This document discusses a dataset on movies extracted from IMDB, Rotten Tomatoes, and The Numbers. It contains budget, sales, and critic review data for movies released between 1990-2015. Regression analysis was conducted to predict opening weekend sales and total sales based on factors like genre, production company, ratings. Sentiment analysis surprisingly found positive sentiment correlated with lower sales but higher profits. The document recommends adventure films over action for budgets over $100M and provides "recipes for success" based on genres, directors, actors for different budget levels to increase profits. It also discusses a big data strategy for Warner Brothers considering data costs, analytics tools, and developing strategies based on analysis.

1. Rohit Reddy Gottam
Anas Abdul Quader Bin
Maura Bosbyshell
Kelly Trindel

2. About the Data
• Our current dataset was extracted from IMDB.com,
rottentomatoes.com and the-numbers.com
– Movies release between 1990-2015
– Wherever available, preference is given to IMDB data
• From rottentomatoes.com: critic reviews
– IMDB user ratings not so predictive of sales
• From the-numbers.com: supplemental budget and US
total sales figures
– 233 additional budget estimates
– 10% more total U.S. sales figures
• Budget, sales and profits were adjusted using the
Consumer Price Index from BLS
– Measure of average change over time in prices paid for typical
goods and services
– Puts everything in 2015 dollars

3. Predicting Sales
Opening Weekend Total
Screens Opening
Weekend
.4426* .3541*
Rotten Tomatoes Critics
Ratings
.1499* .2137*
Genre .1375* .1263*
Production Company .0963* .1043*
MPAA Rating .0799 .0821*
Notable Actor .0303 .0417
Notable Director .0303* .0562*
Full Model .5289* .5055*
* p < .05

4. Surprising Results: Sentiment
• It is surprising that positive sentiment is
negatively related to sales (r = -0.11719, p
<.0001) and budget (r = -.12621, p <.0001) yet
it is positively related to profit (r = .06251, p =
.0003)
• However, when we look more closely…

5. +Sentiment
Total
Sales
+Sentiment
Profit
+Sentiment
Budget

6. Surprising Results: Genre
Non-
Action
Film
Action
Film
Mean Profit
Non-
Adventure
Film
Adventure
Film
• When all
observations are
included, action
and adventure
films bring in
higher sales and
higher profits
• However, for
films budgeted
between $100
million and $200
million:

7. Surprising Results: Genre
Non-
Action
Film
Action
Film
Mean Profit
Non-
Adventure
Film
Adventure
Film
• And for films
budgeted at $200
million or more:
• Take Home Point:
Make an
adventure film if
the budget is $100
million or higher,
not necessarily an
action film

8. And whatever the budget…
• Just say ‘no’ to Woody Allen
No Woody Woody
Total Sales Profit
But say ‘yes’ to Spielberg, if the
budget allows for that…

9. Recipe for Success
• For $100 Million Spend:
– Genre: Adventure (r2 = .0486*);
– Director: Steven Spielberg (r2 = .0361*)
• Our top notable director appearing in movies budgeted in
this range whose presence best predicts profits
– Actors: Tom Hanks (r2 = .0108*); Tom Cruise (r2 =
.0050)
• Top notable actors appearing in movies budgeted in this
range whose presence best predict profits
– Storyline: Fantasy/futuristic film, something that
takes the audience to another time and another place
• Examples: Life of Pi, A.I. Artificial Intelligence

10. Recipe for Success
• For $200 Million Spend:
– Genre: Adventure (r2 = .0382*);
– Directors: Sam Raimi (r2 =.0123); Michal Bay (r2 = .0097)
• Top notable directors appearing in movies budgeted in this range
whose presence best predict profits
– Actors: Robert Downey Jr (r2 = .0486*); Johnny Depp (r2 =
.0258*)
• Top notable actors appearing in movies budgeted in this range
whose presence best predict profits
– Storyline: Hero movie, set in another time in a charming,
far off place
• Example: Alice in Wonderland; The Hobbit

11. We Predict Increased Profits based on
our Recommendations:
$100 Million Budget $200 Million Budget
Recommendation Increased Profit Recommendation Increased Profit
Steven Spielberg $334,533,502.00 Robert Downey Jr. $666,677,417.00
Tom Hanks $215,079,434.00 Johnny Depp $369,329,588.00
Adventure $123,714,124.00 Sam Raimi $341,772,104.00
Tom Cruise $71,871,367.00 Michael Bay $274,495,089.00
Adventure $149,611,939.00

12. Business Analytics Summary: Warner
Brothers
Five Forces Strength of
Threat
Warner
Brothers
Competition **** +
New Entrants * +
Suppliers ** neutral/+
Substitutes **** neutral
Customers *** neutral/+
S Recognition/credibility in in wide range of genres
Vertical and horizontal integration
Longstanding history of success
W Few niche capabilities, opportunities
O Continue to broadly appeal to new generations, changing demographics
T New distribution models, slowing industry ticket sales

13. Big Data Strategy
Analytics: Business Value Drivers
• Develop “recipes” for success that coincide with WB strengths/artistic vision,
budgeting process, and current distribution channels, based on factors leading to
ticket sales, in terms of:
– Timing of release, plus other variables, and combinations of variables
– Number and location of release theatres, and demographics of theatre
locations
– Insight into consumer-generated “buzz” leading to strong sales
– Changes in consumer preferences
Costs
• Data collection – online repositories, social media platforms, census data (R
and Java)
$90,000/yr for developer/analyst
• Storage - HAAS - Virtualized cloud implementation of Hadoop cluster (HDFS), with
Map Reduce
Up to $15,000 per month, less to start
• Analytics – multiple regression analysis, crosstabs, and visualizations (SAS, Excel,
Tableau)
– External data science expertise, approx. $100,000/yr (50 hrs x 5 people x
$200/hr, semiannually)
– Tableau Server - $1,000 per user SAS Server - $7,000 per user

14. Rohit Reddy Gottam
Anas Abdul Quader Bin
Maura Bosbyshell
Kelly Trindel

15. What’s so special about
Team Weird Science?
• We liked some other approaches we saw, so we
borrowed them:
– Teams #4 and #7 included critics review data from
Rotten Tomatoes
• The Rotten Tomatoes Critics Ratings variable in our first
regression was data we brought in from Group 4. 70% match
rate
• We regressed Team 4’s sentiment scores on our sales and
profit figures and found a pattern similar to our own.
– We looked at release day of the week and holiday
release variables from team # 5 but these were not
predictive of sales or profit data for us

16. What’s so special about
Team Weird Science?
• We interpreted the relationship between
sentiment and sales figures with greater insight
• Our regression analysis results are strong:
– We reported variance accounted for each individual
predictor in our models
– We interpreted our parameter estimates in the
‘recipe’ regressions in terms of real change in profits
• We used a strategic planning process to
determine the desired outcomes of the analytics
initiative

17. What’s so special about
Team Weird Science?
• Our work has been much improved since Part
2:
– Variance accounted for in our overall regression
analysis is more than doubled
• Including the number of screens variable
• Including the rotten tomatoes critics ratings
– Better data-driven analysis to make
recommendations at the $100 million and $200
million spend