The Research On Customer Behavior Analysis And Forecast Based On Data Mining

The globalization and diversity of economy makes the enterprise changed its pattern from "product-centric" to "customer-centric". Customer relationship management (CRM) is becoming one of the most important aspects to evaluate the competitiveness of the enterprise. Analyzing the massive CRM data with data mining techniques has many advantages.It can discover the potential useful knowledge about customer, which will help the enterprise understand customers'buying habits and provide customers with personalized and better services.Meanwhile, CRM based on data mining may help the enterprise find, attract and develop potential customers to maximize business profits. Therefore, the study of data mining technology in the CRM has important theoretical and practical significance.The classification and forecast are important research topics in data mining domain. Many research works have been employed the customer relations management. With the data set provided by Orange corporation, we aim to classify and predict the customers" three aspects, including Appetency, Churn, Up-selling. We firstly design a data mining flow oriented to the customers classification. After preprocessing the data sets, we implement and modify three classification algorithms.And then we propose four kinds of combined classifiers.At last, we conduct several experiments to evaluate the performance the algorithms.The experimental results are also presented, compared and analyzed. The main works in this thesis are following:Data preprocessing:data preprocessing is a very important step for the whole data mining process.It has the direct influence to data mining's final results.Therefore, data preprocessing in this thesis is divided into two steps:simple preprocessing and deep preprocessing. Simple preprocessing incorporates data exploration, data cleaning, data discretization and attribute/feature selection. The deep preprocessing, however, depends on the specific classification model requirements.The construction of classification model:With the data set provided by Orange Corporation, we firstly study the algorithm of Multi-Layer Perceptrons and then implement it on the data set to construct our first classifier. The second classifier is constructed based on Support Vector Machine.Then we implement and modify the algorithm of Logistic Model Tree, which help us construct the third classifier. To improve the performance of classification, we propose four kinds of combined classifiers including combined posteriors, combined votes, weighted posteriors, and weighted votes.Experiment design and analysis:To evaluate the performance the algorithms, we conduct several experiments.We firstly present the experiment framework/flow. And then we implement the proposed or modified algorithms detailed in chapter 4.The AUC(Area Under the Curve) is adopted as the criteria of classification performance.Three single classifier including MLP, SVM and LMT results are compared and analyzed.The results show that our modified LMT has the highest AUC value.In order to improve the classification performance, we implement our four proposed combined algorithms.The experimental results show that weighted posteriors and weighted votes achieves good performance.This thesis apply the data mining technologies in the customer data set provide by Orange. Through designing the data mining process and constructing classifiers, we achieve the classification and prediction of the customers'three aspects, including Appetency, Churn, Up-selling. Experiment results show that our methods are effective and efficient in CRM. Therefore, the models constructed in this thesis have some potential significance in CRM.