1 business application of agent-based simulation complex and dynamic interactions of motion picture...
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Business Application Business Application of Agent-Based of Agent-Based
Simulation Simulation Complex and Dynamic Interactions of Motion Picture Complex and Dynamic Interactions of Motion Picture
MarketMarket
SwarmFest 2004SwarmFest 2004May 11, 2004May 11, 2004
이 승규이 승규 Seung-Kyu RheeSeung-Kyu Rhee이 원희이 원희 Wonhee LeeWonhee Lee
22Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Movie: The Product and Movie: The Product and the Marketthe Market Movie
Is naturally a new product and Has short life-cycle from one week to several months
The Product With huge initial investment and High uncertainty of the market performance Highly risky
business The Market
Constituents of the movie supply chain From a writer with an idea To theater managers with screens to allocate and Everybody in between
Consumers in complex social network Local and central information Preference and constraints
Competing movies and substitutes
33Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Focus of this paper
Feedback
Movie: The DecisionsMovie: The Decisions Given a movie to sell
A distributor has to decide How much marketing budget to spend, When to release it, How many screens to secure, etc.
The decisions should be based on The projected market performance, Which, in turn, would be influenced by the
decisions themselves and Many other uncontrollable factors, notably the early
performance of the movie itself.
Feed-forward
44Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
The ProblemThe Problem How is the market going to respond to
Various supplier’s decision alternatives under Various market conditions with competing movies and The communication dynamics about the movie quality
Adaptive reactions of competitors and myself What-if analysis is critical, but It is only possible with detailed knowledge of the dynamic
process
?
55Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Existing ResearchExisting Research Ranges from simple statistical forecasting
models to a complex dynamic Markov chain model with behavioral parameter estimation
Some agent-based models have been proposed to describe the near-chaotic market behavior in terms of market share change
To our knowledge, no existing model is comprehensive enough to be useful for decision makers in motion picture industry
66Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
A Sample of Existing A Sample of Existing ModelsModelsResearch Objective
Method Authors Characteristics Limits
Decision support
system and Forecasting
Interactive Markov Chain
Eliashberg et al. (2000)
•Forecasting before the release by estimating parameters with audience survey
•Empirical test applied to real cases
•Competition•Dynamics
ForecastingQueuing model
Sawhney & Eliashberg
(1996)
•Estimating function and parameters
•Competition•Dynamics•Lack of explanatory variables
Understanding system behavior
Agent-based modeling
De Vany & Lee (2001)
•Reliability of product quality and market performance feedback by Information cascading perspective
•Marketing variables
•Low reliability of results
•Too simple decision rule
Finding major
variables
Empirical study
Bagella & Becchetti
(1999), De Vany & Walls
(1999)
•Finding important variables
•Comparison of coefficient between variables
•Competition•Dynamics
77Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Issues Covered in Issues Covered in LiteratureLiterature
Competition
Movie characteris
tics
Marketing
(advertising,
distribution)
Critique
review
Quality or
WOM
Market performance feedback
Eliashberg et al. (2000) ○ ○ ○
Jedidi et al., (1998) ○ ○ ○
Lampel et al., (2000) ○
Zufryden (1996) ○ ○ ○
Mahajan et al. (1984) ○ ○ ○
Prag and Casavant (1994)
○ ○ ○
Linton & Petrovich (1988)
○
Litman & Kohl (1989) ○ ○ ○ ○
Sochay (1994) ○ ○ ○
Lampel and Shamsie (2000)
○ ○ ○
De Vany and Lee (2001) ○ ○ ○
88Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Challenges to ABMChallenges to ABM KISS? Reality?
In agent-based simulation community, there is a tendency to prefer simple models
From practitioners’ viewpoint, however, it does not help much to confirm the fact that the market is too complex and anything is possible (e.g., De Vany and Lee, 2001)
Big question: How real is real enough? In this paper we expand the scope of the movie
market model by including diverse sources of movie quality information and competition effect.
99Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Consumer State Consumer State Transition ModelTransition Model
Promotion: advertising effectivenessPlace: distribution effectiveness (number of screens)Product: theme acceptability and audience consensus to qualityPrice: irrelevant
PositiveSpreader
NegativeSpreader
Undecided Inactive
MarketingStrategy
MovieSelection
MovieQuality
PreviewPerformance
Competitive and composite quality evaluation: critique, preview audience, and audience consensus to quality
Box Office
WoM in Neighborhood
NeutralSpreader
Modified based onEliashberg et al. (2000)
1010Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Number of Neighborhood = 6Number of contacts = 2Number of WoM communication = 1
Incommunicable: Undecided or Inactive (post-WoM)
Communicable: positive/neutral/negative spreader
AgentToday’sContacts
ExampleExample
Agent in Social Agent in Social NetworkNetwork ABM v. EBM
Daily update of movie-going probability for each agent
Eliashberg et al. (2000) used aggregated market transition equations
1111Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Rich Microstructure in Rich Microstructure in Agent ModelAgent Model Modeling objective
Heuristic approach for better understanding of the market Gross and Strand (2000): Predictive, Explanatory, and Heuristic
Initial exploration of diverse variables and parameters Sensitivity analyses under diverse scenarios Part of bigger model: Production-Distribution-Competition Toward a commercially useful “Decision Support System”
Choice of rich microstructure The most salient characteristic of “culture products”
Experience goods: performance seriously affected by social interaction and human intervention
Model saturation can be determined by diverse experiments and sensitivity analyses
1212Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
The Simulation The Simulation ProcessProcess
Pre-release Period Release PeriodSet-up
•Number of movies•Quality of movies •Marketing effectiveness•Frequency of marketing•Audience of preview
DIstributor
•Number of neighbors•Number of contacts•Frequency of WoM•Duration of WoM•Consensus to quality
Audience
•Preview a movie
DIstributor
•Review of critique
Critique
•WoM of preview audience
Audience
•Market performance feedback (Box office report)
Media
•WoM update•Movie- going decision•Spreading WoM
Audience
•Releasing a movie•Ending a movie(No audience)
DIstributor
•Frequency of critique review
•Consensus to quality
Critique
•Frequency of market performance feedback
Media
1313Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
General ParametersGeneral ParametersReference Range Baseline Model
Number of movies De Vany & Lee (2001) 2~10 movies 5 movies
Quality of film Korean movie industry High, medium, low qualityHigh: 1, medium: 3, low:
1
Number of audience N/A 10,000~20,000 10,000 persons
Number of preview audience
Korean movie industry 1 – 20 (0.0001~0.002%) 10 persons
Preview period Korean movie industry 1~21 days 7 days
Marketing impacts Eliashberg et al. (2000) 0.0 – 1.0 0.5
Critique preferenceN/A
positive, negative, neutralDependent on critique
consistency
Critique consistency 0.0 – 1.0 1.0
movie-going probability
Korean movie industry 0.01~0.05 0.02
Maximum number of movie selection
N/A 1~5 movies 1 movie
WoM preference Mahajan et al. (1984) positive, negative, neutralDependent on WOM
consistency
WoM consistency De Vany & Lee (2001) 0.0 – 1.0 0.7
WoM: neighborhood N/A 0 – 10 persons 10 persons
WoM duration Eliashberg et al. (2000) 0-32 days 21 days
WoM frequency Eliashberg et al. (2000) 1 – 10 per week 2 per week
1414Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Signal ParametersSignal Parameters
Range Baseline Model
Characteristics
Marketing signals
1~7 times in pre-release
period2 times Performance
independent centralized informationCritique
signals
1~7 times in pre-release
period0.2 times
WoM signals Depend on WoM structure
Performance dependent
(box office and showing period) decentralized
information
Market feedback signals
1~3 times per week
(depends on release period)
Once a week
Performance dependent (showing
period)centralized information
1515Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Model Test Using Real Model Test Using Real DataData Test movies
Two week brackets in January, February, and July of 2000
Movies with more than 100,000 viewers Test with opening market share and final market
share Chi-square test (Chung and Cox, 1994)2
1
(Actual Predicted )
Predicted
N
i ii
i
Q
21 ( 1)N ?
