Increasing Movie Viewership: A Promotional Campaign Strategy Research

Increasing Movie Viewership: A Promotional Campaign Strategy Research

1301-362 Mansi Gupta1301-094 Kannan T S1301-167 Rahul Singh1301-076 Gaurav Agarwal1301-574 Utkarsh Bhatnagar1301-036 Aseem Shandilya

Introduction to the Movie Industry

• Hollywood• The earnings for at the global box

office for all films released in each country around the world reached $34.7 billion in 2012

• Bollywood• Out of a population of 1.2 billion,

only 45 million watch movies (Almost 4%)

• Generating revenue of $3 billion in 2011. Expected to grow by 10% every year and reach $4.5 billion by 2015

• There are about 12,900 screens in India of which almost 1300 are multiplexes. • These multiplexes account for almost

75% of the total revenue of the Indian movie industry.

Objective of the Research

• To understand the driving forces behind a user’s decision to watch a movie

• As a pilot research undertaken before the actual research

Scenario: A multiplex/movie streaming website wishes to launch a promotional

campaign that is designed to provide offers customized to every viewer/customer according to their needs/habits”

Scope of Study

• Studies following parameters:• Genre• Ratings• Cast (Actors/Actresses)• Soundtrack• Length• Name of the director• Reviews (Online/Print)• Reviews from friends• Trailers• Name of the production house

• Limited to the Indian context as all the respondents belong to said nationality. • Methodology of data collection could

not include individuals of different nationalities.

• Most of the respondents were in the age group of 18-29• The report cannot be said to be

applicable to the population at large.

The Identified Problems

• Management Problem: How do we increase the customer transactions at our theatre/website?

• Research Problem(s):• What factors help a customer decide whether to watch a movie or not?• Do all the factors carry the same weight in the decision making process?• Is there a relation between these factors? If so, what?• What causes these factors to vary? How often do they vary?

This paper covers only research problems 1, 2 and a part of 3 as of now.

Methodology (Data Collection)

• Instrument: • The data collection instrument being

used is a questionnaire• Questionnaire attempts to collect

data from two points-of-view:• In the first, it asks the respondent to

rate the various factors according to his/her preference.

• In the second, it asks the respondent whether they feel the factor is important when it comes to a movie’s success

Basic Research Method that is exploratory in nature

• Location: • Done online using a form designed with

Google forms• Chosen for the absence of any charges

and no limits on the number of respondents

• Done online owing to: • Ease-of-use and access to a large target

audience• Researchers’ limited mobility was also

taken into account• Ability to remind the intended

respondents again and again with minimum hassle

Questionnaire Sample

Methodology (Sample Design)

• Sampling Method (Actual):• Non-probabilistic method known as

convenience sampling• Chosen because the researchers did

not have access to the requisite number of samples

• Sampling Method (Optimal):• Stratified sampling which is a

probabilistic sampling• Designed to have minimal variation

within itself but the variance across samples is quite significant.

• Sample Size (Ideal)• ss=Sample Size• Z = Z value (e.g. 1.96 for 95% confidence

level) • p = percentage picking a choice, expressed as

decimal (.5 used for sample size needed) • c = Maximum allowance of error between true

proportion and sample proportion (e.g., .04 = ±4)

• Z=1.96 (Since confidence level is 95%)

• p=0.5 since we do not know the sample size

• c= .05 (for +/- 5)• Sample size calculated as 384

(384.16 to be precise)Methodology (Sample Design)

• Sample Size (Adjusted)• SS= New/adjusted sample size• ss’= Unadjusted sample size• p’= population size

• ss’=384.16• p’=45000000 (45

Million viewers in India)'1'1

'

pssssSS

• Sample Size (Actual)• The Actual Sample Size was 117 as this

was the amount of data that we were able to collect

Data Analysis (Factor Analysis)

Component

1 2 3

Importance of Trailers .861

Importance of Reviews .828

Importance of Director's name .191 .651

Importance of High ratings .920

Importance of Cast .832

Importance of Soundtrack .346 .569 .112

Importance of Genre .696 -.476

Importance of Production House -.231 .633 .282Extraction Method: Principal Component Analysis.

Rotation Method: Oblimin with Kaiser Normalization.

a. Rotation converged in 9 iterations.

Pattern Matrixa

Factor

1 2Rating for Genre .842 .267Rating for friends experiences .838 -.301

Rating for soundtracks .601Rating for director .603 Rating for cast .544 .338Rating for movie reviews .740 Rating for trailers .524 .615Rating for ratings .804 Rating for production house -.165 .770

Extraction Method: Principal Axis Factoring. Rotation Method: Oblimin with Kaiser Normalization.a. Rotation converged in 6 iterations.

• Factors that decide/ are important for the success of a movie:• First Impact--- Trailers and Director• Movie elements-- cast, sound track,

genre, production house• External elements-- Reviews, Ratings

• Factors that influence the decision to watch a movie:• Direct- Genre, Friends' Experience,

Director, cast, movie reviews, ratings • Indirect- Production house,

soundtracks

Cross-Tabulation (Genre)

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 27.826a 10 .002

Likelihood Ratio 33.362 10 .000

Linear-by-Linear Association 1.200 1 .273

N of Valid Cases 117

a. 8 cells (44.4%) have expected count less than 5. The minimum expected count is .34.

