project on film industry

7/31/2019 Project on Film Industry

1/40

Identification of critical success factors in

the Indian film industry and studying their

interrelationship

MARKETING RESEARCHPROJECT REPORT

UNDER THE GUIDANCE OF

Contents

1. Introduction to the Problem

2. Research Objectives

3. Methodologya) What we did for the primary data

b) Questionnaire in the Project Proposal

c) Questionnaire we actually used in the survey


2/40

d) Secondary Data Collection

4. Samplinga) Sampling the primary data

b) Sampling the Secondary Data

5. Factor Analysis as a data reduction method for primary dataa) Basic Idea of Factor Analysis as a Data Reduction

Method

b) Combining two variables into a Single Factor

6. Secondary Data Analysis and Interpretation

7. Implications and Suggestionsa) Limitations

b) Suggesstions

8. References

Introduction to the Problem

The business of movies is a risk intensive business. For an experiential good likemovies,

Indian film industry is a pretty disorganized one. Most of the movies are made without

keeping in mind the target audience and in that search for that elusive Holy Grail called

box office success our movie makers end up throwing their product to everybody. And

trying to be everything to everybody is not an easy business. Rather it could send you out

of business!


3/40

So if we could somehow determine what the audience want, and jot out success factors

for a movie, a producer/directors life could be made a lot easier. Specifically, as with

other experiential goods, the primary reason for people to consume a movie is to

experience it, rather than expecting it to fulfill a physiological need. This makes the task of

finding out these factors all the more difficult.

There is a lot of subjectivity on what constitutes a good movie and a bad movie. That is

because all movie-goers dont want to experience the same thing. But the basic need is

experience here.

We feel that Indian film industry is to a large extent out of sync with what the

consumer wants. That is reflected in the way how movies in India are marketed and

distributed. Although of late, things have started to change. An example is Yash Raj films

who have tried to follow a more systematic approach of targeting, segmenting their viewers

and then promoting accordingly.

India has the biggest film industry in the world (volume wise). Whereas in other

countries like US where films are made mostly by studios, in India movies are made by

production houses etc. Some relevant data is show below:

Cinema attendance (Top 25 Countries)


4/40

1 India

2 United States

3 Indonesia

4 France

5 Germany

6 Japan

7 China

8 United Kingdom

9 Spain

10 Mexico

11 Canada

12 Italy

Cinema attendance (per capita)

0

1000

2000

3000

4000

5000

6000

Iceland

NewZe

alan

d

Geo

rgia

Cana

da

Ireland

India

Fran

ce

Unite

dKi

ngdom

Switz

erland

Austria

German

y

Portu

gal

Series1

Research Objective


5/40

To determine and interpret the critical success factors for a movie made by the Indian

film industry by collating data from a wide database of movies and movie goers.

Methodology

Our research involved identifying the possible success factors for a movie first. For this

purpose we collected secondary data from around dozen or so sites which are based on

movies and conducted what is known as exploratory research.

Exploratory Research using secondary data

The data collated was totally external and consisted of published materials,

Computerized Databases. We tried contacting professional groups like Times Of

India, Indiafm.com since they regularly publish data about movies. So we thought

that they would be maintaining an archive of the movies they give reviews for. But

ultimately they suggested us to visit their sites only for this information. So the

internet obviously was an important source of information.

- Movie guides giving information about casts, directors, music etc. helped us

thinking about the possible factors

- Directories/web databases (like imdb.com) guided us to other sources of

information and how to avail of them.

After digging data and information web we decided to split our research into two parts.This was necessitated due to the fact that the sample data which needed to be collected was

of two types:-

a) Quantitative which could be determined from secondary sources and which movie goers

couldnt talk about e.g. Revenues, budgets, music sales.


6/40

b) Quantifiable which could be determined from primary sources like movie goers. Eg

Preference for favourite actors, effect of director/banners, effect of sensuous scenes.

What we did for the primary sample data collection?

We agreed on a set of parameters which could be included in a questionnaire. These were:-

Music

Director/banner

Reviews

Script/storyline

Friends/relatives opinion of the movie

Good theatre/multiplex

Quality of promos

Genre

Locales/visual effects

Price of the ticket

Sensuous scenes in a movie

Then we brainstormed about how to collect data from primary sources out of the

possible methods of collecting primary data like surveys, focus groups, depth interviews

we found surveys to be the most feasible way of collecting data. This was more so because

the movie industry is based out of Mumbai and more importantly because of our lack of

contacts with the professionals from this industry. After we decided to go for a survey there

were many options with us telephonic surveys, email based surveys, in home surveys and

mall based surveys.

We chose mall based surveys mostly because of our sampling technique which

involvedcollecting data only from extensive movie goers from the age group of 21 to 30.

Well talk about our sampling technique later in the report, but below are enumerated some

advantages of using questionnaires for collecting data.


