销售渠道获胜倾向预测：回归方法
Sales pipeline win propensity prediction: a regression approach
摘要：
销售渠道分析是企业销售渠道主动管理的基础，对业务成功至关重要。特别是，获胜倾向预测，它涉及定量估计在指定的时间窗口内赢得正在进行的销售机会的可能性，是销售管理的基本构建块，并为资源优化和销售差距分析等许多应用奠定了基础。随着大数据的激增，使用数据驱动的预测模型作为推动更好销售业绩的手段越来越普遍，无论是在企业对客户（B2C）还是企业对企业（B2B）市场。然而，B2B交易数量相对较少（与B2C交易量相比），噪声数据和快速变化的市场环境对有效的预测建模提出了挑战。本文提出了一个基于机器学习的销售机会获胜倾向预测的统一框架，旨在解决这些挑战。我们证明了我们提出的系统使用的数据，从一个前500名的企业在企业对企业市场的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
