Business Application of Agent-Based Simulation Complex and Dynamic Interactions of Motion Picture Market

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Business Application of Agent-Based Simulation
Complex and Dynamic Interactions of Motion
Picture Market

• SwarmFest 2004
• May 11, 2004

? ?? Seung-Kyu Rhee ? ?? Wonhee Lee
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Movie The Product and the 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

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Movie The Decisions
Focus of this paper

• 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.

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The Problem

• How is the market going to respond to
• Various suppliers 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

?
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Existing 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

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A Sample of Existing Models
Research 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
Forecasting Queuing 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
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Issues Covered in Literature
Competition Movie characteristics Marketing (advertising, distribution) Critique review Quality orWOM 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) ? ? ?
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Challenges 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.

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Consumer State Transition Model
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Agent in Social Network

• ABM v. EBM
• Daily update of movie-going probability for each
  agent
• Eliashberg et al. (2000) used aggregated market
  transition equations

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Rich Microstructure in Agent 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-Comp
  etition
• 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

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The Simulation Process
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General Parameters
Reference Range Baseline Model
Number of movies De Vany Lee (2001) 210 movies 5 movies
Quality of film Korean movie industry High, medium, low quality High 1, medium 3, low 1
Number of audience N/A 10,00020,000 10,000 persons
Number of preview audience Korean movie industry 1 20 (0.00010.002) 10 persons
Preview period Korean movie industry 121 days 7 days
Marketing impacts Eliashberg et al. (2000) 0.0 1.0 0.5
Critique preference N/A positive, negative, neutral Dependent on critique consistency
Critique consistency N/A 0.0 1.0 1.0
movie-going probability Korean movie industry 0.010.05 0.02
Maximum number of movie selection N/A 15 movies 1 movie
WoM preference Mahajan et al. (1984) positive, negative, neutral Dependent 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
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Signal Parameters
  Range Baseline Model Characteristics
Marketing signals 17 times in pre-release period 2 times Performance independent centralized information
Critique signals 17 times in pre-release period 0.2 times Performance independent centralized information
WoM signals Depend on WoM structure Depend on WoM structure Performance dependent (box office and showing period) decentralized information
Market feedback signals 13 times per week (depends on release period) Once a week Performance dependent (showing period) centralized information
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Model Test Using Real Data

• 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)

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Test Data Set Actual Movies in Korean Market
Movie title Opening day Critique 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
Jan. (Set 1) A Happy Funeral Parlor (Korea) 1. 8 L M 0.3 6,725 111,837
Jan. (Set 1) Fly me to Polaris (Hong Kong) 1. 15 L M 0.3 10,120 202,840
Jan. (Set 1) The Bone Collector (USA) 1. 1 L M 0.4 13,372 212,564
Jan. (Set 1) Stuart Little (USA-Germany) 1. 8 L M 0.4 16,331 392,933
Jan. (Set 1) Happy End (Korea) 1. 1 M L 0.4 13,690 132,029
Jan. (Set 1) 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
Feb. (Set 2) Samurai Fiction (Japan) 2. 19 M M 0.4 14,232 224,256
Feb. (Set 2) The Beach (USA) 2. 3 M M 0.3 14,231 187,460
Feb. (Set 2) The Messenger The Story of Joan of Arc (France) 2. 19 M M 0.4 13,084 220,986
Feb. (Set 2) 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
Early July (Set 3) Gone in 60 Seconds (USA) 7. 1 L M 0.5 21,272 348,710
Early July (Set 3) 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
Late July (Set 4) The Perfect Storm (USA) 7. 29 M M 0.9 35,184 508,913
Late July (Set 4) The Patriot (USA) 7. 22 L M 0.4 18,229 149,415
Late July (Set 4) Nightmare (Korea) 7. 29 L M 0.4 12,801 279,174
Late July (Set 4) Ring 2 (Japan) 7. 29 M L 0.2 7,164 106,652
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Test Application to Market Data Shapes
Simulation
???? ?? ?????? ???? ???
Actual
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Test Application to Market Data Fitness
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Result Baseline Model
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Baseline Result WoM Depletion

• WoM intensity gets weaker along the show duration
• Initial audience size and signal accuracy (viewer
  consensus) intervene

Signal accuracy 0.7
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Analysis Marketing Impacts

• Marketing impacts positively affect the
  performance of good movies, and increase the
  total market size

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Analysis Marketing Impacts

• Bad movies increased marketing impacts
• Bad movies only take the market away from other
  movies

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Analysis Marketing Impacts

• 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 spreads

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Analysis Marketing Signals

• If consumers take central marketing information
  more seriously (than other quality information),
  the market growth potential is seriously impaired

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Analysis WoM Range and Intensity

• Increasing WoM signals positively affect the
  performance of good movies, and increase the
  total market size

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Analysis WoM Consensus

• Increasing WoM consensus positively affect the
  performance of good movies, and increase the
  total market size

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Analysis WoM Impacts

• By the action-based WoM assumption, good WoM
  spreads widely, but bad WoM does not

Movie High Positive WoM Listener No WoM Negative WoM Listener
High quality movie Number of audience 3539 (35) 5834 (58) 609 (6)
High quality movie Number of movie-goer 1,275 (46) 1,436 (52) 42 (2)
High quality movie Movie-goer ratio 41 22 7
Low quality movie Number of listener 497 (5) 7393 (74) 2094 (21)
Low quality movie Number of movie-goer 71 (7) 954 (92) 15 (1)
Low quality movie Movie-goer ratio 14 13 1
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Analysis Show Duration and WoM Accumulation

• The accumulated impact of WoM shows the inverted
  U shape, for the movie-going rate per WoM
  (probability) decreases after the peak

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Analysis Competition

• 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

Average number of viewers per movie according to
competition scenarios
The number of movies Good movies Ordinary movies Bad movies Evenly distributed 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
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Discussion Market Growth

• 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

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Discussion Critique Debates

• 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

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Hypothesis for Release
Competition (Number of movies)

Market Size

Competition
Quality distribution (Number of good movies)

Release Attractiveness
Competitors marketing


Marketing signals

My Marketing
Audience Characteristics
WoM range/probability/ Duration/consensus
My Quality
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Discussion Competitive Strategy

• Actual competition data

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Discussion Competitive Strategy

• Proposed taxonomy of movie quality and marketing
  strategy

Quality
LOW
MEDIUM
HIGH
Weakest
LOW
Weak
MEDIUM
Marketing
Focused
Strong
Strongest
HIGH
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Discussion Modeling Issues

• 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)

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Discussion Modeling Issues

• 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

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Final 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