论文标题
B2B销售预测建模的广义流：Azure机器学习方法
A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach
论文作者
论文摘要
预测销售机会的结果是成功业务管理的核心。通常，在销售决策过程中，做出这一预测主要依赖于主观的人类评估。在本文中，我们通过在基于云的计算平台上提出彻底的数据驱动机器学习（ML）工作流程：Microsoft Azure机器学习服务（Azure ML），解决了预测业务结果（B2B）销售结果的问题。该工作流程由两个管道组成：（1）在历史销售机会数据上培训概率预测模型的ML管道。在此管道中，数据具有广泛的功能增强步骤，然后用来并行训练ML分类模型的集合。 （2）使用训练有素的ML模型并推断赢得新销售机会的可能性以及计算最佳决策边界的可能性。在一家主要的全球B2B咨询公司的真实销售数据集上评估了拟议的工作流的有效性。我们的结果表明，基于ML预测的决策更准确，并带来更高的货币价值。
Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, making this prediction has relied mostly on subjective human evaluations in the process of sales decision making. In this paper, we addressed the problem of forecasting the outcome of business to business (B2B) sales by proposing a thorough data-driven Machine Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to utilize the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.
