Project Bollywood - Final Report

8/7/2019 Project Bollywood - Final Report

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Cross Tabulation

As one of our objective to find out the preference of the respondents, hence we

decided to carry out cross-tabulation on the basis of the satisfaction level of the

respondents and their frequency to watch a movie in the theatre in a month. The

area in which the preferences were looked after were

1) Which kind of Directors philosophy they liked in watching a movie?

2) What kind of actors do they prefer to watch for particular script or directors

philosophy?

3) Which kind of music do they prefer in a movie?


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From the cross-tabulation it is clear that those who are satisfied or neutral about the

current bollywood movies, they will prefer to watch a movie which is close to reality.

They feel that actors who have established themselves in this field are fit to portray

characters. They felt that Romantic or soft numbers will really add value to such

movies. One interesting fact from this survey is that even those who are currently

not satisfied or those who watch movie more than once in month share the same

philosophy.

Preference of the respondents who are

Type

Satisfied

andNeutral

dissatisfied

& VeryDissatisfied

Freq

morethanonce

Actor Established 59 15 54

Music Soft 28

Romantic 29 11 30Director Realistic 34 8 29

Sarcasticcomedy 7

About 92 % of the respondents watches movie for entertainment purpose and at the

same time they want that it should be more close to reality. It is also well clear from

the type of script they prefer to watch. From the graph below, we can see that

maximum number of respondents wants the script of the film should be close to

reality or current issues prevalent in the society.


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It is not that other scripts or philosophy wil not work, for example Dhoom was aTrendy and stylish kind of movie with all fast music numbers and comparatively

new face, but movie like 3 Idiots, Taare Zameen Par etc were more preferred with

a repeat audience. The main USP of 3 idiot was all the factors as preferred by the

respondents.

The above survey does not indicate that our TG group wants a real life story in a

serious manner. They want that this philosophy should be explained in a light way

or in comic way. This is clear from the graph below. Maximum number of

respondents prefered comedy movies over other genres like romance, action,

thriller etc.

On this front also, 3 Idiots scores well. The movie brought about the problem of

graduating youth in a light humorous way which was well accepted by our TG.


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From cross-tabulation, it is clear that maximum respondent prefered comedy.

However, there were many respondents who liked action, comedy, romance and so

on. Hence combining these genres, we found out that maximum respondents who

has multiple genre choices, majority of them prefered either Romantic Comedy or

action comedy. Few of them prefered Action Romance Comedy.

Multiple Regression to find relationship between Satisfaction level of the Respondent and

the main attributes of the film

Variables Entered/Removed(b)

ModelVariablesEntered

VariablesRemoved Method


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1

Location,Hero, Film

Title, Genre,Director,Heroine,

Songs,Prod.House

, Story(a)

. Enter

a All requested variables entered.b Dependent Variable: Satisfaction

Model Summary

Model R R SquareAdjusted R

SquareStd. Error ofthe Estimate

1.340(a) .116 .041 .794

Coefficients

Model

UnstandardizedCoefficients

StandardizedCoefficients

t Sig.B Std. Error Beta

1 (Constant) 3.589 .433 8.296 .000

Genre -.056 .069 -.084 -.804 .423

Film Title -.074 .075 -.104 -.990 .324

Story .212 .084 .346 2.520 .013

Hero -.072 .084 -.098 -.854 .395Heroine -.066 .075 -.095 -.886 .378

Director -.210 .086 -.270 -2.451 .016

Prod.House

.138 .083 .182 1.667 .098

Songs -.068 .074 -.102 -.909 .365

Location .048 .070 .070 .681 .497

The main aim of carrying out multiple regression was to find a correlation between the

satisfaction level of the respondent with the various attributes of the movie such as genre, title,

actor, director etc.

But as seen from the table, after running multiple regression in SPSS, it is clear that the change

in the satisfaction level is explained by only 11.6% change in the above mentioned independent

variable. Hence, there may be other variables affecting the satisfaction level of the respondents.


