Under the data-driven environment, market competition is increasingly fierce. Enterprises begin to pay attention to precise marketing to make costs down, improve marketing efficiency and competitiveness. E-mail marketing is widely used in enterprises due to its advantages of low cost and wide audience. This paper uses machinelearning techniques such as decision tree, cluster analysis and Naive Bayes algorithm to analyze customer characteristics and attributes with historical purchase records, and further analyzes the key factors that affect potential customers' purchase behavior by selecting models with high promotion degree through promotion graph, to realize accurate marketing. The results show that the prediction effect of decision tree is better than clustering analysis and Naive Bayesian algorithm, and has a higher promotion degree. The customers who are 45-55 years old and commute 1-2 kilometers away are more likely to make purchases if they do not have a car or have a car at home.

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A Machine Learning Based Method for Customer Behavior Prediction

The objective of achieving profitability is one of the main targets of any banking sector for longer sustainable existence. The customer satisfaction index determines the longevity in relation of customer-bank and thereby provides the idea of devising new policies and strategies for healthy connection of customers with the bank. Offering the services and products to the customer based on his choice and needs requires understanding the customer. The customer data available in the bank can provide the deep insights to the bank for designing the customized service and products. Deriving useful information from customer data using data mining techniques is of paramount importance in these days. Leveraging existing granular customer data can help banks  gain deep actionable customer insights useful to understand the customers and to reveal and unlock opportunities for increasing profitability .customer segmentation and  profiling are vital in achieving two main objectives of CRM(Customer Relationship Management)i.e.; customer retention and customer development. The main aims of customer profiling and segmentation include expanding customer base, design of tailor made products, micro targeting of sales, aligning right channels for right products, increasing effectiveness of cross selling and up-selling, enhanced customer experience by focused customer relationship, prioritizing relationship with high value customers, effectively managing cost with low value customers based on the profiling and segmentation of customers. In this paper we are using data mining techniques i.e. Naive Bayes classification algorithm for customer profiling and BIRCH clustering algorithm for customer segmentation

http://www.ijarcs.info/index.php/Ijarcs/article/download/6172/5100

Customer profiling and segmentation in retail banks using data mining techniques

This article based on the basic situation of college students' current performance management and elective guidance, introduce the knowledge of data mining, and select the association rules mining algorithm using in the analysis of students failed courses. Find the Associated factors affecting students achievement factors, and provide decision support information for educators. So promote to carry out the intervention scientifically, and improve education quality and the scientific level of University Students work.

Study and analysis of data mining technology in college courses students failed

In this paper, Apriori algorithm has been improved and applied to substation data mining process. Ant colony algorithm is applied to get the optimal solution of reactive power allocation in substations. The state transition probability formula is amended and parameters are dynamically adjusted in this ant colony algorithm. The choice of the ant’s path to the next node is determined by the tabu table formulated according to the confidence level of the data mining. The switching strategy of the capacitor sets are given by online algorithm.

An Improved Data Mining Technique Combined Apriori Algorithm with Ant Colony Algorithm and its Application

Data mining has been used in several studies to uncover hidden information within dataset and to predict outcomes after application of algorithms. In this study, neural connection was adapted as data mining tool where supervised multilayer perceptron which is the architecture with back propagation learning algorithms was used on a new automobile data after application of clustering to remove outliers in the original auto mpg data set. The purpose was to classify the city fuel consumption of different car automobiles as efficient, averagely efficient and less efficient..

Classifying auto-MPG data set using neural network

It discusses application of data mining technology in Xinjiang Training Center of CNPC. The existing training management information system gathered lots of data, used data mining technology bases on these data, applied Microsoft SQL Server 2005 IDE and Microsoft Time Series as an example, created data mining model to mining data, predicted the abiliy of training Departments ,in order to provide useful informatns for Managers when they made decision of reasonable allocation training resources, finally summed up problems and solutions in the development process.

Rixit Kudrat

Papers

Application of Data mining Technology