Top PDF DATA MINING AND CRM IN TELECOMMUNICATIONS

DATA MINING AND CRM IN TELECOMMUNICATIONS

DATA MINING AND CRM IN TELECOMMUNICATIONS

CRM cannot be practiced in business without tracking patterns within customer data. Since the goal of data mining is extracting meaningful patterns and relationships from large data sets, data mining can redefine and improve customer relationships. Fortunately, telecommunications companies know more about their customers than anyone else. They know who their customers are, they can easily keep track of their customers’ activities. A huge amount of data generated by telecommunications companies cannot be analyzed in a traditional manner, by using manual data analysis. That is why different data mining techniques ought to be applied. Data mining helps a business understand its customers better. This paper will address the most valuable data mining applications for CRM in telecommunications.
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Title: The Relevance of Analytical CRM and Knowledge Management in an Organisation: A Data Mining Structure

Title: The Relevance of Analytical CRM and Knowledge Management in an Organisation: A Data Mining Structure

ABSTRACT - Knowledge Management and Analytical CRM are useful tools and technology in decision making process and understanding in an organization. This paper aims to provide a complete analysis of the concepts of Data Mining, knowledge management and analytical CRM to set up a framework for integrating all three together. The objective is to show how Knowledge Management and analytical CRM can be integrated into a structure that supports decision making using efficient Data Mining Techniques and to research how working on an analytical CRM system can enable organizations deliver complete solutions.
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Improvement of CRM using Data Mining: A Case study at Corporate Telecom Sector

Improvement of CRM using Data Mining: A Case study at Corporate Telecom Sector

experiments, it is observed that CMAR is constituent & highly effective in terms of memory requirements, time complexity. On the analysis of the existing Data mining Techniques in computer Relation Management, different predictive & descriptive techniques such as Association rule, Clustering & Classification analyzed and also described how these techniques were used to improve the customer relationship with the company which leads a considerable profit. “Optimizing the CRM process in Product Market place by Association Rule Mining Technology” paper where, author stated that optimized the CRM process by three steps of business practice “Measure”, ”Product” &”act” as a cycle of CRM by continuously improving by effective tools like Association rule Mining [2]. In the paper “Data mining Techniques for CRM”, where author analyzed the current scenario where companies interact with their customers changed dramatically over a past few years. As a result companies have found that they need to understand their customers better & to quick respond to their wants & needs in time frame [15]. Paper of [3] i.e.” Proposed ranking for point of sales using data mining for Telecomm operations” helps Telecomm companies in making decisions that optimize the sales points to reduce costs and also to identify profitable customers and churn one.“Customer churns prediction in Telecommunication Industry using Data mining technique-A Review” paper predicts that Customer churn prediction is a foremost feature of a contemporary telecom CRM system. Churn prediction prototype guides the customer’s relationship management to retain the customers who are probable to quit [8]. In recent epochs no. of supervised data mining techniques & classification are used to model the churn
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DATA MINING STRATEGIES AND TECHNIQUES FOR CRM SYSTEMS

DATA MINING STRATEGIES AND TECHNIQUES FOR CRM SYSTEMS

Abstract –Whenever millions of data is being stored in database regularly, data mining is responsible to discover the hidden knowledge, rules and patterns from it. Data mining is going to be involved in every organization for extracting extra information which are not visible for everyone. Organizations always planning to get useful information from it. Though, study on customer relationship management (CRM) is reaching more practical and attractive factor for the growth of every organization in the same way, discovery the hidden gold is also supporting to achieve the goal and for the success of organization. The main critical success factor for any (CRM) includes, Marketing Management, Customer Support Management, Sales Management and Facilities Management, etc. In this paper we proposed, analyzed and validated that data mining is also a major success factor in the success of CRM. We first presented the CRM model and then explained the main role of each feature, then we add data mining feature in the CRM model. Further more, we applied data mining strategies and techniques for the generation of new rules and patterns. We talk about that within the boundaries of CRM strategies the data mining tool also play an affective and valuable role for the establishment and growth of the organization.
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Application of Data Mining and CRM in Banking Sector Medical Insurance

