An application of Customer Embedding for Clustering

Abstract

Effective and powerful strategic planning in a competitive business environment brings businesses to the fore. It is important for the growth of the business to move the customer to the center by acting more intelligently in the planning of marketing and sales activities. In order to find customer behavior patterns, the use of clustering models from machine learning algorithms can yield effective results. In this study, traditional customer clustering methods are enriched by using customer representations as features. To be able to achieve that, a natural language processing method, word embedding, is applied to customers. By using the powerful mechanism of word embedding methods, a customer space is created where the customers are represented based on the products they have bought. It is observed that appending customer embeddings for customer clustering have a positive effect and the results seem promising for further studies.