Artificial Neural Networks in Customer Segmentation

Ozan, Sukru; Iheme, Leonardo O.

doi:10.1109/siu.2019.8806558

Cited by 12 publications

(1 citation statement)

References 4 publications

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“…In [22] monetary based customer segmentation problem is addressed where payment information of customers are used for segmentation by using traditional ML methods like linear and logistic regression. Also in [23] the same problem is solved by using a simple neural network and the accuracy is further improved.…”

Section: Related Workmentioning

confidence: 99%

Case studies on using natural language processing techniques in customer relationship management software

Ozan¹

2020

J Intell Inf Syst

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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.

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Section: Related Workmentioning

confidence: 99%