7/40


8/40

In the project proposal we submitted we gave out the following questionnaire:

Name: Age: Email:

If earning:

Monthly Income:

If studying:

Monthly spending:

Q1. How many times do you watch a movie in a month (Average)?

1) 0 2) 1-2 3) 3-5 4) >5

If the answer to Q1 is 1) skip the rest of the questionnaire

Q2. Where do you generally watch a movie?

1) At Home on TV 2) At home on vcd 3) In the theatre 4) At friends place

Answer this question only if the answer to the above is 3)

Q3. Do you prefer a multiplex to a normal theatre while making a choice for a movie?

1) Yes 2) No 3) Only when with family 4) Depends on the

movie

Answer this only if the answer to question 2 is other than 3)

Q4. Why do you prefer watching a movie at home?

1) Sheer convenience 2) Affordability 3) Lack of good theaters nearby 4) Would

prefer shopping as an outing


9/40

Q5. What do you think is most important for you in a movie before you watch a movie?

Rank the following in your order of preference starting from 1 (1 is the most important)

Story

Music

Locales where shot

Your favorite Stars

Director

Critical success

Q6. As an independent parameter is story important to you?

Very Important Important Not that important Not important at all

Q7. As an independent parameter is Music important to you?


Q8. As an independent parameter is Locales where shot important to you?


Q9. As an independent parameter is your favorite Stars important to you?


Q10. As an independent parameter is your favorite Stars important to you?



10/40

Q11. As an independent parameter is Director important to you?


Q12 As an independent parameter is Critical success important to you?


Q13 You think you relate to the movies being made these days?

Quite often Sometimes Rarely Never

Q14 Did you like Dil Chahta Hai or Mughal-e-Azam as a movie?

Ans 1) DCH 2) MEA 3) BOTH 4) Havent seen either/one of

them

Questionnaire we actually used for the survey


11/40

We made quite a few changes in the questionnaire structure wise and content wise.

Structure wise: We introduced a continuous rating scale instead of likert scale. We

standardized the format.

Content wise: We removed direct questions like Where do you generally watch a movie

and others which we thought were unimportant to us for our study. This was because we

were clearer about what we wanted to do by then.

So here is the questionnaire:

Name:

Age: 40

Email: Working: Yes / No

Studying: Yes / No Monthly expenditure on movies (approx.):

Q1. How many times do you watch a movie in a month in a theatre (Average)?

1) 0 2) 1-2 3) 3-5 4) >5

For the questions below encircle/tick mark the number, which you think is the most

appropriate indication of the importance of the underlined parameter according to you.

A higher number indicates more importance.

(1= Not important at all) (10 = Extremely important)

Q2. How important is the presence of your favourite actors in the film for you to watch a

movie?

1 2 3 4 5 6 7 8 9 10

Q3. How important is the music of the film for you to watch a movie?


12/40

1 2 3 4 5 6 7 8 9 10

Q4. How important is director/banner of the film for you to watch a movie?

1 2 3 4 5 6 7 8 9 10

Q5. How important are reviews (in newspapers for example) of the film for you to watch a

movie?

1 2 3 4 5 6 7 8 9 10

Q6. How important is the script/storyline (which uve read or heard somewhere) for you to

watch a movie?

1 2 3 4 5 6 7 8 9 10

Q7 How important is your friends/relatives opinion of the movie for you to watch a

movie

1 2 3 4 5 6 7 8 9 10

Q8 How important is a good theatre/multiplex for you to watch a movie?

1 2 3 4 5 6 7 8 9 10

Q9 How important is the quality of promos/trailers/website for you to watch a movie?

1 2 3 4 5 6 7 8 9 10

Q10 How important is the genre (thriller/comedy etc.) for you to watch a movie?


13/40

1 2 3 4 5 6 7 8 9 10

Q11 How important are the locales/visual effects for you to watch a movie?

1 2 3 4 5 6 7 8 9 10

Q12 How important is the price of the ticket for you to watch a movie?

1 2 3 4 5 6 7 8 9 10

Q13 How important is the locales/visual effects for you to watch a movie?

1 2 3 4 5 6 7 8 9 10

Q Is the presence of sensuous scenes in a movie a motivator for you to watch a movie?

a) Yes b) No c) actually has a negative impact

Q 14 How important is then element of sensuous scenes in a movie?

1 2 3 4 5 6 7 8 9 10

THANKS A TON!!! Enjoy your movie.

What we did for Secondary Data Collection

We collected data about the following parameters for secondary data collections

Revenues Money collected at the ticket office throughout India.


14/40

Budget Cost of production incurred by the producer or financier.

