video game industry
TRANSCRIPT
Balancing bets and losses:exploration vs. exploitation
in the Video Game Industry
Federico BertazzoniTudor CarstoiuSimone Di CarloAndrea Muttoni
The 4 Bit
Team 20
Paper Structure• Exploration vs Exploitation (Tudor)
– General overview– Application in the video industry– Specific examples (e.g. Assassin’s Creed)
• Overview Industry (Federico)– Specific focus on Publishers
• Our value added• Research motivations• Methodology• The Model• Outcomes• Conclusions• References
Behind the Human Mind
Behavioural and Brain Science
Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration.
Cohen, McClure, Yu (2007)
Exploration Vs Exploitation
• Refinement
• Choice
• Production
• Efficiency
• Selection
• Implementation
• EXECUTION
• Search
• Risk taking
• Experimentation
• Play
• Flexibility
• Discovery
• INNOVATION
AMBIDEXTERITY(Duncan ‘76, March ’91)
MASTER THE PRESENT
Profits
Value of core offering
SHORT TERM PERFORMANCE
VALUE CAPTURE
Sales and installed base
Investment in core innovation and capacity
EXPLOITATIONREUSE OF EXISTING
KNOWLEDGE
TECHNICAL SCIENCE
PRE-EMPT THE FUTURE
Investment in new competences
Business innovation
MANAGE GROWTH AND RISK
VALUE CREATION
EXPLORATIONINCREASE KNOWLEDGE BASE
CREATIVE ART
Intertiality of Competences
• Miopia of learning• Competence Trap• Core capabilities to core rigidities• Important to balance exploration/exploitation
What is the situation in the industry?
Exploration in videogame industry
New & Original
Exploitation in videogame industry
Building uponexisting success
Initial ThoughtsYour intuition?
Development costs
Marketing costs
Exploration(original title)
Exploitation(sequel/licensed)
Initial ThoughtsOur intuition:
Development costs
Marketing costs
Exploration(original title)
HIGH HIGH
Exploitation(sequel/licensed)
MED-LOW MED-LOW
RealityOften case:
Development costs
Marketing costs
Exploration(original title)
MED-LOW MED-LOW
Exploitation(sequel/licensed)
HIGH HIGH
The Video Game Industry
• $80 BILLION WORTH IN 2012
• 10,6% REAL ANNUAL GROWTH PER YEAR
• 32.000 PEOPLE EMPLOYED IN 34 STATES
• NUMBER OF FINAL USERS:
The 3 Pillars in the Supply Chain
Developers Publishers Console producers
PUBLISHERS
• TOP 10: C10• WHY? Highly representative• Their role: intermediaries at the top of the
pyramid. They achieve large economies of scale, they take care of the marketing and distribution. Same role as movie/music publishers.
• EXAMPLE: EA
The Top 10 Players (2010)1. Electronic Arts2. Activision Blizzard3. Nintendo4. Ubisoft5. Microsoft6. Take-two7. Sony8. Sega9. THQ10. Square Enix
Vertical Integration
• Some of the top publishers are also console producers and have in-house development studios. Why?– Nurture the value chain (Sony, Microsoft)– Exploit first-mover possibilities (Nintendo)– Lower transaction costs– Lower uncertainty
Time period
2003-2010
Highly representativeGives us an ex-post possibility
Very dynamic market period2005-2009 of growth compared to US GDP
Research Motivation
• How explorative is the industry?• How does the industry react to performance?• How does the industry react to external
events?
Hypothesis Overview
2 Null hypotheses:• high levels of exploitation over time have no
effect on performance.• an external event (new console launch) has no
effect on exploitation.
Methodology• Data source: mobygames.com• Exploration: new original title (Max Payne)• Exploitation: sequels or licensed titles (FIFA
2012)• Average of individual game ratings as proxy
for firm performance• Type of regression: panel data regression
Data Mining
Total titles examined: 3.212Total titles kept: 1.564
Information considered:• Reported release date• Ratings• Publisher• Original/Licensed/Sequel -> Lots of Wikipedia
Our very own innovation case
• Started by hand and did over 1000 titles.• Data was inconsistent and difficult to sort • We had two options:
– time machine OR– find a better way
The CrawlerWe united our power and developed a small software that helped us gather the data saving us hours of manual work.
