Abstract

Sales pipeline analysis is fundamental to proactive management of an enterprize's sales pipeline and critical for business success. In particular, win propensity prediction, which involves quantitatively estimating the likelihood that on-going sales opportunities will be won within a specified time window, is a fundamental building block for sales management and lays the foundation for many applications such as resource optimization and sales gap analysis. With the proliferation of big data, the use of data-driven predictive models as a means to drive better sales performance is increasingly widespread, both in business-to-client (B2C) and business-to-business (B2B) markets. However, the relatively small number of B2B transactions (compared with the volume of B2C transactions), noisy data, and the fast-changing market environment pose challenges to effective predictive modeling. This paper proposes a machine learning-based unified framework for sales opportunity win propensity prediction, aimed at addressing these challenges. We demonstrate the efficacy of our proposed system using data from a top-500 enterprize in the business-to-business market.

Comment: accepted by IM2015 as short paper


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1109/inm.2015.7140393
https://dblp.uni-trier.de/db/journals/corr/corr1502.html#YanGSHC15,
https://ieeexplore.ieee.org/document/7140393,
http://ui.adsabs.harvard.edu/abs/2015arXiv150206229Y/abstract,
https://arxiv.org/abs/1502.06229,
http://ieeexplore.ieee.org/document/7140393,
https://doi.org/10.1109/INM.2015.7140393,
https://academic.microsoft.com/#/detail/1916889544
Back to Top

Document information

Published on 01/01/2015

Volume 2015, 2015
DOI: 10.1109/inm.2015.7140393
Licence: CC BY-NC-SA license

Document Score

0

Views 0
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?