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Customer Relationship Management and Small Data — Application of Bayesian Network Elicitation Techniques for Building a Lead Scoring Model

机译:客户关系管理和小型数据 - 应用贝叶斯网络阐述技术为建设铅评分模型

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摘要

Customer Relationship Management is an every day task for companies, even the ones dealing with Small Data.We are more interested here by Lead Scoring that refers to the practice of calculating and assigning a score to leads (business contacts or qualified prospects) of the company.In this paper, we present one way of building a Lead scoring model with a Bayesian network using a small amount of data. In addition to its ability of handling uncertainty, Bayesian networks are knowledge representation models that can be built from expert knowledge. In our specific context, we then propose to build our Lead scoring model from expertise and apply usual heuristics to decrease the complexity of our model (parent divorcing, NoisyOr). We specifically propose three ways of estimating the parameters of our NoisyOr submodels. The only data available is used to validate our approach, with good precision and recall results on a small set of 23 examples.
机译:客户关系管理是公司的每一天任务,即使是处理小型数据的人。我们对铅评分更感兴趣,指的是计算和分配分数的实践,以获得公司的{业务联系或合格的前景)在本文中,我们使用少量数据提出了用贝叶斯网络建立铅评分模型的一种方式。除了处理不确定性的能力外,贝叶斯网络是知识表示模型,可以由专业知识构建。在我们的具体背景下,我们建议从专业知识建立我们的领导评分模型,并应用通常的启发式,以降低我们模型的复杂性(父层离婚,诺斯义)。我们专门提出了三种估算诺斯中子模型参数的方法。可用的唯一数据用于验证我们的方法,具有良好的精度,并记录一小组的23个示例。

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