0	.24475	0
135	190	18	1	0	.12197	0
165	200	25	1	0	.40238	0
145	175	30	1	0	.65193	1
120	180	28	1	0	.66520	1
100	180	21	1	0	.30860	0
100	160	19	1	1	.13323	0
95	250	18	1	1	.58936	1
120	200	30	1	1	.85228	1
125	240	29	1	1	.92175
130	172	30	1	1	.69443	1
120	130	35	1	1	.76972	1
120	140	38	1	1	.90642	1
125	160	32	1	1	.75435	1
115	185	40	1	1	.98365	1
150	195	65	0	1	.86545	1
130	175	72	0	1	.97205	1
170	200	56	0	1	.31892	0
145	210	58	0	1	.62148	1
180	200	81	0	1	.99665	1
140	190	73	0	1	.98260	1

The table above shows the predicted probabilities of an adverse reaction, and the classification of each into group 0 or 1 on the basis of that probability, using 0.5 as the cut-off score.

Question:

 Case 4: Using logistic regression proof that particular drug for women has characteristics that cause some of them an adverse reaction to a particular drug.

 

CASE – 5   Conjoint Analysis

Problem

XYZ paint company identified the attributes which are important to their customers and also classified each of the attributes into their levels. Based on this, they want to use the technique of conjoint analysis to determine from a potential customer’s point of view, how important each attribute is to him. They also want to know how much utility the customer derives from a given combination of these levels of attributes. It also helps to understand the feasible offerings from the marketer’s point of view. The three important attributes identified for the paint are:

1. Life—this is the number of years the paint coat lasts.
2. Price—the price of one litre of paint.
3. Colour—the colour of paint.

The levels of the above mentioned attributes are as follows:

• Life—3 years, 4 years, 5 years
• Price—Rs. 50 per litre, Rs. 60 per litre, Rs. 70 per litre
• Colour—Green, Blue, Cream

Input data

After the attributes and their levels are decided, the next stage is to collect from the respondent, the ranking of all 27 combinations of levels. This can be seen from Table 1.1.

TABLE 1.1  Input Data for Conjoint Analysis

S.No.	Life (in years)	Price (Rs/Litre)	Colour	Rating (27 to 10
1	5	50	Green	27
2	4	50	Green	26
3	5	50	Cream	25
4	5	50	Blue	24
5	5	60	Green	23
6	4	60	Green	22
7	5	70	Green	21
8	5	60	Blue	20
9	5	60	Cream	19
10	4	50	Blue	18
11	4	50	Cream	17
12	5	70	Blue	16
13	3	50	Green	15
14	5	70	Cream	14
15	3	50	Blue	13
16	4	60	Blue	12
17	4	60	Cream	11
18	3	50	Cream	10
19	4	70	Green	9
20	3	60	Green	8
21	4	70	Blue	7
22	3	60	Blue	6
23	4	70	Cream	5
24	3	60	Cream	4
25	3	70	Green	3
26	3	70	Blue	2
27	3	70	Cream	1

Table 1.2  Shows different codes assumed for various levels of attributes for a regression run. The coding of the attribute levels for this purpose is known as ‘effects coding’. In this table, which is similar to the coding of dummy variables, the three levels of life are coded as follows:

Life in years	Var 1	Var 2
3	1	0
4	0	1
5	─1	─1

Thus, the two variables, Var 1 and Var 2 are used to indicate the 3 levels of life, as per the coding scheme mentioned above.

Similarly the coding scheme for the three levels of the price is as shown as follows:

Price (Rs. Per liter)	Var 3	Var 4
50	1	0
60	0	1
70	─1	─1

Finally, the coding scheme for colour is as shown below:

Colour	Var 3	Var 4
Green	1	0
Blue	0	1
Cream	─1	─1

Thus, 6 variables, that is Var 1 ─ Var 6 are used to represent the 3 levels of life of the paint (3, 4, 5), 3 levels of price per litre (50, 60 & 70) and 3 levels of colour (green, blue and cream). All the six variables are independent variables in the regression run. Var 7 is the rating of each combination given by the respondent, and forms the dependent variable for the regression curve. The recoded input data are shown in Table 1.3.