User Review User voting on a scale of 10 from imdb.com

Critical Review critical review taken as an average of 2-3 reviews

Music Review user voting on a scale of 10 from planetbollywood.com

All India Cost distributors cost from boxofficeindia.com

Over Seas Gross Revenues collected in US from ibose.com

Music Sales From Boxofficeindia.com

Award Points Used a formula: Fifty points were attributed to a Best Picture

Academy Award, 25 points for each Best Actor, Best Actress and Best Director

award, and 10 points were given for each remaining award category. As five

movies share nominations in each category, points for nominations were divided by

five (e.g., 10 points for a Best Picture nomination)

Distributor share Revenue- Taxes-theater owners margin

ROI Distributor share/All India Investment

In choosing the parameters for secondary data we choose those parameters which are

normally attributed with the success of a movie and indicate whether it has made an

impact on the audiences. For e.g. In Bollywood revenues are a direct indicator of whether

a film has managed to appeal to the mass audiences. Similarly big budget films tend to

have presence of popular stars and large sets.

User Reviews are the ratings given to a particular movie by the audiences and hence

shows its appeal among the audiences watching the movie. Critical reviews are the

reviews given to the movie by experts and bollywood reviewers and play their part in

forming an opinion among the audiences.

Music Review of a film plays a part in creating the public interest in the movie much

before its release. Music of a movie is launched around a month before its release and good

music reviews can ensure a good opening for a movie.


15/40

All India Cost of the movie is the amount which the producers are able to get from the

distributors for their films prior to its release and shows the distributors judgment in

the movie. Typically in bollywood awards are an indicator of a success of a movie. The

more the top awards bagged by the film the more likely is it that it has been successful

commercially.

Due to the absence of any ready to use data we used the option of collecting data from

the internal sources and used already published data and data from computerized

databases from the internet for our requirements. The databases we used were special

purpose databases like the one for movies at www.ibosnetwork.com and

www.boxofficeindia.com we have also used statistical data from the internet for drawing

important insights.

Sampling

Sampling the primary data
http://www.ibosnetwork.com/http://www.boxofficeindia.com/http://www.boxofficeindia.com/http://www.ibosnetwork.com/http://www.boxofficeindia.com/


16/40

Sampling is the process of selecting units (e.g., people, organizations) from a

population of interest so that by studying the sample we may fairly generalize our results

back to the population from which they were chosen

We followed these steps while sampling:

1. Target Population: The first step in good sample design is to ensure that the

specification of the target population is as clear and complete as possible to ensure that all

elements within the population are represented.

Therefore given the time constraints and feasibility aspects we have decided to keep our

target population as 21-30 year college/office going urban movie-goers.

2. Sampling frame/technique: Method by which the researcher can derive a sample

from a POPULATION. The target population is sampled using a sampling frame. Often

the units in the population can be identified by existing information; for example, pay-rolls,

company lists, government registers etc. Naturally, if the aim of a certain study is to learn

things about a certain population, the optimum methodology is to test all members of that

population. It would have been very costly and time-consuming for us to collect data from

the entire population of the movie market. Hence we used sampling techniques

We came across two types of sampling techniques:

1. Probability sampling

2. Nonprobablity sampling

Probability techniques tend to be used for quantitative methods, while non-probability

often is used in qualitative research.

Probability techniques ensure that each sampling unit has a known likelihood of

distribution. This gives unbiased selection of sampling units and proper sampling

representation of the defined populations.


17/40

Examples of probability techniques include:

Simple random sampling: each sample has an equal chance of selection. For example, you

might pull names randomly from a list.

Systematic random sampling: use of an ordered list (example: a membership roster) and

pulling one sample at regular intervals (every fifth name).

Stratified random sampling: divide population into subgroups. For example, dividing all

purchasers into groups based on dollar size of purchase. Then random samples are drawn

from each stratum, and combined into one larger sample.

Cluster sampling: drawn from mutually exclusive subgroups. For example, you might

sample all customers who visit a store on Sundays. These customers compose one discrete

cluster.

With non-probability techniques, the likelihood of sampling each particular unit is

unknown. Sampling cannot be regarded as statistically representative of a larger

population. Examples of these techniques include:

Convenience sampling: samples are drawn at the convenience of the interviewer. For

example, when conducting mall intercepts, the interviewer selects people who are

accessible and willing to participate.

Judgment sampling: the sample of key respondents is believed to possess the attributes

valuable to the researcher. For example, a round-table discussion for a company earning

over $10 million annually may be conducted with ten executives selected because they also

run organizations earning over $10 million annually.

We randomly selected people in our target age group at various multiplexes in Mumbai

especially on weekends and in Delhi.


18/40

So in essence we shall use the probability technique of cluster sampling whereas we

divided target population in two clusters i.e. moviegoers outside of Mumbai and

moviegoers inside Mumbai. We chose to conduct the survey for the movie goers outside of

IIMC on a weekend because thats when people go for movies in droves.

