Marketing Research June 2018 Assignment

01 Apr

Marketing Research

Q1. What is Sampling?

Which kind of sample is being used in each of the following situations:

i. A reporter asks 100 people on the street what do they think of the current state of crime against women

ii. A survey is conducted to understand opinions of people in Gujarat towards the Modi govt. A sample of 100 men and 100 women across all age groups is chosen uniformly

iii. A survey is conducted among neuro surgeons. Starting with the first neuro surgeon, he is asked to refer to another neuro surgeon and so on.

Support with a rationale.

Q2. What is a pilot test? What are the considerations that need to be kept in mind by a market researcher while designing a questionnaire?

Q3. You are the Product Manager of a leading telecom company which is facing stiff competition due to the entry of Reliance Jio.

a. State the management problem(s) that your company might be facing and the corresponding research objectives that your research will aim to cover.

b. Briefly describe the research methods that you would use towards this research.

Marketing Research

02 Sep

 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”.

 

Market Research

02 Jul

CASE-1                                                                                                                     

Interviewing in M.R

Welcomgroup Hotels

The Welcomgroup owns a chain of 20 hotels located in different parts of the country. In recent years, it has been expanding the chain by setting up new hotels.

When there were only a few hotels, the Managing Director of the Welcomgroup used to personally visit them with a view to ensuring that they provided high quality of food and service to their patrons. But now he finds that with so many hotels it is extremely difficult to personally visit each and every hotel. At the same time, he needs some mechanism to ensure that hotels of the chain continue to provide high quality service.

The Managing Director has discussed this problem with some Senior Officials of the company. As a result of this discussion, he finds that:

Option 1: A suitable questionnaire may be designed and the same may be given to guests during their stay in the hotels. They may be requested to return the filled-in questionnaire at the reception counter while leaving the hotels.

Option 2: A suitable questionnaire may be designed and the same may be posted to their homes soon after they have reached there with the request that these be returned, duly filled in, by post.

Option 3: A trained interviewer may be appointed on a temporary basis. He could visit different hotels without giving any prior intimation of his visits. In each hotel he visits, he may personally interview selected guests and seek their opinion on the quality of food arid service in that hotel and their suggestions, if any, for improvement.

Option 4: The services of a marketing research firm may be hired. It may be asked to conduct a suitable study based on, say, telephone interviews of a random sample of guests from each hotel and to submit its report to the Managing Director.

Questions

Discuss the relative merits of these options, indicating which one you would adopt if you were the Managing Director?

 

CASE- 2                                                                                                                    

Attitude Towards Advertising

You have been asked to ascertain the attitude of people towards advertising, whether favourable or unfavourable, in the medium-sized city where you live. The study should indicate whether heterogeneous groups differ significantly or otherwise in their attitudes towards advertising.

The proposed study has to be carried out in two parts. Part I will involve the construction of a suitable scale for measuring attitudes of people. Part II will examine some hypotheses and conclude whether they are accepted or rejected. The hypotheses will concern the differences in attitude of the two groups towards advertising. For this purpose, you may think of groups in terms of male and female, young and old, educated and uneducated, rich and poor.

Questions

1. What type of study is this?

2. How would you develop a suitable scale for the proposed study?

3. Which scale would be most appropriate and why?

4. What would be the limitations of such a study?

 

CASE- 3                                                                                                                    

Consumer Medical Attitudes

A study was undertaken sometime back to assess attitudes of consumers concerning health care. The study was conducted using a structured questionnaire which was administered to 1000 persons living in Baroda.

Some of the questions contained in the questionnaire are given below (the original numbers of these questions were different).

Questions

1. What type of attitude scale is used in each of these questions?

2. Would you like to change any of these attitude scales? If so, indicate the change/s that you would like to make?

Exhibit

Selected questions which were contained in a questionnaire on C.M.A.

