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

机译:客户关系管理和小数据-贝叶斯网络启发技术在建立潜在顾客评分模型中的应用

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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日常任务,即使是处理小数据的公司也是如此。在这里,“潜在客户评分”使我们更加感兴趣,这是指为公司的潜在客户(业务联系人或合格的潜在客户)计算和分配分数的做法在本文中,我们提出了一种使用少量数据使用贝叶斯网络构建潜在顾客评分模型的方法。贝叶斯网络除了具有处理不确定性的能力外,还可以从专家知识中构建知识表示模型。然后,在特定的情况下,我们建议根据专业知识来建立潜在客户评分模型,并应用常用的试探法来降低模型的复杂性(父母离婚,NoisyOr)。我们专门提出了三种估算NoisyOr子模型参数的方法。仅有的可用数据可用于验证我们的方法,并具有良好的准确性,并通过一小部分23个示例获得了召回结果。

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