  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.
