Vip-Focused Crm Strategies In An Open-Market

Nowadays, an open-market which provides sellers and consumers a cyber place for making a transaction over the Internet has emerged as a prevalent sales channel because of convenience and relatively low price it provides. However, there are few studies about CRM strategies based on VIP consumers for an open-market even though understanding VIP consumers’ behaviours in an open-market is absolutely important to increase its revenue. Therefore, we propose CRM strategies focused on VIP customers, obtained by analyzing the transaction data of VIP customers from an open-market using data mining techniques. To that end, we first defined the VIP customers in terms of recency, frequency and monetary (RFM) values. Then, we used data mining techniques to develop a model which best classifies customers into VIPs or non-VIPs. We also validate each of promotion types in the aspect of effectiveness to VIP customers and identify association rules among the types from the transactions of VIP customers. Then, based on the findings from these experiments, we propose strategies from the perspectives of CRM dimensions such as customer identification, attraction, retention and development for the open-market to thrive

A PREDICTIVE MODEL FOR CUSTOMER PURCHASE BEHAVIOR IN E-COMMERCE CONTEXT

Predicting customer purchase behaviour is an interesting and challenging task. In e-commerce context, to tackle the challenge will confront a lot of new problems different from those in traditional business. This study investigates three factors that affect purchasing decision-making of customers in online shopping: the needs of customers, the popularity of products and the preference of the customers. Furthermore, exploiting purchase data and ratings of products in the e-commerce website, we propose methods to quantify the strength of these factors: (1) using associations between products to predict the needs of customers; (2) combining collaborative filtering and a hierarchical Bayesian discrete choice model to learn preference of customers; (3) building a support vector regression based model, called Heat model, to calculate the popularity of products; (4) developing a crowdsourcing approach based experimental platform to generate train set for learning Heat model. Combining these factors, a model, called COREL, is proposed to make purchase behaviour prediction for customers. Submitted a purchased product of a customer, the model can return top n the most possible purchased products of the customer in future. Experiments show that these factors play key roles in predictive model and COREL can greatly outperform the baseline methods

Exploring the Preference Structure for Online and Traditional Retail Formats

retail, structure, formats

Doctor of Philosophy

dissertationIn the current business world, data collection for business analysis is not difficult any more. The major concern faced by business managers is whether they can use data to build predictive models so as to provide accurate information for decision-making. Knowledge Discovery from Databases (KDD) provides us a guideline for collecting data through identifying knowledge inside data. As one of the KDD steps, the data mining method provides a systematic and intelligent approach to learning a large amount of data and is critical to the success of KDD. In the past several decades, many different data mining algorithms have been developed and can be categorized as classification, association rule, and clustering. These data mining algorithms have been demonstrated to be very effective in solving different business questions. Among these data mining types, classification is the most popular group and is widely used in all kinds of business areas. However, the exiting classification algorithm is designed to maximize the prediction accuracy given by the assumption of equal class distribution and equal error costs. This assumption seldom holds in the real world. Thus, it is necessary to extend the current classification so that it can deal with the data with the imbalanced distribution and unequal costs. In this dissertation, I propose an Iterative Cost-sensitive Naïve Bayes (ICSNB) method aimed at reducing overall misclassification cost regardless of class distribution. During each iteration, K nearest neighbors are identified and form a new training set, which is used to learn unsolved instances. Using the characteristics of the nearest neighbor method, I also develop a new under-sampling method to solve the imbalance problem in the second study. In the second study, I design a general method to deal with the imbalance problem and identify noisy instances from the data set to create a balanced data set for learning. Both of these two methods are validated using multiple real world data sets. The empirical results show the superior performance of my methods compared to some existing and popular methods