If the conjoint analysis is run as a regression model, the rating (which is the reverse of ranking) is used as a dependent variable. All combinations from the first to the twenty-seventh are ranked by the respondent. Rank 1 can be considered as the highest rating and given a rating of 27. Rank 2 can be given a rating of 26 and so on. This is not an interval-scaled rating, and should have only ordinal interpretation.

Table 1.3   Conjoint Problem Input Data Coded for Regression

Var 1	Var 2	Var 3	Var 4	Var 5	Var 6	Var 7
─1.00	─1.00	1.00	0.00	1.00	0.00	27.00
0.00	1.00	1.00	0.00	1.00	0.00	26.00
─1.00	─1.00	1.00	0.00	─1.00	─1.00	25.00
─1.00	─1.00	1.00	0.00	0.00	1.00	24.00
─1.00	─1.00	0.00	1.00	1.00	0.00	23.00
0.00	1.00	0.00	1.00	1.00	0.00	22.00
─1.00	─1.00	─1.00	─1.00	1.00	0.00	21.00
─1.00	─1.00	0.00	1.00	0.00	1.00	20.00
─1.00	─1.00	0.00	1.00	─1.00	─1.00	19.00
0.00	1.00	1.00	0.00	0.00	1.00	18.00
0.00	1.00	1.00	0.00	─1.00	─1.00	17.00
─1.00	─1.00	─1.00	─1.00	0.00	1.00	16.00
1.00	0.00	1.00	0.00	1.00	0.00	15.00
─1.00	─1.00	─1.00	─1.00	─1.00	─1.00	14.00
1.00	0.00	1.00	0.00	0.00	1.00	13.00
0.00	1.00	0.00	1.00	0.00	1.00	12.00
0.00	1.00	0.00	1.00	─1.00	─1.00	11.00
1.00	0.00	1.00	0.00	─1.00	─1.00	10.00
0.00	1.00	─1.00	─1.00	1.00	0.00	9.00
1.00	0.00	0.00	1.00	1.00	0.00	8.00
0.00	1.00	─1.00	─1.00	0.00	1.00	7.00
1.00	0.00	0.00	1.00	0.00	1.00	6.00
0.00	1.00	─1.00	─1.00	─1.00	─1.00	5.00
1.00	0.00	0.00	1.00	─1.00	─1.00	4.00
1.00	0.00	─1.00	─1.00	1.00	0.00	3.00
1.00	0.00	─1.00	─1.00	0.00	1.00	2.00
1.00	0.00	─1.00	─1.00	─1.00	─1.00	1.00

OUTPUT AND ITS INTERPRETATION

The output of the regression model is shown in Table 1.4. Variables 1 to 6 are treated as independent variables. The column titled ‘B’ (the regression coefficient column) provides the part utility of each level of attributes.

Table 1.4   Multiple regression output for conjoint problem (partial output shown)

Variables in the regression equation
VARIABLE	B
Var 1	─7.00
Var 2	0.11
Var 3	5.44
Var 4	─0.11
Var 5	3.11
Var 6	─0.88

 

 

For example, the life of 3 years is represented by variable 1 as per our coding scheme. Its utility is equal to ─7.11 (looking under column ‘B’ of Table 1.4 for variable 1). Similarly the utility for variable 2, representing life of 4 years is 0.11. The utility for the 3rd level of life, is not in the table, but is derived from the property of this coding, that all the utilities for a given attributes should sum to 0. Thus, utility for life of 5 years should be equal to 7 (─7.11 + 0.11).