3. Sample size: We kept the sample size open ended. And by the time we ended collecting

surveys we had about 150 sample responses with us. We kept the proportion 50% for

people outside of Mumbai and 50% for people inside of Mumbai.

m

Sample for primary data

Sampling the Secondary Data

Choice of Sample Movies for the secondary data

The secondary data has been compiled from an in depth analysis of about 53 major

movies from the period 2001 to 2004. The data has been taken from movies after 2001 as

Mumbai

Outsiders


19/40

the year 2001 is often considered to be a watershed year in terms of movie production and

the whole experience of movie watching. It was from this year onwards that the impact of

huge multiplexes began to be felt on the movie industry.

The whole movie watching experience began to be seen more in an integrated manner.

It was not just watching a movie anymore; but also included the entire experience of dining

out, spending a romantic evening or just having a blast with friends. The new multiplexes

which sprang up all over the country catered to this category of urban youth for whom

going out in the evening meant a lot more than just watching a movie.

It was also the time when the impact of internet started to be felt on the movie

industry. The advent of the internet had an impact on all aspects of the film industry, right

from how movies started to be marketed to how the movie-goers started to book their

tickets. No single innovation had ever before had so much impact on how movies began to

be viewed.

The year 2001 was also the year from when critics believe that the preferences of the

Indian movie-goers started to show a distinct shift. The traditional musicals of

yesteryears started to loose their sheen as the modern urban movie-goers started to look at

more than just elaborate sets and gaudy song sequences to grab their attention. The

importance of good scripts and powerful performances started to emerge. The Indian

movie-goer had become more knowledgeable and the movie industry wasnt complaining.

Finally the efforts of all the people in the background whose performances often went

unnoticed started to be appreciated.

For our secondary data, the movies have been chosen over this four year period based

on a lot of considerations. The primary basis for selection of these movies is the revenue

that each of these movies have generated. This is because the revenue earned by a movie is

usually directly proportional to the revenue it generates. The other factors that have been

taken into consideration while choosing the movies are factors such as the impact that these

movie might have had on the industry, variation in subjects and scripts, musical


20/40

excellence , etc. We have strived to keep the database as broad as possible by including

movies which are as diverse as possible. It is only then that a proper analysis of all the

factors that contribute towards the success of a movie can be determined.

Sample for primary data

Factor Analysis as a data reduction method for primary data

Now we needed to reduce the factors we have obtained using the exploratory and

descriptive research

The main applications of factor analytic techniques are:

(1) to reduce the number of variables

(2) to detect structure in the relationships between variables, that is to classify variables.

Year 2003Year 2004

Year 2005Year 2006

S1

0

5

10

15

Series1


21/40

Therefore, factor analysis is applied as a data reduction or structure detection

method. In our case it will be used first as a data reduction approach to find out the

important success factors out of the given list of factors we have obtained from the

exploratory research.

Basic Idea of Factor Analysis as a Data Reduction Method

Suppose we want to measure people's satisfaction with their lives. We design a

satisfaction questionnaire with various items; among other things we ask our subjects how

satisfied they are with their hobbies (item 1) and how intensely they are pursuing a hobby

(item 2).

Most likely, the responses to the two items are highly correlated with each other.

Given a high correlation between the two items, we can conclude that they are quite

redundant.

Subjects' single scores on that new factor, represented by the regression line, could

then be used in future data analyses to represent that essence of the two items. In a sensewe have reduced the two variables to one factor. Note that the new factor is actually a

linear combination of the two variables.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .689

Bartlett's Test of

Sphericity

Approx. Chi-Square 196.272

df 66

Sig. .000


22/40

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .

Bartlett 's Test of Sphericity Approx. Chi-SquareBartlett's Test of Sphericity df

Bartlett's Test of Sphericity Sig.

Statistics

KMO and Bartlett's Test

For factor analysis to be applicable on data we require the Kaiser-Meyer-Olkin Measure of

Sampling Adequacy to be greater than 0.5. This is the case in our research. The meaning of

this test is that the variables have to be correlated to each other in order to do factor

analysis on them. In order to validate this we shall have a look at the correlations between

the different variables.

Where:-Starpw = Star Power

Music = Music Reviews

Dirban = Director/Banner

Review = Critical/User Review

Script = Storyline


23/40

Fropin = Friends/Relatives Opinions

Theatre = Quality of multiplexes/movie halls

Promo = Ads/Trailers/Promos

Locales = Locations/Sets/Cinematography

Ticpric = Ticket Pricing

Sensex = Sensual scenes in the movie

A cursory look at the correlation table above suggests a good amount of correlation

between the variables.

Principal Components Analysis. The example described above, combining two

correlated variables into one factor, illustrates the basic idea of factor analysis or of

principal components analysis to be precise. If we extend the two-variable example to

multiple variables, then the computations become more involved, but the basic principle of

expressing two or more variables by a single factor remains the same.

