On machine learning towards predictive sales pipeline analytics

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2015, Pages 1945-1951

  Yan, Junchi ^a,b,c   Zhang, Chao ^b   Zha, Hongyuan ^a,d   Gong, Min ^b   Sun, Changhua ^b   Huang, Jin ^b   Chu, Stephen ^b   Yang, Xiaokang ^c  

^a EAST CHINA NORMAL UNIVERSITY   (China)

^b IBM CHINA RESEARCH LAB   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

^d Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; PIPELINES;

MODERN MACHINES; ON-MACHINES; REAL-WORLD PROBLEM; SALES MANAGEMENT;

SALES;

EID: 84959916172     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. Chen, C.-Y.; Lee, W.-L; Kuo, H.-M.; Chen, C.-W.; and Chen, K.-H. 2010. The study of a forecasting sales model for fresh food. Expert Systems with Applications.
2. Cox, D. R., and Oakes, D. 1984. Analysis of survival data, volume 21. CRC Press.
3. Daley, D. J., and Vere-Jones, D. 1988. An introduction to the theory of point processes, volume 2. Springer.
4. Ertekin, S.; Rudin, C; and McCormick, T. H. 2013. Reactive point processes: A new approach to predicting power failures in underground electrical systems.
5. Hawkes, A. G. 1971. Spectra of some self-exciting and mutually exciting point processes. Biometrika.
6. Hunter, D. R., and Lange, K. 2004. A tutorial on mm algorithms. The American Statistician 58(1 ):30-37.
7. Kawas, B.; Squillante, M. S.; Subramanian, D.; and Varsh-ney, K. R. 2013. Prescriptive analytics for allocating sales teams to opportunities. In ICDM Workshop.
8. Kober, J., and Peters, J. 2012. Reinforcement learning in robotics: A survey. In Reinforcement Learning. Springer. 579-610.
9. Lawrence, R.; Perlich, C; Rosset, S.; Khabibrakhmanov, I.; Mahatma, S.; Weiss, S.; Callahan, M.; Collins, M.; Ershov, A.; and Kumar, S. 2010. Operations research improves sales force productivity at ibm. Interface 40(l):33-46.
10. Lewis, E., and Möhler, E. 2011. A nonparametric em algorithm for multiscale hawkes processes. Journal of Nonparametric Statistics.
11. Lewis, E.; Möhler, G.; Brantingham, P. J.; and Bertozzi, A. 2010. Self-exciting point process models of insurgency in iraq. UCLA CAM Reports 70 38.
12. Li, L., and Zha, H. 2013. Dyadic event attribution in social networks with mixtures of hawkes processes. In CIKM, 1667-1672. ACM.
13. Li, L., and Zha, H. 2014. Learning parametric models for social infectivity in multi-dimensional hawkes processes. In Twenty-Eighth AAAI Conference on Artificial Intelligence.
14. Li, L.; Deng, H.; Dong, A.; Chang, Y.; and Zha, H. 2014. Identifying and labeling search tasks via query-based hawkes processes. In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
15. Liniger, T. J. 2009. Multivariate hawkes processes. PhD thesis, Swiss Federal Institute Of Technology, Zurich.
16. Linoff, G. S., and Berry, M. J. A. 2011. Data mining techniques: For marketing, sales, and customer relationship management. Indianapolis, IN, USA: Wiley Publishing.
17. Möhler, G. O.; Short, M. B.; Brantingham, P. J.; Schoen-berg, F. P.; and Tita, G. E. 2011. Self-exciting point process modeling of crime. Journal of the American Statistical Association 106(493).
18. Ogata, Y 1988. Statistical models for earthquake occurrences and residual analysis for point processes. J. Amer. Statist. Assoc. 83(401 ):9-27.
19. Ogata, Y 1998. Space-time point-process models for earthquake occurrences. Annals of the Institute of Statistical Mathematics 50:379-402.
20. Ozaki.T. 1979. Maximum likelihood estimation of hawkes' self-exciting point processes. Annals of the Institute of Statistical Mathematics 31 ( 1 ): 145-155.
21. Rubin, I. 1972. Regular point processes and their detection. Information Theory, IEEE Transactions on 18(5):547-557.
22. Shivaswamy, P. K.; Chu, W.; and Jansche, M. 2007. A support vector approach to censored targets. In ICDM.
23. Tian, Y; Yan, J.; Zhang, H.; Zhang, Y; Yang, X.; and Zha, H. 2012. On the convergence of graph matching: Graduated assignment revisited. In ECCV.
24. Varshney, K. R., and Singh, M. 2013. Dose-response signal estimation and optimization for salesforce management. In SOLI.
25. Weber, T. A., and Chehrazi, N. 2012. Dynamic valuation of delinquent credit-card accounts. Technical report, EPFL-CDM-MTEI.
26. White III, C. C, and White, D. J. 1989. Markov decision processes. European Journal of Operational Research 39(1):1-16.
27. Yan, J.; Tian, Y; Zha, JH.; Yang, X.; Zhang, Y; and Chu, S. 2013a. Joint optimization for consistent multiple graph matching. In ICCV.
28. Yan, J. C; Wang, Y; Zhou, K.; Huang, J.; Tian, C. H.; Zha, H. Y; and Dong, W. S. 2013b. Towards effective prioritizing water pipe replacement and rehabilitation. In IJCAI.
29. Yan, J. C; Li, Y; Liu, W.; Zha, H. Y; Yang, X. K.; and Chu, S. M. 2014. Graduated consistency-regularized optimization for multi-graph matching. In ECCV.
30. Zhou, K.; Zha, H.; and Song, L. 2013a. Learning social infectivity in sparse low-rank networks using multidimensional hawkes processes. In AISTATS.
31. Zhou, K.; Zha, H.; and Song, L. 2013b. Learning triggering kernels for multi-dimensional hawkes processes. In ICML.
32. Zliobaite, I.; Bakker, J.; and Pechenizkiy, M. Towards context aware food sales prediction. In ICDMW'09.