Data Mining Techniques by Berry, Michael J, Linoff, Gordon S
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Linoff, Gordon S.; Berry, Michael J. *Wiley Computer Publishing. Many companies invest in using a customer relationship management tool, or CRM, such as Salesforce. But there's a free tool at your fingertips that you need to . Results 1 - 30 of 98 Data Mining Techniques: for Marketing, Sales and Customer Relationship Management by Linoff, Gordon S., Berry, Michael J. and a great.
However, bringing in a new customer tends to cost more than holding on to an existing one Berry and Linoff, As computer technology improves, the amount of data stored in databases is exploding.
However, these enormous sets of data provide poor information. In the competitive business environment, enterprises need to turn this large amount of data into their own meaningful information. Thus this information can guide their investment, management and marketing strategies. The data mining technology is concerned with the discovery and extraction of latent knowledge from a database Chang et al. Many algorithms are developed, proposed and applied: These techniques have become more popular and been frequently used in real-world applications, clustering and decision tree are selected to further explain data mining.
Clustering is one of the techniques in data mining. It separates a heterogeneous population into a number of more homogeneous subgroups or clusters so that data in each cluster share some common trait Berry and Linoff, K-means is one kind of the major algorithms in common use for automatic cluster detection Macqueen, Clustering is often done as a beginning to other form of data mining or modeling. In this study, clustering technique is used to divide customers into clusters with similar traits and then decision tree technique used to analyze the high churn rate cluster for customer churn prediction.
Decision tree is popular and powerful for both classification and prediction. The attractiveness of tree-based methods is due largely to the fact that decision tree represent rules Berry and Linoff, A decision tree is based on the methodology of tree graphs and can be considered one of the more simple inductive study methods Quinlan,; Russell and Norving, Even if the user lacks any statistical knowledge, he or she can use a decision tree to analyze specific behavior and it can be converted into rules easily.
However, if it becomes too complicated or too huge for decision-making, trimming some of its leaves or branches may become necessary in order to improve its effectiveness.
Data mining techniques : for marketing, sales, and customer relationship management - EconBiz
Of all the calculative methods, ID3, C4. This study based on a real data set is obtained from a wireless network company in Taipei City. The users of the wireless network company with more than 4, members had joined the WIFLY project lasting ten month from January to October. We apply data mining techniques to analyze over 80, transactions.
The data set make users connect to WIFLY related information of behavior pattern, such as account, frequency, duration, gap and so on.
In this study, the chief analytical attributes are listed in Table 1. The objective is to understand the characteristics between different types of users in wireless network company. In order to improve customer retention and to predict customer churn. Analytical attributes used in decision tree Since to make sure the result is useful for wireless network companies, we separated ten- month original data set into three parts, which are history data, training and testing data.
The first four months data is picked out to construct the historical information. The next three months is picked out as the modeling data set.
Data Mining Techniques
At last, the last three months is picked out for the testing data set to evaluate the model constructed in the previous steps. In this study, the number of user accounts over includes more than ten types of subscribers in WIFLY project. We applied clustering technique to segment the diverse subscribers into a number of more similar subgroups or clusters. Nine clusters are produced from clustering analysis.
First column is the cluster name. Second column shows the percentage of the cluster in population and then displays the related attributes of cluster and population. For example, cluster 0 accounts for The following columns are detailed description for corresponding attributes.
The pie chart shows the distribution of categorical variables for customers within the segment inner circlewhich can be compared with the distribution of all customers outer circle. Inner pie chart is larger than outer pie chart, which indicates that cluster 0 has higher churn rate than that of other clusters. Therefore, we will apply decision tree techniques to predict its characteristics of churned customers.
Furthermore, the inner pie chart in other four clusters is less than the outer pie chart in Fig. They are with lower churn rate. These clusters are our target customers. The results from clustering analysis Fig. The results from decision tree analysis In Fig.
The churn customer includes switching other service provider or terminating account. According to customer relationship management, the cost of retaining old customer is less than that of discovering new customers. Those wireless network service providers can come up with various suitable pricing plans for their loyal customer clusters and build an effective customer churn model to prevent customers from churn.
Decision tree analysis result: Here, our goal is to analyze the characteristics of the customer with high churn rate by using decision tree technique. Figure 2 shows the results produced from decision tree analysis for the cluster with higher churn rate. First column displays the tree shape which shows the classifications of attributes. It denotes the depth-level of a node in the tree visiting tree paths.
This stage consists of processing the data, in order to convert the data in the appropriate formats for applying data mining algorithms. The most common transformations are: To normalize the data, each value is subtracted the mean and divided by the standard deviation.
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Some algorithms only deal with quantitative or qualitative data. Therefore, it may be necessary to discredit the data, i. This stage consists of discovering patterns in a dataset previously prepared. Several algorithms are evaluated in order to identify the most appropriate for a specific task. The selected one is then applied to the pertinent data, in order to find indirect relationships or other interesting patterns.
This stage consists of interpreting the discovered patterns and evaluating their utility and importance with respect to the application domain. In this stage it can be concluded that some relevant attributes were ignored in the analysis, thus suggesting the need to replicate the process with an updated set of attributes.