1616Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Movie titleOpening
dayCritique quality
Audience quality
Marketing
impacts
Opening box office
Total box office
Jan. (Set 1)
Peppermint Candy (Korea) 1. 1 H H 0.2 6,206 290,276
A Happy Funeral Parlor (Korea) 1. 8 L M 0.3 6,725 111,837
Fly me to Polaris (Hong Kong) 1. 15 L M 0.3 10,120 202,840
The Bone Collector (USA) 1. 1 L M 0.4 13,372 212,564
Stuart Little (USA-Germany) 1. 8 L M 0.4 16,331 392,933
Happy End (Korea) 1. 1 M L 0.4 13,690 132,029
Lies (Korea) 1. 11 M L 0.8 19,035 307,702
Feb. (Set 2)
The Foul King (Korea) 2. 4 M H 0.4 22,741 787,412
Samurai Fiction (Japan) 2. 19 M M 0.4 14,232 224,256
The Beach (USA) 2. 3 M M 0.3 14,231 187,460
The Messenger: The Story of Joan of Arc (France)
2. 19 M M 0.4 13,084 220,986
Three Kings (USA) 2. 12 L L 0.2 10,060 134,376
Early July
(Set 3)
Dinosaur (USA) 7. 15 M M 0.8 27,859 554,169
Gone in 60 Seconds (USA) 7. 1 L M 0.5 21,272 348,710
Bichunmoo (Korea) 7. 1 L L 0.8 23,835 631,913
Late July
(Set 4)
Bayside Shakedown (Japan) 7. 22 M H 0.3 13,496 234,155
The Perfect Storm (USA) 7. 29 M M 0.9 35,184 508,913
The Patriot (USA) 7. 22 L M 0.4 18,229 149,415
Nightmare (Korea) 7. 29 L M 0.4 12,801 279,174
Ring 2 (Japan) 7. 29 M L 0.2 7,164 106,652
Test Data Set: Actual Test Data Set: Actual Movies in Korean Movies in Korean MarketMarket
1717Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
0
5000
10000
15000
20000
25000
30000
1 9
17
25
33
41
49
57
65
73
81
89
반칙왕비치쓰리킹즈잔다르크사무라이픽션
쓰리킹즈비치
사무라이픽션잔다르크
반칙왕
Test Application to Test Application to Market Data: ShapesMarket Data: Shapes
Actual
Simulation
1818Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Test Application to Test Application to Market Data: FitnessMarket Data: Fitness
1919Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Result: Baseline Result: Baseline ModelModel
High Quality
Medium Quality
Low Quality
MediumMedium
LowMedium
High
MediumMedium
LowMedium
High
2020Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
High quality
MediumMedium
LowMedium
High
MediumMedium
LowMedium
High
High Quality
Low Quality
Baseline Result: WoM Baseline Result: WoM DepletionDepletion WoM intensity gets weaker along the show
duration Initial audience size and signal accuracy (viewer
consensus) intervene
Signal accuracy = 0.7
2121Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Analysis: Marketing Analysis: Marketing ImpactsImpacts
Total Market size
72007400760078008000820084008600880090009200
Mkt
g=0.
1
Mkt
g=0.
2
Mkt
g=0.
3
Mkt
g=0.
4
Mkt
g=0.
5
Mkt
g=0.
6
Mkt
g=0.
7
Mkt
g=0.
8
Mkt
g=0.
9
Mkt
g=1.
0
0500
10001500200025003000350040004500
Mkt
g=0.
1
Mkt
g=0.
3
Mkt
g=0.
5
Mkt
g=0.
7
Mkt
g=0.
9
High QualityMedium QualityMedium QualityMedium QualityLow Quality
Marketing impacts positively affect the performance of good movies, and increase the total market size
2222Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Analysis: Marketing Analysis: Marketing ImpactsImpacts Bad movie’s increased marketing impacts
Bad movies only take the market away from other movies
Total Market Size
85508600865087008750880088508900
Mkt
g=0.
1
Mkt
g=0.
2
Mkt
g=0.
3
Mkt
g=0.
4
Mkt
g=0.
5
Mkt
g=0.
6
Mkt
g=0.
7
Mkt
g=0.
8
Mkt
g=0.
