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Business Application of Agent-Based Simulation Complex and Dynamic Interactions of Motion Picture Market. SwarmFest 2004 May 11, 2004. 이 승규 Seung-Kyu Rhee 이 원희 Wonhee Lee. Movie: The Product and the Market. Movie Is naturally a new product and

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slide1

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

SwarmFest 2004

May 11, 2004

이 승규Seung-Kyu Rhee

이 원희Wonhee Lee

movie the product and the market
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
movie the decisions
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.

Feed-forward

Feedback

the problem
The 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

?

existing research
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
challenges to abm
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.
consumer state transition model

Promotion: advertising effectiveness

Place: distribution effectiveness (number of screens)

Product: theme acceptability and audience consensus to quality

Price: irrelevant

Movie

Quality

Marketing

Strategy

Positive

Spreader

Movie

Selection

Undecided

Neutral

Spreader

Inactive

Negative

Spreader

Preview

Performance

Box Office

WoM in

Neighborhood

Modified based on

Eliashberg et al. (2000)

Competitive and composite quality evaluation:

critique, preview audience, and audience consensus to quality

Consumer State Transition Model
agent in social network

Example

Number of Neighborhood = 6

Number of contacts = 2

Number of WoM communication = 1

Today’s

Contacts

Agent

Incommunicable:

Undecided or Inactive (post-WoM)

Communicable:

positive/neutral/negative spreader

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
rich microstructure in agent model
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-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
model test using real data

?

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)
test application to market data shapes
Test Application to Market Data: Shapes

Simulation

쓰리킹즈

비치

사무라이픽션

잔다르크

반칙왕

Actual

result baseline model

Medium

Medium

Low

Medium

High

Medium

Medium

Low

Medium

High

High Quality

Medium Quality

Low Quality

Result: Baseline Model
baseline result wom depletion

Medium

Medium

Low

Medium

High

Medium

Medium

Low

Medium

High

Low Quality

High quality

High Quality

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

analysis marketing impacts
Analysis: Marketing Impacts
  • Marketing impacts positively affect the performance of good movies, and increase the total market size
analysis marketing impacts22

Unstable

Analysis: Marketing Impacts
  • Bad movie’s increased marketing impacts
    • Bad movies only take the market away from other movies
analysis marketing impacts23
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
analysis marketing signals
Analysis: Marketing Signals
  • If consumers take central marketing information more seriously (than other quality information), the market growth potential is seriously impaired
analysis wom range and intensity
Analysis: WoM Range and Intensity
  • Increasing WoM signals positively affect the performance of good movies, and increase the total market size
analysis wom consensus

WoM

consensus=1

Random

WoM

consensus=1

Random

Analysis: WoM Consensus
  • Increasing WoM consensus positively affect the performance of good movies, and increase the total market size
analysis wom impacts
Analysis: WoM Impacts
  • By the “action-based WoM” assumption, good WoM spreads widely, but bad WoM does not
analysis show duration and wom accumulation

Longer show and more WoM

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
analysis competition
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

discussion market growth
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
discussion critique debates
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 consumertaste 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
hypothesis for release
Hypothesis for Release

Competition

(Number of movies)

Market Size

+

Competition

Quality distribution

(Number of good movies)

Release

Attractiveness

Competitor’s marketing

+

Marketing signals

+

My Marketing

Audience

Characteristics

WoM range/probability/

Duration/consensus

My Quality

discussion competitive strategy
Discussion: Competitive Strategy
  • Actual competition data
discussion competitive strategy34
Discussion: Competitive Strategy
  • Proposed taxonomy of movie quality and marketing strategy

Quality

LOW

MEDIUM

HIGH

LOW

Weakest

Weak

MEDIUM

Marketing

Focused

Strong

HIGH

Strongest

discussion modeling issues
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)
discussion modeling issues36
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
final thoughts
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