Cross tabulation of the frequency of watching a movie and Genre as a factor in decision making

Chi square test for the frequency of watching a movie and Genre as a factor in decision making

• Since the Chi Square is significant (.000<.05) we can say that genre and frequency do have a relationship • Mostly for every frequency category,

people rate genre as high influencer.

• Since the Chi Square is significant (.006<.05) we can say that soundtrack and frequency are related • 56 % of people who watch movie once a week

rating it as a high influencer• 54.7% of those watching movies 2-3 times a

month rating it as a medium influencer.

Cross-Tabulation (Soundtrack)Cross tabulation of the frequency of watching a movie and Soundtrack as a factor in decision making

Chi square test for the frequency of watching a movie and Soundtrack as a factor in decision making

Chi-Square Tests




Linear-by-Linear Association .499 1 .480



• Since the Chi Square is significant (.001<.05) we can say that soundtrack and frequency are related • 53.3 % of people who watch movie once

a month and 76.7% those watching movies once every 3 months rating it as a high influencerCross-Tabulation (Cast)

Cross tabulation of the frequency of watching a movie and Cast as a factor in decision making

Chi square test for the frequency of watching a movie and Cast as a factor in decision making

Chi-Square Tests

Value Df Asymp. Sig. (2-sided)



Linear-by-Linear Association 2.081 1 .149



• Since the Chi Square is significant (.001<.05) we can say that soundtrack and frequency are related • All the people who watch a movie rating as a

high influencer

Cross-Tabulation (Reviews)Cross tabulation of the frequency of watching a movie and reviews as a factor in decision making

Chi square test for the frequency of watching a movie and Reviews as a factor in decision making

Chi-Square Tests







• Since the Chi Square is significant (.011<.05) we can say that soundtrack and frequency are related • High percentage of frequent movie watchers

rating as a high influencer

Cross-Tabulation (Trailers)Cross tabulation of the frequency of watching a movie and trailers as a factor in decision making

Chi square test for the frequency of watching a movie and Trailers as a factor in decision making

Chi-Square Tests







Recommendations

• Clients should focus their further analysis and attempts around the mentioned five parameters. Especially Genre and Reviews

• To collect POS data and then try to sort the customers according to their frequency of watching a movie• Based on the our analysis the

customers driving factor can be identified and promotional campaigns prepared accordingly

• For example, if a customer belongs to a once every 3 months category he/she should be notified once a movie starring his/her favorite actor is about to be released and a promotional offer should be made to attract his business

Genre

Almost all viewers rate as a high

influencer

Soundtrack

Once a

month and

once a week viewers rate

as high

influencers

Cast

Once a

month, 2-3 times

a month

and once every

3 month

s viewers rate

as high

influencer

Review

Rated as a high

influencer

by all movie viewe

rs

Trailers

High percentage

of freque

nt movie watch

ers rate as high

influencer

Analysis results regarding influence of all factors

Limitations

• Data Collection:• Bias

• Under-coverage bias• Remedy this by taking a larger

sample and having a better organized data collection plan

• Non-response bias• Out of all the people approached,

several of them did not or could not respond to our survey

• Data collected for very few respondents• Better output could have been

achieved

• Approach towards the problem:• More secondary research should

have been conducted• Data should have been segregated by

country of origin• Some parameters differ from country

to country (e.g Soundtrack)• Several parameters like ratings and

reviews need to be explored further for impact of originating agency etc.

Further Development

• Further Analysis of Relationships between factors• Analysis of variation in various parameters• Analysis of nuances of various individual parameters• Analysis of seasonality and life cycle of various parameters

References

• Bulygo, Z. (2013, September 6). How Netflix Uses Analytics To Select Movies, Create Content, and Make Multimillion Dollar Decisions. Retrieved February 03, 2014, from Kissmetrics: http://blog.kissmetrics.com/how-netflix-uses-analytics/

• Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems 45, 1007–1016.

• Gazley, A., Clark, G., & Sinha, A. (2011). Understanding preferences for motion pictures. Journal of Business Research 64, 854-861.

• Ghosh, P. (2013, May 03). Bollywood At 100: How Big Is India’s Mammoth Film Industry? Retrieved March 02, 2014, from International Business Times: http://www.ibtimes.com/bollywood-100-how-big-indias-mammoth-film-industry-1236299

• Karniouchina, E. V. (2011). "Impact of star and movie buzz on motion picture distribution and box office revenue". International Journal of Research in Marketing 28.1, 62-74.

• Vaibhav. (2013, June 1). Yeh Jawaani Hai Deewani: The Latest Record Breaker Of Indian Film Industry - See more at: http://onvab.com/blog/indian-films-industry-facts-movies-earnings-statistics-rankings-trends/#sthash.flcLpIWT.dpuf. Retrieved February 10, 2014, from ONVAB: http://onvab.com/blog/indian-films-industry-facts-movies-earnings-statistics-rankings-trends/

THANK YOU

Increasing Movie Viewership: A Promotional Campaign Strategy Research

Data & Analytics

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