Customer Segmentation Using Machine Learning

Abstract

Nowadays Customer segmentation became very popular method for dividing company’s customers for retaining customers and making profit out of them, in the following study customers of different of organizations are classified on the basis of their behavioral characteristics such as spending and income, by taking behavioral aspects into consideration makes these methods an efficient one as compares to others. For this classification a machine algorithm named as k-means clustering algorithm is used and based on the behavioral characteristic’s customers are classified. Formed clusters help the company to target individual customer and advertise the content to them through marketing campaign and social media sites which they are really interested in.

Personalized recognition system in online shopping by using deep learning

“…Since the advent of mass media, the advertising industry has relied heavily on personalization to reach its target audience [12,13,14]. Machine learning-based methods have now been included in ad personalization development.…”

“…Modern recommender systems and AI technologies are utilised to find new customers [6]. Parsable information, such as textual data, is analysed by Mooney and Roy [15] to determine the similarity between customer and product content profiles. Text mining of usergenerated material on social networking sites (SNS), especially Twitter, has been widely employed for audience prediction and segmentation [10], blogger interest discovery [16], and content-based recommender systems for SNS users with appropriate interests and preferences [17].…”

“…Unlike frequent shoppers at supermarkets, customers in convenience shops often only buy a few goods. Convenience shops in China are usually found in dense urban areas with premium space and high rents [13,14]. The store's checkout lines tend to get quite long in the early morning and late evening.…”