How can a text corpus stored in a customer relationship management (CRM) database be used for data mining and segmentation? In order to answer this question we inherited the state of the art methods commonly used in natural language processing (NLP) literature, such as word embeddings, and deep learning literature, such as recurrent neural networks (RNN). We used the text notes from a CRM system which are taken by customer representatives of an internet ads consultancy agency between years 2009 and 2020. We trained word embeddings by using the corresponding text corpus and showed that these word embeddings can not only be used directly for data mining but also be used in RNN architectures, which are deep learning frameworks built with long short term memory (LSTM) units, for more comprehensive segmentation objectives. The results prove that structured text data in a CRM can be used to mine out very valuable information and any CRM can be equipped with useful NLP features once the problem definitions are properly built and the solution methods are conveniently implemented.

中文翻译：

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在客户关系管理软件中使用自然语言处理技术的案例研究

如何将存储在客户关系管理 (CRM) 数据库中的文本语料库用于数据挖掘和细分？为了回答这个问题，我们继承了自然语言处理 (NLP) 文献（例如词嵌入）和深度学习文献（例如循环神经网络 (RNN)）中常用的最先进方法。我们使用了 2009 年至 2020 年互联网广告咨询机构的客户代表从 CRM 系统中提取的文本注释。我们使用相应的文本语料库训练词嵌入，并表明这些词嵌入不仅可以直接用于数据挖掘，但也可用于 RNN 架构，这些架构是使用长短期记忆 (LSTM) 单元构建的深度学习框架，以实现更全面的分割目标。