Using Peer-to-Peer Downloading Behavior to Forecast Pre-launch Sales of Music:

1. Using Peer-to-Peer Downloading Behavior to Forecast Pre-launch Sales of Music: A Bayesian Analysis Il-Horn Hann, Joo Hee Oh Marshall School of Business University of Southern California Preliminary

2. 1. Research Motivation Want to measure and understand the behavior of online systemusers and its linkage to the business forecasts from individual-level system usage data on Peer-to-Peer network (ARES)

3. 2. Backgrounds 1. Lee, Boatwright and Kamakura (Management Science 2003) -Bayesian model for Pre-launch Sales forecasting for Billboard 200s albums • Provides Pre-launch Sales forecasts for albums in Billboard 200s • Does not provide pre-launch sales forecasts for NOT successful albums • Does not provide point-estimate of first weeks’ sales after launch 2. Wendy Moe and Peter Fader (Marketing Science 2002) -Using Advance Purchase Orders to Forecast New Product Sales • Generate Sales forecasts for music albums using advance-purchase orders • Limited results for generalization for the special type of consumers

4. 3. Research Question How can we develop an empirical model utilizing downloading behavior data from peer-to-peer network to generate pre-launch sales forecasts of music for the first-week after launch?

5. 4. Model Development Hierarchical Random-Effects Model (Preference or Quality Variable)

6. 4. Model Development Second-level Model Hierarchy

7. 4. Model Development Conjugate Prior-Distribution for Hierarchical Linear Models Model hyper-parameters Rossi & Allenby et al. (2005)

8. 4. Model Development Hyper-parameters Album characteristics (Z) Downloading Behavior (X) Estimation/Calibration set Hold-Out Sample

9. 4. Model Development Gibbs-Sampling Draw and Draw Repeat, as necessary. and Forecasting Model Point-Estimate of 1st week Sales Average # of Downloads before launch, Available # of Dates, Weekly Dummy Var.

10. 5. Hypotheses H1 The more downloaded from the P2P, the higher sales of the album. H2 The more dates available from the P2P, the higher sales of the album. H3 The more previous total # of albums artist have, the higher sales sensitivity to the downloads #. H4 Albums launched in the same rank of week have similar estimation coefficients of sales on downloads #. H5 The genre of music, the gender of artist affect positively to the sales through downloads #.

11. 6. Empirical Illustration Description of the Data • Downloads data from the Ares P2P network (April 5, 2007-July 15, 2007) • Sales for Newly Released albums in billboard’s Top 200 (May 1-July 15,2007) • Album specific characteristics • Previous total # of albums of the artist • Genre of music (Rap & Rock) • Gender of the artist (Male) Data Preprocessing • Newly Released albums on Billboard 200s weekly chart : 98 albums • Extracted Movie Soundtracks or Re-entered albums due to atypical patterns • Ends up 74 newly Released-albums on Billboard 200s • Choose 50 for Calibration set/ 24 For Hold-out Sample Calibration Panel set : 50 Cross sectional + 4 Time-series Hold-out Sample: Generate one point-estimate of first-weeks’ sales

12. 6. Empirical Illustration

13. 6. Empirical Illustration

14. 7. Results- Estimation [Figure 3] Total Estimation Results for Calibration set

15. 7. Results- Estimation [Figure 4] Weekly Estimation Results for Calibration set

16. 7. Results- Estimation [Figure 5] Individual Album Sales Estimation: High, Medium, Low -Level of Success

17. 7. Results- Estimation [Figure 5] Individual Album Sales Estimation: High, Medium, Low -Level of Success

18. 7. Results- Estimation [Figure 5] Individual Album Sales Estimation: High, Medium, Low-Level of Success

19. 8. Results- Forecasting [Figure 6] Comparison of Forecast Results using Different Measure of Downloading #

20. MAPE = 8. Results- Forecasting [Table 1] Comparison of Forecasts (MAPE) with Different Measure [Table 2] Comparison of Fit (MAPE) with Previous Studies (Lee, Boatwright, Kamakura 2003)

21. 8. Results- Forecasting [Figure 7] Point-estimate of Pre-launch Sales forecasts for the first-week

22. 8. Results- Forecasting ) on Explanatory variables [Figure 8] Sales-Coefficients ( Sales on # of Available Dates Sales on # of Average Downloads #

23. 8. Results- Forecasting Sales on 2nd week dummy variable Sales on 3rd week dummy variable

24. 8. Results- Forecasting ) on Album characteristics [Figure 9] Downloads-Coefficient ( Total # of previous albums Rap (Genre) Rock (Genre) Male (Gender)

25. 9. Future Work Data • Small sample-size for the # of Newly-Released albums • Lack of New-albums which is NOT on Billboard 200s’ chart • => New release data purchased • Does ARES network large enough to represent downloads behavior? • => Better prediction by using additional P2P network data Methods • Needs enhancement from simple standardized form of model • Need enhancement in the prior-distribution for using Barnard et al. (2000) Model • Does weekly cyclic pattern really exists in larger sample of albums? • Does supply side of data (audio_source) for downloads better than demand side of data (hash_request) for downloads # in forecasting sales? • Is there ommitted variable problem for not considering promotional-effect variable?