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.
Enhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；Swanson School of Engineering, University of Pittsburgh（匹兹堡大学斯旺森工程学院）；Duquesne University（杜肯大学）
Automated metrics calculation in a dynamic heterogeneous environment
微软软件实验的一个一贯主题是解决各种产品的大规模实验问题。在规模上运行实验（即，在许多用户上进行许多实验）已经成为整个行业的现有技术。然而，提供一个单一的平台，允许在一个高度异质和不断发展的生态系统中进行软件实验仍然是一个挑战。在我们的例子中，异质性跨越了多个维度。首先，我们需要支持多种产品的实验：网站、搜索引擎、移动应用程序、操作系统、云服务等。其次，由于使用我们平台的产品和团队的多样性，它需要足够灵活，以分析多个计算结构（例如Spark，Azure Data Explorer）中的数据，并在需要时轻松添加对新结构的支持。第三，促进组织中实验文化发展的主要因素之一是使度量定义和分析过程民主化。为了实现这一目标，我们的系统需要足够简单，不仅可以供数据科学家使用，还可以供工程师、产品经理和销售团队使用。最后，不同的人物角色可能需要使用平台进行不同类型的分析，例如仪表板或实验分析，平台应该足够灵活以适应这种情况。本文提出了我们的解决方案，上述问题的异质性。
Integrating Marketing Channels into Quantile Transformation and Bayesian Optimization of Ensemble Kernels for Sales Prediction with Gaussian Process Models
AttnBoost: Retail Supply Chain Sales Insights via Gradient Boosting Perspective
Nanyang Technological University（南洋理工大学）；Washington State University（华盛顿州立大学）；Southern Methodist University（南方 Methodist 大学）；Georgetown University（乔治城大学）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；China Construction Bank（中国建设银行）