I. Given below are some characteristics that people look for in a doctor of their choice. You should rate each characteristic on a scale of 1 to 5 in order of importance. The characteristic most important to you, should be rated 1, and the least important should be rated 5.

  1. The doctor’s clinic is close to your residence.                                         1        2          3          4          5
  2. The doctor has long experience.                                                                1        2          3          4          5
  3. The doctor’s fee is moderate.                                                                     1         2          3          4          5
  4. The doctor is known for his diagnosis.                                                     1        2          3          4          5
  5. The doctor is recommended by your friends.                                         1        2          3          4          5
  6. The doctor is cordial and sympathetic to every patient.                       1        2          3          4          5
  7. The doctor is available for consultation without much waiting.         1        2          3          4          5

II Would you say that the quality of health care given by your doctor is:

  1. Excellent
  2. Good
  3. Satisfactory
  4. Poor
  5. Very poor

III What do you feel about the charges you pay to your doctor? Are they:

  1. Excessive
  2. Somewhat high
  3. Reasonable
  4. Low

IV Some statements are given below. Please indicate whether you agree with these statements. If you strongly agree with a statement, rate it 1; if you agree, rate it 2; if you are neutral, rate it 3; if you somewhat disagree rate it 4; and if you strongly disagree, rate it 5.

  1. 1 have full confidence in my doctor.                                                                           1       2         3          4       5
  2. My doctor explains my medical problems to me.                                                    1       2         3          4       5
  3. Generally doctors are overpaid.                                                                                  1        2         3          4      5
  4. I generally have a thorough physical check-up once a year.                                 1        2         3          4      5
  5. I often watch TV programmes which discuss health problems.                           1        2         3         4       5
  6. Most doctors are responsible persons.                                                                       1        2         3         4       5
  7. Doctors should not go on strike for any reason.                                                       1        2         3         4       5
  8. If a patient cannot recover from illness, he should be allowed to die.                 1        2         3         4       5
  9. I am very particular about my food.                                                                            1        2         3         4       5
  10. In case I have a terminal illness, I would not like my doctor to tell me.              1        2         3         4       5

 

CASE-4                                                                                                                     

(A Case on Advertising Research)

Future Demand for Formulations

Formulation manufacturing is an important segment of the pharmaceutical industry. In fact, bulk drugs are pure chemical compounds. Normally, they are not used in their crude form. What a patient requires is sometimes a mixture of various bulk drugs, or a mixture of a bulk drug with certain additives which work as excipients, diluents, stabilizers or preservatives, or for the purposes of adding taste and flavour. Any mixtures or additives used in bulk drugs to increase their utility are known as formulation.

As far as the consumer is concerned, formulation is the final product. With the increasing emphasis on health standards, the importance of formulation is increasing.

Formulations are available in various forms, which can be classified in three groups:

1. Powders

2. Tablets and pills

3. Liquids

Most of the drugs are available in all the three forms. However, some are used only in liquid or tablet form like insulin. Some formulations which are used only for external applications are available in liquid form like benzene and spirit. The selection of form depends upon the severity of the disease it intends to cure, the age of the patient, taste and odour and many such factors.

Future Demand

Formulations are directly used by patients. Demand for formulations depends upon various factors like the standard of health, occurrence of disease, population, income, awareness of people about their health and government health programme. These data are not easily available. However, two variables have been chosen for estimating future demand.

The level of public health, government health programmes and medical facilities are, by and large, reflected in the data on expenditure on health programmes by government whereas factors like population and income may be reflected in data on current consumption. So data on public expenditure on health and per capita consumption of formulations are used to estimate demand.

It is very difficult to separate demand for each form of formulations like tablets, capsules and pills because data on production, export and import in terms of such forms are not available—nor is their market mutually exclusive. In fact, to a large extent, it is mutually inclusive. So the demand for all formulations is estimated together on the basis of the data given in Table A.

