Dynamic e-targeting using learning spatial choice models

Journal of Interactive Marketing

Volumn 20, Issue 3-4, 2006, Pages 30-42

  Jank, Wolfgang ^a   Kannan, P K ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33846098443     PISSN: 10949968     EISSN: 15206653     Source Type: Journal    
DOI: 10.1002/dir.20065     Document Type: Article

References (22)

1. Bell, D.R., & Song, S. (2005). Neighborhood Effects and Trial on the Internet: Evidence From Online Grocery Retailing. Working Paper, Wharton School, University of Pennsylvania, Philadelphia.
2. Booth, J.G., Hobert, J.P., & Jank, W.S. (2001). A Survey of Monte Carlo Algorithms for Maximizing the Likelihood of a Two-Stage Hierarchical Model. Statistical Modelling., 1, 333-349.
3. Bronnenberg, B.J. (2005). Spatial Models in Marketing Research and Practice. Applied Stochastic Models in Business and Industry, 21, 335-343.
4. Bronnenberg, B.J., & Sismeiro, C. (2002). Using Multimarket Data to Predict Brand Performance in Markets for Which No or Poor Data Exist, Journal of Marketing Research. 39, 1-17.
5. Caffo, B.S., Jank, W.S., & Jones, G.L. (2005). Ascent-Based Monte Carlo EM. Journal of the Royal Statistical Society, Series B, 67, 235-252.
6. Cressie, N.A.C. (1993). Statistics for Spatial Data (Rev. ed.). New York: Wiley.
7. Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum Likelihood From Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society B, 39, 1-22.
8. Deutsch, S.J., & Pfeifer, P.E. (1981). Space-Time ARMA Modeling With Contemporaneously Correlated Innovations. Technometrics, 23, 401-409.
9. Hoffman, D.L., & Novak, T.P. (2000). How to Acquire Customers on the Web. Harvard Business Review, May/June, 179-188.
10. Hofstede, F.T., Wedel, M., & Steenkamp, J.-B.E.M. (2002). Identifying Spatial Segments in International Markets. Marketing Science, 21, 160-177.
11. Jank, W.S. (2004). Quasi-Monte Carlo Sampling to Improve the Efficiency of Monte Carlo EM. Computational Statistics and Data Analysis, 48, 685-701.
12. Jank, W.S., & Kannan, P.K. (2005). Understanding Geographical Markets of Online Firms Using Spatial Models of Customer Choice. Marketing Science, 24. 623-634.
13. Joseph, P. (2002, January 24). Beyond the Banner: New Strategies in Online Advertising. ZDNet, Available: http://www.techupdate.zdnet.com/ techupdate/stories/main/0,14179,2841691,00.html
14. Karypis, G. (2001). Evaluation of Item-Based Top-N Recommendation Algorithms. Proceedings of the 10th annual conference of Information and Knowledge Management, 247-254.
15. Linden, G., Smith, B., & York, J. (2003). Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, January/February, 76-80.
16. Mittal, V., Kamakura, W., & Govind, R. (2004). Geographic Patterns in Customer Service and Satisfaction: An Empirical Investigation. Journal of Marketing, 68. 48-62.
17. Ng, S.K., McLachlan, G.J., & Lee, A.H. (2006). An Incremental EM-Based Learning Approach for Online Prediction of Hospital Resource Utilization. Artificial Intelligence In Medicine., 36, 257-267.
18. Quova Geopoint. (2005). "Quova puts geolocation on the map." Quova press release 07/04/2005, available at http://www.quova.com/page.php?id= 33&news_id=103
19. Rust, R.T., & Donthu, N. (1995). Capturing Geographically Localized Misspecification Error in Retail Store Choice Models. Journal of Marketing Research, 32, 103-110.
20. Sarwar, B., Karypis, G., Constan, J., & Riedl, J. (2001, May). Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th World-Wide Web Conference, Hong Kong.
21. Yang, S., & Allenby, G.M. (2003). Modeling Interdependent Consumer Preferences. Journal of Marketing Research, 40, 282-294.
22. Zhang, H. (2002). On Estimation and Prediction for Spatial Generalized Linear Mixed Models. Biometrics, 58. 129-136.