Expressed as a pie chart it looks like this:


24/40

Starpw

Music

Dirban

rev iew

script

Fropin

Theatre

Promo

Genre

Locales

Ticpric

Sensex

Variables

Starpw

Music

Dirban

rev iew

script

Fropin

Theatre

Promo

Genre

Locales

Ticpric

Sensex

Variables

Correlation

Sig. (1-tailed)

Statistics

Correlation Matrix

The principal component analysis has basically extracted out four components out of 12

variables we chose. The four components cumulatively explain about 53% of the data.


25/40

1

2

3

4

Component

Starpw

Music

Dirban

rev iew

script

Fropin

Theatre

Promo

Genre

Locales

Ticpric

Sensex

Variables

Component Matrix

1

2

3

4

Component

Starpw

Music

Dirban

rev iew

script

Fropin

Theatre

Promo

Genre

Locales

Ticpric

Sensex

Variables

Rotated Component M atrix


26/40

Combining two variables into a Single Factor.

One can summarize the correlation between two variables in a scatter plot. A

regression line can then be fitted that represents the "best" summary of the linear

relationship between the variables. If we could define a variable that would approximate

the regression line in such a plot, then that variable would capture most of the "essence" of

the two items.

Basically, the extraction of principal components amounts to a variance maximizing

(varimax) rotation of the original variable space. Now as to how we interpreted SPSS

output with regards to the combining of the variables is as follows. Looking at the pie

charts above and the table below we can see that according to SPSS variables Theatre,

Promo, Genre, Locales can be clubbed into one factor; Star power, director/banner and

music into another; friends opinion, critical review and storyline/script into the third and

finally price of the ticket and presence of sensual scenes in the movie fourth.

Rotated Component Matrix (a)

Component

1 2 3 4

Starpw -.003 .530 .279 .242

Music .278 .716 -.073 -.046

Dirban .058 .765 .017 -.117

review -.040 .295 .702 .122

script .326 -.066 .503 .060

Fropin -.011 -.054 .664 -.224

Theatre .615 .055 .328 -.037

Promo .638 .135 .026 .216

Genre .685 .218 -.070 .030

Locales .695 -.008 .033 -.061

Ticpric .316 .044 .172 .701Sensex -.201 -.100 -.354 .676

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 6 iterations.

Where:-


27/40

Starpw = Star Power

Music = Music Reviews

Dirban = Director/Banner

Review = Critical/User Review

Script = Storyline

Fropin = Friends/Relatives Opinions

Theatre = Quality of multiplexes/movie halls

Promo = Ads/Trailers/Promos

Locales = Locations/Sets/Cinematography

Ticpric = Ticket Pricing

Sensex = Sensual scenes in the movie

So the four critical success factors are:

1. Creativity Cost: This factor clubs the three variables Star Power,

Director/Banner, Music. Creative cost is the factor which basically determines the

funneling of money for trying to attain creative excellence. Thus whereas there

many other facets to budget like costs on sets etc. this factor concentrates on only

the money spent on creative aspect. This factor is responsible for the initial draw of

the crowd to the theatres. The kind of stars in a movie and the director and the

success of the music is expected to play a part in the successful opening of the film.

We believe that the rest of the creativity parameters e.g. screenplay, dialogues etc

are hugely dependant on the director.

A film like The Rising is expected to draw initial audiences to the theatres just

based on the sheer presence of a star like Aamir Khan because he presents an image

of working in good films and hence the film carries a tag of credibility in the eyes

of the audiences initially. Similarly a director like Ram Gopal Verma is able to get

good opening for his movies even though they may not eventually become huge

successes. Music if highly successful tend to draw in audiences as shown by the

example of Veer Zaara.


28/40

2. Audience Feedback: This factor consists of the three parameters friends

opinion, critical review and storyline/script. After the initial draw of audiences to

the theatres by the critical budget this factor is necessary to draw further audiences

and repeat audience to the theatres. This factors help in creating the buzz around the

movie. For this to happen the movie has to have go further than having the star

appeal and a good banner and director and has to appeal to the audiences.

Many movies in spite of the good star appeal and good banners, despite a good

initial opening have done disappointing business later on precisely due to weak

audience feedback. A movie like Yaadein directed by Subhash Ghai and starring

Kareena Kapoor and Hrithik Roshan went on to be a huge disappointment whereas

movies have picked up businesses despite seeing sluggish opening. Style starring

obscure actors did brisk business after a sluggish opening due to it being highly

appreciated by the audiences and getting good reviews as a slick comedy.

3. Experiential satisfaction: Once again we emphasize that movies are an

experiential product. This factor clubs the variables like quality of theatres, genre,

promos, and locales. This measures the feel good factor among the movie goers.

