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.