Consumption of formulation is considered to be a dependent variable “Y” which is dependent on two independent variables, ‘X1’, the government expenditure on health and ‘X2’, the index number of per capita consumption of formulation. The multiple regression line is estimated as under:

TABLE A

Index Number of Per Capita Consumption of Formulation, Consumption of Formulation and Health Facility Provided by Government

Year Index No. of Per

Capita Consumption

of Formulation(X2)

Index No. of

Consumption

of Formulation

(Y)

Index No. of Health Facility

Provided

by Govt(X1)

1970–71 100.0 100.0 100.0
1971–72 111.8 114.3 105.0
1972–73 120.9 126.4 109.8
1973–74 146.8 157.0 126.0
1974–75 149.1 163.2 133.6
1975–76 146.5 166.2 136.4
1976–77 157.2 180.0 141.6
1977–78 187.3 219.3 152.3
1978–79 196.7 235.4 160.0
1979–80 229.1 280.4 166.8
1980–81 265.6 332.4 172.5
1981–82 301.37 385.7 180.0
1982–83 335.87 439.5 188.9
1983–84 341.22 456.5 193.5
1984–85* 371.86 669.5 203.5
1985–86* 417.57 741.2 214.2
1986–87* 449.00 813.0 225.3
1987–88* 489.31 905.9 237.0
1988–89* 519.08 999.8 249.4
1989–90* 581.37 1114.77 262.4
1990–91* 633.58 1256.58 275.52
1991–92* 690.68 1375.74 289.29


Note:* 1. Estimated

  1. Index Number of health facility provided by Government will increase by 5% upto 1991—92. 3. Index Number of per capita consumption of formulation is estimated to increase at 9% p.a. for 1984—85 to 1991—92.

Y = a + B1X1 + B2X2

where, Y = Index number of consumption of formulation

X1 = Index number of health facility provided by Government

X2 = Index number of per capita consumption of formulation

Solving the above equation, the following values are found, namely,

a = -139.02, B1 = 0.62 and B2 = 1.80

Thus, the demand equation would be

Y = -139.02 + 0.62X1 + 1.80X2

The future value of X1 and X2 is projected with the help of their past trend rate of growth which is found to be 5 per cent and 9 per cent respectively during 1970—71 to 1983—84.

Accordingly, the estimated subsequent value of X1 and X2 is substituted in the above equation and the future demand for formulation is obtained. This is given in Table B.

TABLE B

Future Demand for Formulation

Year Indigenous

Demand

Export

Demand

Total Demand
1989–90 4055 203 4258
1990–91 4440 222 4662
1991–92 4858 243 5101

Besides this indigenous demand, export demand exists. At present, 3.5 per cent of production is being exported. If adequate efforts are made, this may easily increase to 5 per cent in the coming years. On this assumption, the projections of export demand have been made.

Questions

1) Critically examine the method used in this case for making projections of demand for formulations. What additional data would you need for making demand projections more realistic?

 

CASE- 5                                                                                                                    

Ethical Issues in Marketing Research

Below are given some of the situations/cases wherein ethical issues are involved. Identify the ethical issue in each situation/case and indicate what you would do.

1. A marketing research firm assured its sample respondents that it would conduct an anonymous survey. As such their identity would remain undisclosed. However, it used an ultraviolet ink on each questionnaire as a result of which every respondent could be identified though the identity mark was invisible. Later on when it came to the notice of someone, the firm justified the use of an ultraviolet ink on the ground that it wanted to identify defaulting respondents so that it could send them reminders. Some people sharply reacted to this and said that such a practice was a clear deception as the firm acted against its explicit assurance to respondents.

2. A marketing research firm has recently taken up a study of some department stores. It has asked one of its members to visit these stores and pose as a customer and observe the buying behaviour of customers. No one knows that he or she is being observed by some one.

3. A large manufacturing company dealing in some cosmetic products has its own marketing research cell. However, it generally uses marketing research as a sales ploy. Its investigators try to push up the sale of its products to households when they visit them while conducting a field survey’.