The theatre in which a movie is running plays a significant part in the business of a

movie.

The movies which run in up market theatres and multiplexes which offer a

complete movie watching experience to the audiences with DTS Sound and air

conditioning and other add-ons like restaurants and games parlours are more likely

to draw in more families to watch the movies than one which is released in an

obscure theatre. Similarly locales and genres of a movie play an important part in

success of a movie in India.

A movie like Hum Aapke Hain Kaun because of its genre based on a North Indian

joint family with lots of marriage and festive scenes thrown in was a smash hit


29/40

because it was complete family viewing experience for the audiences. Baghban

was a similar example.

Similarly Company with its genre of the Mumbai underworld was a success. Also

use of foreign locations and slick sets further enhances the feel good factor. Promos

play their part in creating this feel good experience by highlighting the above

aspects of the movie. Television channels and the rapid growth of internet have

made it possible to watch trailers of a movie easily. Gone are those days where

publicity was limited to songs on chitrahaar. So an expectation of experiential

satisfaction could work wonders for a movie. Promos typically shown in between

the interval of a movie are the most effective.

4. Value for Money: This factor consists of the parameters price of the tickets

and the sensuous scenes in a movie. This factor mainly affects movies which are

low budget to medium budget. Such movies are usually targeted at the B and C

centers where the above parameters play an important role. Hence we see the trend

of a raunchy item number added to most of the movies to do business in these

centers.

These also partially explain the fact why there were so many low budget movies

starring Mithun in the early 90s not only recovered their costs but went on to do

brisk business. Of late movies like Jism and Murder have been successful in such

centers due to being released in theatres which were low priced and had sensuous

content.

Secondary data analysis and interpretation


30/40

Before we go on explaining the analysis we did for secondary data wed like to explain

how the movie industry operates. This is a simplistic view

Movie industry (Agents and transaction costs)

The flow of money is depicted from viewers to theater owners to distributors to producers.

We have used the following in finding out correlation between ROI and other independent

variables:

Movie (ready to bereleased)

Theater/Multiplexesowners (weekly coststo distributors)

Movie released

Viewers (ticketprice)

Producer/Finanier Budget(costof production)

Movie (ready to bedistributed)

Distributor(costs of prints toproducer, publicitycosts)


31/40

Gross revenues = Money collected at ticket counters across India

Net revenues = Gross revenues Taxes and duties

Distributor share = Net revenues margin of multiplex and theatre owners

All India costs = costs incurred by distributors

ROI = Distributor share/ All India costs

Secondary data analysis was done prior to the primary analysis and is prone to a large

standard error. That does not mean that it is useless and does not point out anything.

Secondary data collection was the most tedious of tasks as it involved visiting a plethora of

sites and collecting data about various films.

Large standard error is attributed mainly to size of the sample (we collected data for about

53 movies and ended up running regression only on 45); on consistency of the data

collected (although the data collection was done from a-priori agreed to sites and was from

reliable sites like imdb.com and boxofficeindia.com. Certain factors like inflation have not


32/40

been accounted for while considering budgets/revenues.

R

R Square

Adjusted R SquareStd. Error of the Estimate

Statistics

Model Summary

Model : 1

Hence the secondary data analysis although exhaustive and most time consuming can at

best serve as the basis for conducting primary analysis and not really a contributor to the

number of critical factors.

Some analysis:

Model Summary

Model R R SquareAdjusted R

SquareStd. Error ofthe Estimate

1 .603(a) .363 -.274 17.00051

a Predictors: (Constant), Music sales(in million units)(from boxofficeindia.com), Awards Points( usingformula screen awards/filmfare awards), Music review(/10)(planet bollywood.com), All India cost(crores)(boxofficeindia.com), Overseas gross(only usa's data- million dollars)(imdb.com), User rating(/10)(imdb.com), Critical review(/5)(imdb.com- average of 2-3 reviews), Budget(crores) (boxofficeindia.com)


33/40

We can see a large standard error of estimate in the model summary.

Coefficients (a)

Model

UnstandardizedCoefficients

StandardizedCoefficients

t Sig.B Std. Error Beta

1 (Constant) -22.060 64.917 -.340 .743Budget(crores)(boxofficeindia.com)

-.648 .884 -.456 -.732 .485

User rating(/10)(imdb.com) 2.395 5.085 .195 .471 .650

Criticalreview(/5)(imdb.com-average of 2-3reviews)

-2.153 5.297 -.167 -.406 .695

Musicreview(/10)(planet

bollywood.com)

1.814 6.383 .100 .284 .784

All indiacost(crores)(boxofficeindia.com)

1.703 2.549 .374 .668 .523

Overseasgross(only usa'sdata- milliondollars)(imdb.com)

-.134 .239 -.189 -.563 .589

AwardsPoints( usingformula screenawards/filmfareawards)

.137 .122 .467 1.126 .293

The Dependent Variable taken in the Regression Analysis is the Return on Investment

to the Distributor (as defined earlier). If the RoI is greater then 1 then the movie is

categorized as hit. In case the RoI is less then 1 then the movie is a flop. For values close to


34/40

1 the movie is considered to have done average business.

