Customer Segmentation Using Real Transactional Data in E-Commerce Platform: A Case of Online Fashion Bags Shop

Customer segmentation has been widely used in different businesses and plays important rules in customer service. How to get a suitable segmentation based on the real transactional data to fully mining the hidden customer information in the massive data is still a challenge in current e-commerce platforms. This paper develops a customer segmentation model for online shops and uses the real data from a fashion bag store as a case. This paper firstly conducts a data preprocessing to select the main customer features, then it constructs a segmentation model based on the Fuzzy C-Means algorithm, and finally accomplishes a customer prediction model using a probabilistic neural network to estimate new customer’s customer type. The results show that the customer samples are classified into three types, and the prediction accuracy is more than 90%. After that, this paper demonstrates the typical features of each type of customer and compares the new group features with the prior VIP groups. The ANOVA analysis test results show that the new groups have more significant differences than prior VIP groups, which means more effective segmentation results

Large Scale Product Recommendation of Supermarket Ware Based on Customer Behaviour Analysis

In this manuscript, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon, both containing information about customers’ purchases. Subsequently, our model analyzes these data in order to classify customers as well as products, being trained and validated with real data. This model is targeted towards classifying customers according to their consuming behaviour and consequently proposes new products more likely to be purchased by them. The corresponding prediction model is intended to be utilized as a tool for marketers so as to provide an analytically targeted and specified consumer behavior. Our algorithmic framework and the subsequent implementation employ the cloud infrastructure and use the MapReduce Programming Environment, a model for processing large data-sets in a parallel manner with a distributed algorithm on computer clusters, as well as Apache Spark, which is a newer framework built on the same principles as Hadoop. Through a MapReduce model application on each step of the proposed method, text processing speed and scalability are enhanced in reference to other traditional methods. Our results show that the proposed method predicts with high accuracy the purchases of a supermarket