Application of Data Mining and CRM in Banking Sector Medical Insurance

The purpose of this paper aims to present how data mining techniques can be implemented through customer relationship management (CRM) is useful in banking sector insurance domain. This is a dual approach. Health insurance through bank is a new concept; initially it was not that popular due to lack of advertisement and awareness. Slowly but gradually it becomes popular among the customers. Like insurance companies bank need not to run after the customers, they already have a steady and large customer data base. The goal is selection of customer and proper approach. Data mining process can be modeled virtually any customer; if the necessary information exists in a data base. In order to gather appropriate information for data mining technique, we have used segmentation. Customers are segmented on the basis of their status in the bank. Which include their income, bank transaction, amount deposited, age, sex and social responsibilities (Demographic-segmentation). Here we have used CRM strategies to identify valued customer. Once the customers are selected clustering is used to find pattern among these customers. In case of implementation of CRM most of the organizations give importance to marketing and business strategies only, but our approach is different, we proposed that data mining is also a winning factor of CRM. We have applied data mining techniques on few part of CRM, which give us more appropriate result for the banking sector insurance field.
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Using Microsoft Dynamics CRM for Analytical CRM: A Curriculum Package for Business Intelligence or Data Mining Courses

Using Microsoft Dynamics CRM for Analytical CRM: A Curriculum Package for Business Intelligence or Data Mining Courses

Abstract: The purpose of this curriculum package attempts to show how to use the Microsoft SQL Server Business Intelligence Development Studio to access Microsoft Dynamics CRM and conduct a data mining and analysis. First, students use the Dynamics CRM 2013 to input sales data (lead and opportunity). Second, students use SQL Server Business Intelligence Development Studio to analyze the data to find out the relationship between lead and opportunity. The mining models used in the assignment are: decision tree, clustering, Naïve Bayes, and logistic regression. Finally, students can decide which mining mode is good and what customers have the highest possibility to transfer from lead phase to opportunity phase.
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Survey of Data Mining Tools and Techniques for Customer Relationship Management (CRM)

Survey of Data Mining Tools and Techniques for Customer Relationship Management (CRM)

There are many data mining tools available in the market. Nisbet (2004) pointed out that there are no best tool as each tool has its strengths and weaknesses; each tool may be the best for particular needs in particular companies. Yet the most persistent data mining tools used for the CRM are SAS, SPSS, ORACLE and Insightful miner (2007). They give various features to its user that helps companies to achieve financial gain. These tools can be categorized in following categories[6].

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Relating Technology Based CRM to service quality in the telecommunications Industry in Arusha City, Tanzania

Relating Technology Based CRM to service quality in the telecommunications Industry in Arusha City, Tanzania

. The second step is differentiation whereby a company has to differentiate their customers by identifying which customers have most value now and which offer most for the future. The third step is interaction, where by a company interacts with customers to ensure that customers’ expectations are well understood, this step is crucial because it is where company gets a chance to relate and work together with customers through customer service touch points such as call centers and points of sale. In the case of mobile phone companies, this step is crucial too and has a positive impact on the level of service quality and signifies that customer service and service quality relate positively. The last step in building relationship with customers is customization which requires a company to customize its offers and communications to ensure that expectations of customers are met. Thus, through the findings of hypothesis four, it is observed that telecommunications companies in Tanzania have installed a technology based CRM system that supports the integration of business activities and processes to ensure service quality to its customers.
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Data Mining in E Commerce: A CRM Platform

Data Mining in E Commerce: A CRM Platform

This is critical for running an e-commerce business, but is not appropriate for analysis [18, 19], which usually requires full scans of several very large tables and a star schema design which business users can understand. Dimensional modeling techniques can be used to build data warehouse for the purpose of data mining. Data transfers to and from OLTP systems could be complex and time-consuming. But the complexity can be reduced making the data warehouse the integral part of the system. However the bridges support the import and integrations of data from both external systems and data providers. The analytical results are actionable at the last bridge, Deploy Results. It provides the ability to transfer models, scores, results and new attributes constructed back into the Business Data Definition and Customer Interaction components for personalization in business rules. For instance, a customer can have a site personalized for the score he gained for his inclination to accept a cross- sell. This could be categorically tough to get implemented in a non-integrated system. However the results are directly reflected on the company’s e-commerce business when the metadata are shared across the three components deploying the best results in a CRM environment.
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The Confluence of Data Mining and Market Research for Smarter CRM April 17, 2003