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Checking out the multicollinearity, it was found that there was much any relation between the

independent variables hence clarifying that each variable is independent with respect to each

other.

Analysis of Q 10 - Promotional activities influencing the respondents to watch a movie

In order to find out, whether the variables such as TV promo, Music, PR activities, Critics

Review, Word of Mouth etc have an equal impact on the respondents in influencing them to

watch a particular movie, we first applied one way ANNOVA test.

One way ANNOVA Test


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Null Hypothesis :- All the variables mentioned above create an equal impact in influencing

people to watch a movie.

Alternative Hypothesis :- All the promotional effort have different impact on influencing


ANOVASource of

Variation SS df MS F

P-

value F crit

BetweenGroups 99.25 5 19.85

24.406578 1E-22

2.226649

Within Groups 580.7 7140.81330

53

Total679.9

5 719

As F(observed) > F Critical, our null hypothesis is rejected, and it is clear that all promotional effort

has different impact on influencing people to watch a movie.

To find out which of these promotional variables should be focused more, which can highly

influence the people. For that we carried out Factor Analysis.

Factor Analysis

Methodology

1. Different kind of variables such as TV promo, Music, PR activities, Controversies, Word

of mouth etc as attributes on basis of which factoring is to be done.

2. Using Principle component method for data extraction and saving factor scores as

variables to be formed basis for cluster analysis


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The Output and Analysis

From the table of Total Variance Explained, we found out only two factors which explain about

53% cumulative variance of the variables given. This is mainly due to the fact that all the

variables are not having much difference. Hence we can say that all the promotional variables

can be grouped in to two factors namely Direct Promotion and Indirect Promotion

Total Variance Explained

Comp

onent

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 1.766 29.440 29.440 1.766 29.440 29.440 1.611 26.857 26.85

2 1.411 23.511 52.951 1.411 23.511 52.951 1.566 26.094 52.95

3 .917 15.291 68.242

4 .811 13.519 81.760

5 .566 9.442 91.202

6 .528 8.798 100.000

Extraction Method: Principal Component

analysis

Rotated Component Matrixa

Component

1 2

TV_Promo .756 -.013

Music .825 -.070

PR_Activities .587 .303

Critic_reviews -.034 .752

wordofmouth .034 .726

controversies .110 .613

Extraction Method: Principal Component

Analysis.

Rotation Method: Varimax with Kaiser

Normalization.


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Direct Influence :- As seen from the Rotated Component Matrix, TV Promo, Music and PR

Activities are the variables which are related to the above factor.

Indirect Influence :- As seen from the Rotated Component Matrix, Critic review, Word of

mouth, Controversies are the variables which are related to the above factor.

Cluster Analysis

We carried out hierarchical clustering using agglomeration scheduling. We used Dendogram plot

to find out the clusters. Warde method of linkage was used and squared Euclidean distance used

as a basis to find out the clusters.


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Agglomeration Schedule

Stage

Cluster Combined

Coefficients

Stage Cluster First Appears

Next StageCluster 1 Cluster 2 Cluster 1 Cluster 2

1 55 113 .000 0 0 67

2 77 110 .000 0 0 7

3 97 108 .000 0 0 111

4 100 101 .000 0 0 29

5 93 94 .000 0 0 75

6 20 92 .000 0 0 15

7 18 77 .000 0 2 32

8 34 68 .000 0 0 79

9 57 66 .000 0 0 19

10 44 56 .000 0 0 46

11 3 47 .000 0 0 78

12 31 33 .000 0 0 40

13 28 29 .000 0 0 90

14 12 27 .000 0 0 45

15 19 20 .000 0 6 43

16 1 4 .000 0 0 65

17 52 114 .000 0 0 39

18 39 116 .001 0 0 50

19 57 62 .002 9 0 86

20 6 8 .004 0 0 56

21 43 72 .007 0 0 73

22 79 87 .010 0 0 44

23 40 83 .013 0 0 27

24 46 112 .015 0 0 76

25 64 89 .018 0 0 58

26 9 80 .021 0 0 48

27 7 40 .025 0 23 52

28 96 120 .029 0 0 41

29 23 100 .032 0 4 47

30 98 103 .036 0 0 68

31 10 76 .040 0 0 39


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Dendrogram using Ward Method