Data mining in Customer Relationship Management Customer relationship management CRM comprises a setoff processes and enabling systems supporting a business strategy to build long term, profitable relationships with specific customers. In figure2 Data mining can help companies in better understanding of the vast volume of data collected by the CRM systems.
In the past few years, many organizations especially retailers and banks have recognized the vital importance of the information they have on their customers. The banking industry is widely recognizing the importance of the information it has about its customers.
Undoubtedly, it has among the richest and largest pool of customer information, covering customer demographics, transactional data, credit cards usage pattern, and so on. As banking is in the service industry, the task of maintaining a strong and effective CRM is a critical issue. To do this, banks need to invest their resources to better understand their existing and prospective customers.
Berry and Linoff defines data mining as the process of exploring and analyzing huge datasets, in order to find patterns and rules which can be important to solve a problem .
According to Ngai et al. In figure 3 these groups of data mining techniques can be summarized as follows: The focus is on deriving multi-attribute correlations, satisfying support and confidence thresholds . Examples of association model outputs are association rules. For example, these rules can be used to describe which items are commonly purchased with other items in grocery stores. For example, a classification model can be used to identify loan applicants as low, medium, or high credit risks .
Unlike classification in which the classes are predefined, in clustering the classes are determined from the data. Clusters are defined by finding natural groups of data items, based on similitude marks or probability bulk models Berry and Linoff, ; Mitra et al. For example, a clustering model can be used to group customers who usually buy the same group of products . It deals with outcomes measured as continuous variables Ahmed, ; Berry and Linoff, The central elements of forecasting analytics are the predictors, i.
Curve fitting, modeling of causal relationships, prediction including forecasting and testing scientific hypotheses about relationships between variables are frequent applications of regression. It can essentially be thought of as association discovery over a temporal database . For example, sequence analysis can be developed to determine, if customers had enrolled for plan A, then what is the next plan that customer is likely to take-up and in what time-frame.
Usually it is used jointly with other data mining models to provide a clearer understanding of the discovered patterns or relationships Turban et al. Examples of visualization applications include the mind maps . Data Mining Applications in Banking Sector Figure depicts the data mining techniques and algorithm that are applicable to the banking sector. Customer retention pays vital role in the banking sector.
Preventing fraud is better than detecting the fraudulent transaction after its occurrence. Clustering model implemented using EM algorithm can be used to detect fraud in banking sector. Today, customers have so many opinions with regard to where they can choose to do their business Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics.
To improve customer retention, three steps are needed: In this approach, risk levels are organized into two categories based on past default history. For example, customers with past default history can be classified into "risky" group, whereas the rest are placed as "safe" group. Decision trees are the most popular predictive models Burez and Van den Poel, A decision tree is a tree-like graph representing the relationships between a set of variables .
Decision tree models are used to solve classification and prediction problems where instances are classified into one of two classes, typically positive and negative, or churner and non-churner in the churn classification case.
Building a decision tree incorporates three key elements: In this method, for example, alternative of classifying new loan applications, it attempts to predict prospect conventional amounts for new loan applications Neural Network and regression are used for this purpose.
The most common data mining methods used for customer profiling are: Classification predictive and regression predictive 3. Association rule discovery descriptive and sequential pattern discovery predictive 4.
Fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection.
Classification is perhaps the most familiar and most popular data mining technique. Estimation and prediction may be viewed as types of classification.
There are more classification methods such as statistical based, distance based, decision tree based, neural network based, rule based . Each sample Si consists of a p-dimensional vector x1,i,x2,i, …, xp,iwhere the xj represent attributes or features of the sample, as well as the class in which si falls . A CART tree is a binary decision tree that is constructed by splitting a node into two child nodes repeatedly, beginning with the root node that contains the whole learning sample.
Used by the CART classification and regression tree algorithm, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset. In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines SVMs and other kernelized models, that represents the similarity of vectors training samples in a feature space over polynomials of the original variables.
Logistic regression or logit regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable based on one or more predictor variables.
Instead of fitting the data to a straight line, logistic regression uses a logistic curve.
The formula for a univariate logistic curve is To perform the logarithmic function can be applied to obtain the logistic function Logistic regression is simple, easy to implement, and provide good performance on a wide variety of problems . Sometimes the given demographics and transaction history of the customers are likely to defraud the bank. Data mining technique helps to analyze such patterns and transactions that lead to fraud.
Banking sector gives more effort for Fraud Detection. Fraud management is a knowledge-intensive activity. It is so important in fraud detection is that finding which ones of the transactions are not ones that the user would be doing. Clustering helps in grouping the data into similar clusters that helps in uncomplicated retrieval of data.
Cluster analysis is a technique for breaking data down into related components in such a way that patterns and order becomes visible. In order to determine these regions of cauterization first its need to find the maximum difference DIFFmax between values of an attribute in the training data. Ninterval is the binary logarithm of the attribute values account Npoints. In general, Ninterval can be found using another way of looking. Such calculation of Ninterval is based on the assumption that a twofold increase of Npoints will be equal to Ninterval plus one.
Thus Ninterval centers and corresponding deviations that describe all values of the certain attribute from the training data appears.