Bullseyeupdated2

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Bullseyeupdated2

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

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