Similarly for price, the utilities of Rs. 50/litre and Rs. 70/litre are given by the numbers 5.44 and ─0.11, as shown against 3 and 4 in Table 1.4 in Table 1.4 but the utility for Rs. 80/litre is derived from the same property, that the sum of the utilities for different levels of price should sum to 0. Therefore the price Rs. 80/litre has the utility of 5.33 (5.44 + (─0.11).

Finally for colour, green has the utility of 3.11 and blue has the utility of ─0.88. Cream has a derived utility of 2.23 (3.11 + (─0.88).

TABLE 1.5    Utilities Table for Conjoint Analysis

Attributes	Levels	Part Utility	Range of Utility (Max ─ Min)
Life	3 years	─7.11	= 7.00 ─ (─7.11)
	4 years	0.11	= 14.11
	5 years	7.00
Price	Rs. 50/litre	5.44
	Rs. 60/litre	─0.11	= 5.44 ─ (─0.11)
	Rs. 70/litre	5.33	= 5.55
Colour	Green	3.11	= 3.11 ─ (─0.88)
	Blue	─0.88	= 3.99
	Cream	2.23

From the Table 1.5 we can conclude that the life or the number of years the paint lasts is the most important attribute for the customer. There are two indicators for this.

1. The range of utility value is highest (14.11) for the life. (From Range of Utility column)
2. The highest individual value of this attributes is at its 3rd level that is, i.e., 7.00.

Both these figures indicate that the number of years the paint lasts is the most important attribute at given levels of attributes. The price/litre seems to be the second most important attribute, as its range of utilities is 5.55. The last attribute in relative importance is the colour, with the utility range of 3.99.

Combination Utilities

The total utility of any combination can be calculated by picking the attribute levels of our choice. For example, the combined utility of the combination 4 years of life, Rs. 70/litre, and cream colour is 0.11 + 5.33 + 2.33 = 7.67. If we want to know the best combination, it is advisable to pick the highest utilities from each attribute, and add them. The possible combination is 5 years of life, Rs. 50/litre, and green colour, that is, 7.00 + 5.44 + 3.11 = 15.55. The next best combination is 5 years of life, Rs. 70/litre, and green colour, with the combined utility of 7 + 5.33 + 3.11 = 15.44.

Individual Attributes

The difference in utility with the change of one level in one attribute can also be checked. For the life of 3 years to 4 years, there is increase in utility value of 7.22 units, but the next level, that is, 4 years to 5 years has an increase in utility of 6.89.

Similarly, increase in price from Rs. 50/litre to Rs. 60/litre induces a utility drop of 5.55, whereas from Rs. 60/litre to Rs. 70/litre there is an increase in utility of 5.44.

Finally, colour green to colour blue induces 3.99 drop in utility. Next, from colour blue to colour cream there is an increase in utility of 3.11.

Question:

Case 5: Use conjoint analysis to determine from a potential customer’s point of view, how important each attribute is to him. Also determine how much utility the customer derives from a given combination of these levels of attributes. The attributes are life, price and colour.

 

CASE 6

A recent case study for a cellular phone service provider in Chennai listed its research objectives and methodology (including sampling plan) for a marketing research study as follows:

SKCELL, A CELLULAR OPERATOR/STUDY ON VALUE ADDED SERVICES LIKE SMS (SHORT MESSAGING SERVICE), VOICE MAIL, AND SO ON

Research Objectives

To find out

• whether people actually use the mobile phone just for talking
• to what extent the mobile phone is used for its VAS (Value Added Services)
• factors influencing choice of service provider
• awareness of Skycell’s improved coverage

Locations Covered

Chennai city and the suburbs

Methodology

Primary data:

Through questionnaires

Sample Composition

• Mobile phone users
• Business pesons
• Executives
• Youth

Sample size: 75

Age group: 18 – 45 years

Questions:

1. Can you add to methodology section?

2. Distribute the sample of 75 among the different categories of respondents mentioned under “Sample Composition”.