Also we have used standardized beta coefficients instead of unstandardised because of they

have a zero mean and std. deviation 1. That means we are not dealing with coefficients

which have different std. deviations and means.

Beta Coefficients of the Independent Variable with respect to Dependent Variable

Budget (-0.456)

The negative correlation suggests that 2001 onwards the big budgeted movies have more

then often bombed on the box-office, to name a few Swades, Deewar, Bride and Prejudice

etc. At the same time certain low budget movies with novel concepts have made it big on

the screen, like Murder, Raaz and Mujhe Kuch Kehna Hai. Movies that had the budget

spent on Creative Inputs did well where as Movies relying on the Strong and Long Star

Cast eventually bombed.

User Rating(0.195) (This is based on the voting done by the users on imdb.com)

This is one another indicator of a films success. If the people watching the movie on the

first day first show like it, half the success is achieved. These are the people who will go

out and spread the positive word amongst their social circle. Even if a person goes out a

speaks well about the movie to just 4 of his close pals in the whole day. It will only take 8

days for the movie to be publicized amongst entire population of a huge city like Kolkata.

There have been many movies in the past that had a small opening due to lack of big

publicity or weak star cast, but as the word spread, the box office sales picked up and

turned into a blockbuster. For example Andaaz, MunnaBhai MBBS and Koi Mil Gaya.

At the same time there were movies with immense pre-release hype and a sold out first

week before eventually dying out in the third week itself, like LOC Kargil and Raincoat.

Critical Review(-0.167)


35/40

This finding dilutes the thinking that the critics are the real judge of a movies success or

failure. There are several movies which despite scoring badly with the critics went on to

become super hits. These were movies which touched the masses but not the creatively and

artistically inclined self proclaimed critics. Like Gadar, Baghban and Indian.

Music Review(0.100)

The score indicates that a good musical performance helps a movie to pick up with the

masses. It helps in drawing people to the theatre. As the title song catches the minds and

chords of people, an undercurrent of publicity spreads. Just a thought of Dhoom Machaale

Dhoom number and the craze it unleashed substantiates the point.

But at the same time the correlation is not very strong due to a considerable number of

movies like Ab Tak Chappan coming up with almost negligible music score but a very

hard hitting and to the point story and screenplay.

All India Cost (0.374)

All India cost is the cost incurred all over India by the distributors on the prints and

publicity. The strong positive value indicates two things. Firstly; the distributors are more

then often able to judge the performance of the movies on the box office. Secondly, the

expenditure on the pre and post release publicity provides a constant feed to the empty

theatre seats. So at the end of the day, its the penny well spent for the distributors.

Overseas Gross(-0.189)

There are certain movies like Yaadein, Out of Control and Bride & Prejudice that are being

made with the target audiences of NRIs in mind. It is now an over used concept. Such

movies have very less appeal left for the Indian Distributors. We can say that Indian

audiences/distributors have little interest left for such movies over the last few years.

Awards Points (0.467)

The very strong positive correlation of Award Points with the Hit movies is not of huge


36/40

significance as the Awards are often given to the Commercially Hit Movies. Rarely has it

been the case that the Filmfare or Screen Awards went to a movie that fared poorly on Box

Office.

Implications and suggestions

Here we shall try to reconcile our findings from both primary and secondary studies and

pointing out possible limitations in our findings.

Reconciling both the studies

As discussed earlier we found out 4 critical factors which determine the success of a movie.

FINAL CRITICAL FACTORS AND THEIR VALIDITY

Creativity costs

Whereas there are many other facets to budget like costs on sets etc. this factor

concentrates on only the money spent on the creative aspect. Now our secondary study

reveals a negative correlation between budget and ROI so isnt that a contradiction? It can

be argued that to the extent money is spent on putting up a good creative team it can be

called money well spent. But beyond that the trend has been suggesting that large amounts

spent on other production costs can mean an increased chance of movie bombing.

Audience feedback

We can say that to the extent user review from secondary study suggests that audience

feedback is important. But critical reviews have actually revealed a negative correlationwith ROI. That means that common masses in India might have a different interpretation of

a good movie than critics. But more or less the importance of audience feedback is

validated by both studies.

Experiential satisfaction


37/40

There is a positive correlation between distributor costs(publicity costs) and ROI. That

means money spent on publicity is money well spent. That also explains the way publicity

is done has changed since 2000-2001. Movie websites have sprung up, promos are much

sleeker, PR events are being organized prior to release of the movie.

