Customer Segmentation of Cross-border E-commerce based on FRMD Using Unsupervised Machine Learning

As buyers buy things from beyond national borders, cross-border e-commerce has quietly gathered significant momentum. E-commerce, which may be loosely described as the usage of the Internet as a medium for commercial transactions and the dissemination of market information, is expected to play an increasingly significant role in fueling economic expansion throughout the world. Under the assumptions of traditional mass marketing, consumers are all the same. The business has a unified strategy for producing, delivering, and engaging with customers, enabling it to save time and money while expanding its customer base to new heights. Companies relied much more heavily on mass marketing before consumer data was easily accessible. Big data has caused the proliferation of market segmentation. Market segmentation in the context of cross-border e-commerce is the process of identifying distinct groups of international customers and categorizing them so that targeted advertising campaigns may be developed. We extend the traditional FRM (frequency, recency, and monetary value) analysis to include the geographical distance of the foreign customers from the e-commerce company. This research used 3,000 overseas customer data from 8 different e-commerce stores. The unsupervised hierarchical clustering is based on four dimensions, namely, frequency of shopping, recency of transaction, monetary values of the purchased items, and finally, the geographical distance of the foreign customers. Companies of all sizes utilize market segmentation to hone their strategy and provide the highest quality products for their specific target populations

Empirical analysis of customer behaviors in Chinese e-commerce

With the burgeoning e-Business websites, E-Commerce in China has been developing rapidly in recent years. From the analysis of Chinese E-Commerce market, it is possible to discover customer purchasing patterns or behavior characteristics, which are indispensable knowledge for the expansion of Chinese E-Commerce market. This paper presents an empirical analysis on the sale transactions from the 360buy website based on the analysis of time interval distributions in perspectives of customers. Results reveal that in most situations the time intervals approximately obey the power-law distribution over two orders of magnitudes. Additionally, time interval on customer’s successive purchase can reflect how loyal a customer is to a specific product category. Moreover, we also find an interesting phenomenon about human behaviors that could be related to psychology of customers. In general, customers’ requirements in different product categories are similar. The investigation into individual behaviors may help researchers understand how customers’ group behaviors generated

Using multiple criteria decision making models for ranking customers of bank network based on loyalty properties in weighted RFM model

One of the most basic requirements of financial institutes, governmental and private banks in the present age is to have a good understanding on customers' behaviors of bank network. It helps banks determine customer loyalty, which yields profit making for bank. On the other hand, it is important to know about credit risk of customers with the goal of decreasing loss and better allocation of bank resources to applicants of receiving loan. According to nature of customer loyalty discussion and credit risk, these two issues are separately studied. The present article deals with studying customer loyalty and prioritizing based one private bank in Kurdistan province. The proposed model of this paper studies customer loyalty by using Recency Frequency Monetary (RFM) factor for prioritizing customer based on loyalty properties and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In addition, in order to calculate the relative importance coefficient or weight of loyalty properties in RFM method, the pair wise comparison matrix based on analytical hierarchy process (AHP) is used. Results show that in the present study, necessarily customers having higher average monetary value during a specified time period does not have much higher priority compared with other customers

CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns

Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall

CUSTOMER SEGMENTATION BY USING RFM MODEL AND CLUSTERING METHODS: A CASE STUDY IN RETAIL INDUSTRY

In today’s business environment companies should need better understanding on customers’ data. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities to customers, etc. became very important for customer-company engagement. Segmenting customers according to their data became vital in this context. RFM (recency, frequency and monetary) values have been used for many years to identify which customers valuable for the company, which customers need promotional activities, etc. Data-mining tools and techniques commonly have been used by organizations and individuals to analysis their stored data. Clustering, which one of the tasks of data mining has been used to group people, objects, etc. In this paper we propose two different clustering models to segment 700032 customers by considering their RFM values. We suggest that the current customer segmentation which built by just considering customers’ expense is not sufficient. Hence, one of the models that recommended in this research is expected to provide better customer understanding, well-designed strategies, and more efficient decisions

Comparing Loyalty Program Tiering Strategies: An investigation from the gaming industry

Loyalty programs are popular marketing strategies developed for the purpose of attracting, maintaining, and enhancing customer relationships. Due to the explosive worldwide growth of, and increased competition within, the casino industry has compelled contemporary casino marketers to rely more heavily on loyalty programs to increase guest allegiance and the frequency of repeat visits from their customers. Despite the widespread usage of loyalty programs across various gaming businesses in Las Vegas, its effectiveness has not quite been validated. The purpose of this study is to examine customers’ behavioral loyalty within the Las Vegas gaming industry and examine the effectiveness of a specific loyalty program using secondary data obtained from a Las Vegas casino hotel. Specifically, this study segmented loyalty program members to investigate the effectiveness of a casino loyalty program’s tiering strategy on members’ purchase behavior. Further, this study employed Recency-Frequency-Monetary (RFM) analysis to examine two different types of tiering strategies

Practical Aspects of Log File Analysis for E-Commerce

The paper concerns Web server log file analysis to discover knowledge useful for online retailers. Data for one month of the online bookstore operation was analyzed with respect to the probability of making a purchase by e-customers. Key states and characteristics of user sessions were distinguished and their relations to the session state connected with purchase confirmation were analyzed. Results allow identification of factors increasing the probability of making a purchase in a given Web store and thus, determination of user sessions which are more valuable in terms of e-business profitability. Such results may be then applied in practice, e.g. in a method for personalized or prioritized service in the Web server system

Improving Organizational Decision Making Using a SAF-T based Business Intelligence System

Today, companies need to quickly adapt to business changes and react to customers\u27 tendencies and market demands in an unpredictable environment. In this field, the analytical systems represent an important asset that each company should have and use. Data Warehousing Systems (DWS) support companies\u27 analytical needs, however, the development and integration of the data systems is a critical part. Due to specificities of the involved data, each DWS is unique, which compromises the use of reusable components or even the use of pre-built solutions. In this paper, we propose a standard skeleton for a DWS based on Portuguese Audit Tax documents (SAF-T (PT)). These documents represent a standardized procedure for every Portuguese company, providing the necessary data about billing, accounting, and taxation. Thus, they can provide the foundations to use them as a standard data representation to create a DWS that can be posteriorly explored by analytical techniques to generate useful insights