  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.