9
Mkt
g=1.
0
Unstable0
500
1000
1500
2000
2500
3000
3500
4000
Mkt
g=0.
1
Mkt
g=0.
3
Mkt
g=0.
5
Mkt
g=0.
7
Mkt
g=0.
9
High QualityMedium QualityMedium QualityMedium QualityLow Quality
2323Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Analysis: Marketing Analysis: Marketing ImpactsImpacts Decreasing returns to scale for the marketing
impact increase (inducing initial viewer increase) are confirmed for both good and bad movies with some irregularities
But if you have a good movie, then excessive marketing do not help much due to market information spreadsIncrease in the box office of high quality movie
0100200300400500600700800900
Mkt
g=0.
1
Mkt
g=0.
2
Mkt
g=0.
3
Mkt
g=0.
4
Mkt
g=0.
5
Mkt
g=0.
6
Mkt
g=0.
7
Mkt
g=0.
8
Mkt
g=0.
9
Increase in the box office of low quality movie
050
100150200250300350
Mkt
g=0.
1
Mkt
g=0.
2
Mkt
g=0.
3
Mkt
g=0.
4
Mkt
g=0.
5
Mkt
g=0.
6
Mkt
g=0.
7
Mkt
g=0.
8
Mkt
g=0.
9
2424Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Analysis: Marketing Analysis: Marketing SignalsSignals If consumers take central marketing
information more seriously (than other quality information), the market growth potential is seriously impaired
0500
1000150020002500300035004000
Mkt
g sig
nal=
1
Mkt
g sig
nal=
3
Mkt
g sig
nal=
5
Mkt
g sig
nal=
7
Mkt
g sig
nal=
9
Th
e n
um
ber
of
au
die
nce
High qualityMedium qualityMedium qualityMedium qualityLow quality
2525Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
0500
100015002000250030003500400045005000
Wom
Rang
e=0
Wom
Rang
e=2
Wom
Rang
e=4
Wom
Rang
e=6
Wom
Rang
e=8
High QualityMedium QualityMedium QualityMedium QualityLow Quality Total Market Size
82008300840085008600870088008900900091009200
Wom
Rang
e=0
Wom
Rang
e=1
Wom
Rang
e=2
Wom
Rang
e=3
Wom
Rang
e=4
Wom
Rang
e=5
Wom
Rang
e=6
Wom
Rang
e=7
Wom
Rang
e=8
Wom
Rang
e=9
Analysis: WoM Range Analysis: WoM Range and Intensityand Intensity Increasing WoM signals positively affect the
performance of good movies, and increase the total market size
2626Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Total market size
7000
7500
8000
8500
9000
9500
1 2 3 4 5 6 7 8
Total market size
RandomWoM
consensus=1
RandomWoM
consensus=1
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1 2 3 4 5 6 7 8
High QualityMedium QualityMedium QualityMedium QualityLow Quality
Analysis: WoM Analysis: WoM ConsensusConsensus Increasing WoM consensus positively affect
the performance of good movies, and increase the total market size
2727Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Analysis: WoM Analysis: WoM ImpactsImpacts By the “action-based WoM” assumption, good
WoM spreads widely, but bad WoM does not
Movie High Positive
WoM Listener
No WoMNegative
WoM Listener
High quality movie
Number of audience 3539 (35%) 5834 (58%) 609 (6%)
Number of movie-goer 1,275 (46%) 1,436 (52%) 42 (2%)
Movie-goer ratio 41% 22% 7%
Low quality movie
Number of listener 497 (5%) 7393 (74%) 2094 (21%)
Number of movie-goer 71 (7%) 954 (92%) 15 (1%)
Movie-goer ratio 14% 13% 1%
2828Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Analysis: Show Analysis: Show Duration and WoM Duration and WoM AccumulationAccumulation The accumulated impact of WoM shows the
“inverted U shape,” for the movie-going rate per WoM (probability) decreases after the peak
Good Movie-WoM & Movie going behavior
0
200
400
600
800
1000
1200
1400
1600
1800
0이상 2이상 4이상 6이상 8이상10이상
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Positive WoMListener
Movie goer
Movie going ratio
Longer show and more WoM
2929Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Analysis: CompetitionAnalysis: Competition
The number of movies
Good movies
Ordinary movies
Bad movies
Evenly distribute
d
Good Movie when evenly distributed
3 movies 3,027 2,668 1,977 2,956 4,924
6 movies 1,492 1,324 1,084 1,446 2,141
9 movies 991 913 794 956 1,328
Average number of viewers per movie according to competition scenarios
Movie mix in the market affects the total market size