Endogenous changes
Null Hypothesis: high levels of exploitation over time have no effect on performance.
First Alternative: high levels of exploitation have positive effects on performance because the risk goes down and firms are able to capture all the value
Second Alternative: high levels of exploitation have negative effects on firms’ performance because of excessive path dependence so become harder and more difficult reach novelty
Exogenous changes
Null Hypothesis: an external event (new console launch) has no effect on exploitation.
First Alternative: increases exploitation to make the same games available for the new platform.
Second Alternative: new console generations may stimulate more exploration.
THE MODEL
Y= exploration indexi= Publishert= time from 2003 to 2010α= constantx=Adverage Rating / New Console eventε= error
PUBLISHERAVERAGE RATING
EXOLORATIVE INDEX
NEW CONSOLE
TAKETWO 2003 73.1 0.16 0TAKETWO 2004 73.2 0.6 0TAKETWO 2005 70.3 0.18 1TAKETWO 2006 73.2 0.18 1TAKETWO 2007 75.4 0.14 1TAKETWO 2008 80.00 0.00 0TAKETWO 2009 81.00 0.00 0TAKETWO 2010 80.1 0.08 0SONY 2003 71.4 0.33 0SONY 2004 81.1 0.33 0SONY 2005 71.5 0.28 1SONY 2006 71.9 0.2 1SONY 2007 77.3 0.54 1SONY 2008 79.6 0.42 0SONY 2009 78.5 0.42 0SONY 2010 72.00 1.00 0
SAMPLE OF DATA USED
TABLE N°1
VARIABLES ConstantAveragerating
Exploration index
1,0064(0,004)
-0.0106(0,028)
The p-value lower than 5% says that the null hypotesis had to be rejected.
There is a negative correlation between the firms’ capacity to publish new original game and their performance.
According to our result we can suppose that publisher once achived high performances could decide to decrise the risk reducing their level of exploration (i.e. publishing “non original” game such as sequel or licensed game)
PUBLISHERAVERAGE RATING
EXPLORATION INDEX
ELETRONIC ARTS 2003 73. 0.08ELETRONIC ARTS 2004 77.8 0.00ELETRONIC ARTS 2005 75.25 0.06ELETRONIC ARTS 2006 70.00 0.04ELETRONIC ARTS 2007 71.8 0.17ELETRONIC ARTS 2008 70.8 0.23ELETRONIC ARTS 2009 72.2 0.11ELETRONIC ARTS 2010 71.55 0.11
IMPLICATION
IMPLICATIONOn the other hand we can deduce that when a firm shows bad performance it is easier that firm rise up the risk publishing new original game. This view is confirmed by Henrich Grave paper, he says that a firm performe worst than his historical average or compeditors one, it start to take more risk. One tangible example is THQ that fail in 2010.
PUBLISHERAVERAGE RATING
EXPLORATIVE INDEX
THQ t2003 68.17 0.22THQ2004 66.07 0.07THQ 2005 67.6 0.13THQ2006 66.66 0.16THQ2007 63.2 0.18THQ2008 60.46 0.23THQ 2009 69.33 0.21THQ2010 62.00 0.42
VARIABLES ConstantNew
console launched
Exploration index
0.2465 (0,000)
−0.0235 (0,534)
TABLE N°2
The p-value higher than 5% says that the null hypotesis has to be not rejected.
There is no correlation between the firms’ capacity to publish new original game and new console launch.
OUTCOMES
• 1st null hypothesis is strongly rejected: (T-stat)– Positive performance increases exploitation.
• 2nd null hypothesis is not rejected: (T-stat)– External events seem to have little effect on our
dataLimitations….. Data, ratings as proxy, ecc.
IN THE NEWS
CONCLUSIONS
• Largest bets are on exploitation: cash cows• Path dependence and Value Network Trap:
good performance drives exploitation.• Found support for Henrich Greve’s
performance feedback: negative performance increases exploration.