Value for money

People want to escape reality when they watch a movie. They are there for a vicarious

experience in the most affordable cost possible. So if you are a multiplex make sure that u

offer a whole value added product which includes shopping, food along with the movie.

That is when people wont mind spending more on the ticket price. Another interesting

trend is the ITEM GIRL, sensuous scenes phenomena. There could be a whole study on

as to why this has happened, but again we believe that people in cities have started being

more open to the concept of sensuality vis--vis those in rural areas.

Limitations

1. We have conducted a sample survey amongst people going to multiplexes and

amongst people from IIMC and that too in the age group of 21-30. Now what they feel

about a movie might not be a true reflection of the general masses, so a limitation is that

the success factors we have enumerated are a reflection at best only of the population in the

age group 21-30, living in large cities, visiting multiplexes, watching 2-3 movies a month

in a theatre.

2. Sample size- Although we feel that 150 sample surveys are good enough for

primary data, we feel we could have possibly missed on some variables. This is inspite of

the fact that we have tried to include whatever the exploratory research suggested to us.

But definitely for secondary data sample size (53 movies) is an issue, albeit a difficult issue

to resolve. We would definitely have loved to get information from a professional agency

which was not possible due to our lack of contacts, although we made a few futile attempts

to get information from them.


38/40

3. Multicolliearity The independent variables that have been used in the analysis

also have some degree of inter-dependence amongst thamselves. This factor has not been

taken into account while doing the analysis.

Suggestions -

We dont claim we have derived a formula for success for a movie. And anybody who

claims that we feel is either out of his mind or is named Arindam Chaudhary!! Jokes apart

this research gives us four critical factors whose interplay can try to explain why a movie

succeeds. We again dont claim there could not be other extraneous factors which affect the

success of a movie. But we need more time and more information in order to determine

those.

Some suggestions for people pursuing research on the subject :

1. Avoid Multiple Regression - A fundamental assumption of this method is that the

factors used as regressors share no common variance, i.e., are statistically independent.

Interrelated factors in a regression model imply multicollinearity, which strongly distorts

regression. In order to avoid this there two techniques people can use to do research on

factors which are interrelated.

While the first technique applies a sequential modeling approach that considers a number

of demand-side factors (e.g., star power, advertising expenditures) and supply-side factors

(i.e., number of screens on which a movie is released) simultaneously to explain movies

success in foreign markets, the latter uses path analysis to identify differences in

importance of factors between theatrical box office and video rental revenues.

2. Take customer as well as producer perspective

Both people who are supplying(producers) and people who are consuming(moviegoers)

should have a say in your research project. Focus interviews should be conducted amongst

producers/directors to get their experience into view as a part of exploratory research.


39/40

Contributions

Gaurav Chillar

Primary Data Collection, Regression Analysis, Preparation of the report

Gaurav Thapar

Secondary Data Collection (for the year 2004), Primary Data Collection, Factor Analysis,

Regression Analysis, Preparation of Questionnaire

Amit Tyagi

Secondary Data Collection (for the year 2002), Primary Data Collection, Factor Analysis,

Report Preparation

Deepankar Nayak

Secondary Data Collection ( for the year 2003), Primary Data Collection, Preparation of

Report, Preparation of Questionnaire

Amit Marandi

Secondary Data Collection (for the year 2003), Primary Data Collection, Preparation of

report, Preparation of Project Proposal

Bhaskar SenguptaPrimary Data Collection, Secondary Data Collection (for the year 2001), Factor Analysis,

Preparation of Questionnaire

Abhishek Guru

Primary Data Collection, Preparation of Report, Preparation of Project Proposal


40/40

References

1. Determinants of Motion Picture Box Office and Profitability: An Interrelationship

Approach by Thorsten Hennig-Thurau, Mark B. Houston, and Gianfranco J. Walsh (Sept

2003)

2. http://www.tutor2u.net/business/marketing/research_sampling.asp

3. http://www.imdb.com

4. http://www2.truman.edu/shaffer/266ch4_2001.htm

5. http://www.planetbollywood.com

6. http://www.boxofficeindia.com

7. http://www.ibosnetwork.com

8. http://www.filmfaremagazine/indiatimes.com

9. http://www.rediff.com
http://www2.truman.edu/shaffer/266ch4_2001.htmhttp://www.planetbollywood.com/http://www.boxofficeindia.com/http://www.ibosnetwork.com/http://www.filmfaremagazine/indiatimes.comhttp://www.rediff.com/http://www2.truman.edu/shaffer/266ch4_2001.htmhttp://www.planetbollywood.com/http://www.boxofficeindia.com/http://www.ibosnetwork.com/http://www.filmfaremagazine/indiatimes.comhttp://www.rediff.com/

project on film industry

Documents