C A S E 0 5 10 15 20 Label Num +---------+---------+---------+---------+---------+

55 113 39 116 11 34 68 16 118 13 25 3 47 9

80 85 100 101 23 50 95 97 108 86 109 1 4 78 107

40 83 7 14 117 57 66 62 44 56 59 58 98 103 61

48 6 8 67 102 91 46 112 2 36


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96 120 63 71 77 110 18

88 74 79 87 37 12 27 5 64 89 75 82 99 41 54

31 33 21 115 24 119 70 43 72 26 52 114 10 76 53

65 32 111 38 106 49 22 93 94 104 30 81 84 60 45

42 35 51 73 28 29 20 92 19 105 17


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15 69

90

Based on the coefficients and looking at the Dendogram, we found out that there are two clusters

with different characteristics.

Cluster

TVProm

o

Music PRActivit

ies

Criticreviews

wordof

mouth

controversies

No ofrespond

ents

1 4.39 4.47 3.92 4.13 4.68 4.11 38

2 4.30 4.66 3.66 3.13 3.71 3.11 56

The above table shows the mean of the values for the given variables for each cluster. It is seen

from table that clusters 2 which has maximum respondents music has the high mean value as

compared to cluster -1. Also there is not much significant difference in the mean values for TV

promo and PR activities.

From the cluster Analysis, we can interpret that Direct Influencing variables like TV

Promo, Music, PR Activities should be given more importance while designing promotion

campaign in order to influence people to watch movies.


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Analysis of Q 5 - Factors influencing the respondents to watch a movie

In order to find out, whether the variables such as :

GenreFilm TitleStoryLead HeroLeadHeroineDirector

ProductionHouseSongsSets/Location

have an equal impact on the respondents in influencing them to watch a particular movie, we

first applied one way ANNOVA test.

One way ANNOVA Test

Null Hypothesis :- All the variables mentioned above create an equal impact in influencing


Alternative Hypothesis :- All the promotional effort have different impact on influencing


ANOVASource of

Variation SS df MS F P-value

Between Groups 55.56296296 8 6.945370375.0650

223.36792E-

06

Within Groups 1468.6 1071 1.37124183

Total 1524.162963 1079

As F(observed) > F Critical, our null hypothesis is rejected, and it is clear that all the different factor

has different impact on influencing people to watch a movie.


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To find out which of these promotional variables should be focused more, which can highly

influence the people. For that we carried out Factor Analysis.

Factor Analysis

Methodology

1. Different kind of variables such as Genre, Story, Lead Hero etc as attributes on basis of

which factoring is to be done.

2. Using Principle component method for data extraction and saving factor scores as

variables to be formed basis for cluster analysis

The Output and Analysis


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From the table of Total Variance Explained, we found out only three factors which explain about

60% cumulative variance of the variables given. This is mainly due to the fact that all the

variables are not having much difference. Hence we can say that the entire influencing variable

to watch a movie can be grouped in to three factors namely Cast, Crew& Content, Film Identity

and X-fator.

Cast, Crew& Content: - As seen from the Rotated Component Matrix, Story, Lead Hero,

Lead Heroine and Director are the variables which are related to the above factor.

Film Identity: - As seen from the Rotated Component Matrix, Film Title and Production

House are the variables which are related to the above factor.

X-factor:- As seen from the Rotated Component Matrix, Songs and Sets/Location are the

variables which are related to the above factor.

Cluster Analysis

We carried out hierarchical clustering using agglomeration scheduling. We used Dendogram plot

to find out the clusters. Warde method of linkage was used and squared Euclidean distance used

as a basis to find out the clusters.