The Confluence of Data Mining and Market Research for Smarter CRM April 17, 2003

Data Mining is used to help improve customer-acquisition efforts by identifying the profile of potential buyers for a particular product or responders to a campaign. While these derived profiles can lead to improvements in marketing efforts, one can only infer the reasons these groups respond where others do not. With Market Research one can survey customers to understand why they buy a particular product or respond to a specific campaign. Used together, Data Mining and Market Research can provide more actionable results in a more efficient manner. Specifically, Data Mining can identify customer segments to survey and provide hypotheses as to purchase intent and Market Research can narrow field work to a tighter segment and more focused research objective.
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Data Mining of CRM dataset using GAF Algorithm

Data Mining of CRM dataset using GAF Algorithm

A CRM is what allows you to collate that data in one simple view – a dashboard, if you like. It allows you to create processes around the data. For example, you may want to know when a specific customer spends more than £1,000, and create an alert that says “get in touch”. You may want to create an alert around customers who stop spending, so that you can get in touch and revive their engagement with you. You’ve taken the manual deep-dive into a database and made it automatic, intelligent and business-driven.

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Exploiting network topology in mining sequential patterns from telecommunications alarm data

Exploiting network topology in mining sequential patterns from telecommunications alarm data

connected nature of the network means that a single fault may produce a cascade of alarms from affected network el- ements. Conversely, intermittent, self-clearing alarms may be raised without any attendant fault in the network. Event correlation provides a means of dealing with this large vol- ume of alarm data. Correlations define relations between alarm events that facilitate the process of alarm filtering, masking and prioritisation. While sequential data-mining techniques have evolved to identify possible useful correla- tions in alarm data, the task of identifying the subset of important and plausible correlations remains heavily depen- dent on the domain expertise of network equipment manu- facturers and operators. On the other hand, alarm data encodes substantial domain knowledge which may be ex- ploited to improve the mining process. In particular, indi- vidual alarms contain topographical information identifying the network elements which generated them. This paper ad- dresses the challenge of harnessing this latent domain knowl- edge in order to provide criteria for automatically evaluating the plausibility of mined alarm correlations.
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Customer Relationship Management using Adaptive Resonance Theory

Customer Relationship Management using Adaptive Resonance Theory

according to the needs and behaviors of the customers. In the classification problem, we’re predicting to what category something fall into. In regression problem, we’re predicting a number such as the probability that a person will respond to an offer or not. In the CRM, the data mining is frequently used to identify a set of characteristics (called a profile) that segments customers into groups with similar behaviors, such as buying a similar product. The data warehouse, OLAP and other data analytic tools are applied to the database, for example sale database, marketing database and customer database. It will not only be waste of time but also not ideal. Therefore, it is wise that the data of business database that lead to data warehouse after was cleaned, extracted, transformed; loaded the data of data warehouse will offer the data resource for the application of data mining. The data analysis will become great efficient based on the data warehouse. The architecture of CRM [1] based on Data Mining is as follows:
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Modeling and Analysis of Customer-Product Relationship for Revenue Growth of Online Telecom Industry

Modeling and Analysis of Customer-Product Relationship for Revenue Growth of Online Telecom Industry

Customer segmentation is one of the most important data mining methodologies used in marketing and CRM .It helps telecommunications companies to discover the characteristics of their customers. Customer segmentation is grouping similar customers together, based on many different criteria. In this way it is possible to target each and every group depending on their characteristics. A more appropriate term to use is clustering. Clustering is a good way to analyses large and complex set of data. Since each cluster provides a description, the analyst can understand the nature of the problem better. The data to be mined usually includes:
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Data Mining for Banking and Finance

Data Mining for Banking and Finance

In CRM, data mining is frequently used to assign a score to a particular customer or prospect indicating the likelihood that the individual will behave in a particular way. For example, a score could measure the propensity to respond to a particular insurance or credit card offer or to switch to a competitor’s product.Data mining can be useful in all the three phases of a customer relationship- cycle: customer acquisition, increasing value of the customer and customer retention. For example, a typical banking firm let say sends 1 million direct mails for credit card customer acquisition. Past researches2 have shown that typically 6% of such target customers respond to these direct mails. Banks use their credit risk models to classify these respondents in good credit risk and bad credit risk classes. The propor tion of good credit r isk respondents is only 16% out of the total respondents. So, as net result, roughly only 1% of the total targeted customers are converted into the credit card customers through direct mailing. Seeing the huge cost and effort involved in such marketing process, data mining techniques can significantly improve the customer conversion rate by more focused marketing. Using a predictive test model using decision tree techniques like CHAID (Chi-squared Automatic Interaction Detection), CART (Classification And Regression Trees), Quest
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Big Data in Customer Relationship Management (CRM)