4. A marketing research firm does not bother about ethical problems. It uses such data and research techniques as would produce the findings acceptable to its clients or to justify a particular decision.

5. A study is undertaken by a team of marketing researchers. A survey is planned but it has been decided that the purpose of research, as also its sponsorship, should not be disclosed to the respondents. This is because the team of marketing researchers feels that such a disclosure would influence the respondents to such an extent that the proposed research would be of no use.

6. An interviewer has been assigned the job of contacting some specific households chosen in the sample. He visits some households and in respect of others, fills in fictitious data, which are passed off as genuine. Nobody except the interviewer knows that some data are fictitious.

7. A marketing research firm accepts an assignment from on of its clients even though it knows that it would not be possible to submit the report within the stipulated time.

8. A business firm is interested in sponsoring a study with a research firm. It invites research proposals from four different consultants. It then incorporates the ideas included in different proposals in one single proposal submitted by the lowest bidder and assigns him this research study.

9. A project director seeks the permission of the Marketing Research Director to use ultraviolet ink in pre-coding questionnaires in a mail survey. He points out that although the letter refers to an anonymous survey where the identity of the respondent would be undisclosed, he needs the identification of the respondent so that cross tabulation of data can be undertaken. The Marketing Research Director gives him the permission to use ultraviolet ink.

10. An interviewer adopts an unusual practice with a view to getting the cooperation of the respondent. He assures the respondent that as soon as the survey report is ready, he will send him a copy of the same. While making such a promise to the respondent, the interviewer has no intention whatsoever to follow it up.

11. A research firm sometimes uses such devices as giving huge statistical data in appendices and drafting a report full of technical jargon to give an impression that it is a perfect study or that the team engaged in the research is very competent.

 

CASE- 6                                                                                                        

(A case in scientific Method and Research design in H.R)

Management and Marketing Research

Given below is a part of the dialogue between the General Manager and the Marketing Research Manager of a large industrial enterprise.

G.M. I think we have been unnecessarily attaching too much importance to marketing research. I don’t think marketing research can solve all our problems. There is after all a limit to its usefulness and this fact is often not recognised.

M.R.M. Nobody says that marketing research can solve all our problems. However, it can reduce uncertainty and enable us to get useful information on the basis of which better decisions can be made. It is this role of marketing research which people overlook.

G.M. While I concede your point that marketing research can help reduce uncertainty, I still feel that its importance these days is blown out of proportion. Apart from this, I do not like the use of extremely advanced statistical techniques even when there seems to be no justification for their use.

M.R.M. Well, I don’t think that such techniques are always used. In fact, many researchers may not be proficient in such techniques though they can certainly hire experts to do this job. At the same time, I must say that there is a general trend in favour of sophisticated techniques perhaps on account of increasing realisation that marketing problems are rather multi-dimensional and simple statistical tools are hardly useful in such cases.

G.M. I personally feel that there is not enough interaction between the doers of research and users of research. It is because of this ‘distance’ that each side sees the other with a certain degree of suspicion. Don’t you think that several of our problems can be solved more effectively if both the research people and management have a better understanding of each other’s role? But how can it be achieved?

M.R.M. Yes. I agree with you. This is what is required. This is possible if communications between management and research personnel are more frequent. Further, both the parties should shirk off their prejudices and have an open mind. If any good comes out of research, management should accept it without any reservation. Likewise, if management makes some suggestions because of its practical insight into business problems, researchers should not ignore them but examine them with an open mind and, if useful, incorporate them in their research. However, I would emphasise one point here. Researchers should not blindly follow the suggestions given by management. If they do so merely to please their authorities, there will hardly be any research worth its name.

Questions

1. Discuss the idea containing the foregoing dialogue?

2. Identify more other areas/situations which may give rise to conflict between Management and Marketing research?

3. What suggestions would you offer to minimize such conflicts?