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Agglomeration Schedule

Stage

Cluster Combined

Coefficients

Stage Cluster First Appears

Next StageCluster 1 Cluster 2 Cluster 1 Cluster 2

1 100 101 .000 0 0 38

2 48 49 .000 0 0 57

3 43 46 .000 0 0 62

4 28 29 .000 0 0 107

5 67 84 .004 0 0 72

6 15 117 .016 0 0 21

7 61 64 .029 0 0 102

8 80 94 .046 0 0 88

9 74 114 .066 0 0 32

10 12 16 .090 0 0 82

11 7 23 .116 0 0 45

12 13 95 .145 0 0 16

13 2 31 .175 0 0 22

14 17 33 .212 0 0 56

15 86 112 .255 0 0 32

16 4 13 .303 0 12 43

17 5 11 .350 0 0 69

18 14 90 .399 0 0 67

19 51 91 .449 0 0 27

20 27 89 .501 0 0 60

21 3 15 .554 0 6 30

22 2 75 .608 13 0 56

23 9 120 .662 0 0 42

24 98 105 .722 0 0 89

25 38 42 .783 0 0 70

26 93 99 .845 0 0 65

27 19 51 .921 0 19 60

28 81 115 .996 0 0 64

29 10 56 1.072 0 0 46

30 3 72 1.156 21 0 85

31 34 96 1.241 0 0 39


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Dendrogram

* * * * * * * * * * * * * * * * * * * H I E R A R C H I C A L C L U S T E R A N A LY S I S * * * * * * * * * * * * * * * * * * *

Dendrogram using Ward Method

Rescaled Distance Cluster Combine

C A S E 0 5 10 15 20 Label Num +---------+---------+---------+---------+---------+

100 101 83 12 16 24 70 107 77 118 102 81 115 27

89 51 91 19 15 117 3 72 74 114 86 112 26 52 116

108 82 9

120 111 53 93 99 25 79 97


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110 73 104 85 88 109 6

5 11 78 98 105 30 76 8 34 96 66 22 92 17 33

2 31 75 80 94 13 95 4

119 67 84 65 103 28 29

14 90 71 113 7 23 18 87 1 48 49 39 50 35 61

64 55 62 106 60 10 56 69 38 42 47


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68 37 40 43 46 41 63

45 54 36 58 44 57

59

Based on the coefficients and looking at the Dendogram, we found out that there are two clusters

with different characteristics.

Cluster

Genre

FilmTitle

Story Leadhero

LeadHeroi

ne

Director

Production

House

songs Sets /Locati

on

N

1 3.25 2.86 4.20 3.63 3.58 3.82 3.14 3.20 2.41

2 3.00 2.95 3.41 3.64 3.36 2.91 3.09 4.32 4.18

3 2.10 3.55 1.45 2.30 2.30 2.45 2.95 1.95 2.65

The above table shows the mean of the values for the given variables for each cluster. It is seen

from table that clusters 1 which has maximum respondents Genre, Story, Lead Hero, Lead

Heroine, Director and Production House has the high mean value as compared to cluster -2

and3.

From the cluster Analysis, we can interpret that Cast, Crew and Content factor should be

given more importance while making a movie in order to influence people to watch movies.


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Que-11-Satisfaction Level with the Movie

Here we used one sample z-test to find out about the satisfaction level of the respondent

with the movie released in the recent past.

So we construct a null and alternative hypothesis.

Null Hypothesis---mean is greater than or equal to3

Alternative hypothesis---mean is less than 3Critical value for alpha=0.05 will be 1.6577 onetail t test.

So we calculated t(calculated value)-1.806

So here Tcal> Ttab

So we will reject the Null Hpothesis and will accept the Alternative Hypothesis.

It means the most of the people in sample believe that they are not satisfied with the kind of

movie in the recent past.

Documents

Project Bollywood - Final Report