Big Data in Customer Relationship Management (CRM)

- Big Data companies - CRM systems developers - Governmental organizations - Telecommunications companies - Analytics and data reporting companies - Data storage and processing companies - Research and development organizations - Cloud infrastructure and service providers - Wireless and online CRM application developers - Retail and wholesale products and services companies

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Survey and Challenges of Mining Customer Data Sets to Enhance Customer Relationship Management

Survey and Challenges of Mining Customer Data Sets to Enhance Customer Relationship Management

This paper has demonstrates the data mining techniques and tools that used in aCRM. In addition, it reviews categories, dimensions and components of any CRM. CRM new trends such as sCRM and iCRM have been declared as most of researches are going forwards towards the social media and the intelligent areas. The suitable data mining techniques to be used through each CRM dimension have been clearly identified within a detailed table that has been constructed from previous researches. CRM components have been clearly presented with its effects on the customer’s behavior during the customer life cycle. This paper shows a list of free available customer’s data set could be used in mining. Finally it lists the challenges of mining customer data from business and aCRM perspectives. For the future work the researchers are intended to conduct an experiment using different classification algorithms to achieve an enhancement in CRM’s customer identification tasks. The classification algorithms more fit the research problem, as the available attributes in the data set make it a good base for CRM identification, attraction and retention. So the researcher decided to apply the research on the first task which is target customer analysis. This may be a good start to complete the whole CRM cycle on this data set in the future.
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Data Mining Techniques for Banking Applications

Data Mining Techniques for Banking Applications

Abstract: Financial segment is the core division to decide the country’s gross domestic product ( GDP). The considerable and constant development of any country is based on the financial strength of the country. Last few decades observered the increase of financial reforms, liberalization and globalization of Indian financial system coupled with rapid revolution in information technology (IT).The paper presents the advantages of applying data warehousing and data mining (DWDM) techniques in customer relationship management (CRM) of the financial divisions like banking. It is a procedure of analyzing the data from various perceptions and summarizing it into precious information. Data mining ( DM) aids the banks to look for unknown pattern in a group and determine unknown relationship in the data. These methods facilitate useful data analysis for the banking division to avoid customer harassment. And also fraud is an important problem in banking domain. Identifying and preventing fraud is hard, because fraudsters develop new techniques all the time, and the techniques grow more and more difficult to avoid easy finding.
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Modeling and Analysis of Customer Product Relationship for Revenue Growth of Online Telecom Industry

Modeling and Analysis of Customer Product Relationship for Revenue Growth of Online Telecom Industry

Customer segmentation is one of the most important data mining methodologies used in marketing and CRM .It helps telecommunications companies to discover the characteristics of their customers. Customer segmentation is grouping similar customers together, based on many different criteria. In this way it is possible to target each and every group depending on their characteristics. A more appropriate term to use is clustering. Clustering is a good way to analyses large and complex set of data. Since each cluster provides a description, the analyst can understand the nature of the problem better. The data to be mined usually includes:
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The Probability of Predicting E-Customer's Buying Pattern Based on Personality Type

The Probability of Predicting E-Customer's Buying Pattern Based on Personality Type

ABSTRACT: Traditional questionnaires, Customer Relationship Management (CRM), neuroscience, data mining, web mining and web usage mining all came to help marketers to discover the reason WHY customers purchase a product. It has been a controversial marketing topic over the past decades. From buying face-to-face in a retail store to online shopping in Amazon, different fields of science have been involved in this area. Not only marketers have been utilizing marketing and business management science, but also computer science and human computer interaction have been used to predict and analyze the customer’s behaviour in order to maximize the purchase. In this paper, psychological aspect of customer’s behaviour was introduced that has not been considered in previous research.
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