Bullseyeupdated2
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  • 1. Forecasting Page Views & Ad Impressions for Bullseye Ad Campaigns David Feng Statistical Analyst Intern GEL, Trax
  • 2. What is Bullseye?
    • Cross-game advertising
      • A client’s ad campaign runs exclusively on a selected game space for a set amount of time.
    • Example
      • I want to advertise for a new football game I created.
      • I feel that Madden players would be interested in my game.
      • I buy a Bullseye campaign on Madden NFL 09, since I am targetting Madden players.
  • 3. Existing Bullseye Procedures
    • Sales team has two options
      • Propose a predicted number of ad impressions to the client (but how much?)
      • Charge a flat rate on the Bullseye campaign (Halo 3 = Balls of Fury)
    • Problems
      • Under-delivery: must compensate client in some other way – make goods
      • Over-delivery: lost opportunity for revenue
      • Flat-rate pricing: lost opportunity for revenue on AAA games
  • 4. Bullseye Q1’07 Q2’07 Q3’07 Q4’07 Q1’08 Q2’08 eCPM (censored) % of GS Revenue 9% 5% 4% 4% 3% 2%
  • 5. The Bullseye Predictive Model
    • Objective: To provide a more objective means for predicting the number of advertising impressions in a game space.
    • 4 phases
      • Data-collection and processing
      • Predict number of page views for a game space 30 days, 60 days and 90 days in advance
      • Find the relationship between page views and advertising impressions
      • Test validity of models and develop business rules
  • 6. The Bullseye Predictive Model
    • Objective: To provide a more objective means for predicting the number of advertising impressions in a game space.
    • 4 phases
      • Data-collection and processing
      • Predict number of page views for a game space 30 days, 60 days and 90 days in advance
      • Find the relationship between page views and advertising impressions
      • Test validity of models and develop business rules
  • 7. I. Data Collection
    • Gathered data on all games released from 1/1/06 to 5/31/08 using Trax
      • Data pulled in Mon-Sun increments
    • 3782 games, ~2700-2800 after removing irrelevant games
    • Computed lifecycle data for all games on the weekly level
  • 8. II: Predicting Page Views
    • Predict page views because it should be the strongest determinant of ad impressions
    • Determinants
      • From Trax
        • Users
        • Page Views
        • Searches
        • Videos Served
        • Total Users Tracking
        • New Users Tracking
        • Price Checks
        • Forum Activity
    • Pre-existing Awareness
      • IP Type (Original, Sequel, Licensed)
      • Milestones (News, Previews, Videos)
    • Quality of Game
      • Average Review Score
      • (as a stand-in for preview score)
    • Gamespot Traffic
      • Quarter game is released
  • 9. Heteroskedasticity: Big Games = Big Errors
  • 10. Constructing the Forecast Interval
    • Two sources of uncertainty
      • Standard Error of Regression (SER)
      • Coefficient Uncertainty
    • SER
      • Future is uncertain (see figure)
      • Reduce through stratification (e.g. separate models for X360 games, casual games, Q4, high PV-growth)
    • Coefficient Uncertainty
      • Uncertain coefficients, so uncertain dependent variable
      • Ignored
      • Sample size not large enough
      • Software limitation
  • 11. Solution
    • Sell Bullseye ad campaigns on at least 3 SKUs to reduce risk of underdelivery
    • Just need to reduce the SER to improve models’ goodness-of-fit
  • 12. Summary of Results – 30 Days Platform Week0 Week1 Week2 Week-1 Month1 X360 PS3 Wii PC DS PSP
  • 13. Summary of Results – 60 Days Platform Week0 Week1 Week2 Week-1 Month1 X360 PS3 Wii PC DS PSP
  • 14. Summary of Results – 90 Days Platform Week0 Week1 Week2 Week-1 Month1 X360 PS3 Wii PC DS PSP
  • 15. Testing Validity of Models
    • Use games released after 5/31/08
    • Test Interface & Final Interface
    • Interface will eventually be automated into a new tool, so that the sales rep only needs to put in the game ID, and all data will be pulled from Trax
    • A new Bullseye tool and/or an internal version of Trax could be created to ease testing and usage of the models
  • 16. Next Steps
    • Test models
    • Finalize relationship between page views and ad impressions (need 2007 Bullseye campaign data)
    • Generate models for 120-day and 180-day windows
    • Establish business rules using the results of testing
      • New rules on Bullseye ad campaigns (e.g. at least 3 SKUs)
      • Ability to revise projections at the 90-day, 60-day, 30-day windows
      • All Bullseye ad campaigns can convert to the CPM model
  • 17. Acknowledgements
    • Sara Borthwick (manager)
    • Entire Trax Team (Anne, Christian, Christie, Colina, Marisa, Matt, Maura, Shanna)
    • Product Marketing (Andrew, Nina)
    • Statisticians (Erika, Phil)
    • Excel Master (Jared)