Good and bad mix is better than all-average movies If you have a good movie, then release timing
strategy is critical
3030Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Discussion: Market Discussion: Market GrowthGrowth Effects of demand growth
Results from increased population (width) and increased frequency (depth) scenarios show that diminishing returns to scale
The width shows bigger effect in simultaneous release competition
Effects of movie supply and mix Total market size is positively related to
The number, quality and right mix of movies Marketing impacts and communication effects
interact in different fashion according to the movie quality and mix
3131Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Discussion: Critique Discussion: Critique DebatesDebates Debates
Critique influence (Handel, 1950; Litman, 1983) Critique influence timing: influencer vs. predictor
(Burzynski and Bayer, 1977; Eliashberg and Shugan, 1997)
Critique and consumer taste correlation and independence (Wanderer, 1970; Eliashberg and Shugan, 1997)
The model can incorporate the different assumptions and their consequences
What if critiques are ‘influencer,’ ‘predictor’ or both? It can be shown that the same results can be obtained
by changing parameters of initial marketing impact and WoM intensity
3232Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Hypothesis for Hypothesis for ReleaseReleaseCompetition
(Number of movies)
Market Size
Quality distribution (Number of good movies)
Competitor’s marketing
Marketing signals
WoM range/probability/Duration/consensus
Release Attractiveness
My Marketing
My Quality
—
+
—
—
+
+
Competition
AudienceCharacteristics
3333Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Discussion: Discussion: Competitive StrategyCompetitive Strategy Actual competition data
5 4
2, 3 11
LOW MEDIUM HIGH
LOW
MEDIUM
HIGH
Quality
Marketing
Feb. 2000Feb. 2000
5, 7 3
6 1, 4
2
LOW MEDIUM HIGH
LOW
MEDIUM
HIGH
Quality
Marketing
Jan. 2000Jan. 2000
3
1 22
LOW MEDIUM HIGH
LOW
MEDIUM
HIGH
Quality
Marketing
Early July. 2000Early July. 2000
5 3
2, 4
11
LOW MEDIUM HIGH
LOW
MEDIUM
HIGH
Quality
Marketing
Late July. 2000Late July. 2000
3434Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Discussion: Discussion: Competitive StrategyCompetitive Strategy Proposed taxonomy of movie quality and
marketing strategy
Weakest
Strongest
LOW MEDIUM HIGH
LOW
MEDIUM
HIGH
Quality
Marketing
Weak
Strong
Focused
3535Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Discussion: Modeling Discussion: Modeling IssuesIssues The model discussed in this paper is one focusing on the
complex consumer dynamics The concept of model “Saturation”
When applying agent-based simulation to a real and complex decision situation, it is more important that every additional variable and agent should be justified by increased insights and relevance
Heuristic approach Simplified analysis for central v. local communications
On consumer choice More empirical evidence is necessary for the model
improvement Acceleration phenomenon (e.g., The Passion of Christ,
Taegukgi in Korea)
3636Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Discussion: Modeling Discussion: Modeling IssuesIssues Model extension directions for practitioners
Market segmentation and competition Better consumer choice theory is necessary Overlapping release strategy
Theater objects Constraints and theater screen mix strategy
Producer objects Positive and negative feedback of innovation and imitation Resource-based theory of accumulating intangible assets
Combining the models for practical applications
3737Rhee and Lee: ABS for Movie IndustryRhee and Lee: ABS for Movie Industry
Final ThoughtsFinal Thoughts ABM as a research method
Naturally lead researchers to think more about the “dynamics” and “adaptive behaviors” than traditionally thought to be adequate or acceptable
Implications We need more theoretical models, and Empirical data based on new models Especially in practical application purposes