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This document discusses data mining techniques for customer relationship management (CRM). It defines data mining as the extraction of implicit and novel knowledge from large datasets. The document outlines common data mining applications in retail, banking, telecommunications and other industries. It also discusses how data mining can be used across different stages of the customer lifecycle in CRM, such as up-selling, cross-selling and customer retention. Finally, it provides an overview of common predictive and descriptive data mining techniques like decision trees, rule induction, clustering and association rule mining.
Data Mining Concepts with Customer Relationship ManagementIJERA Editor
Data mining is important in creating a great experience at e-business. Data mining is the systematic way of extracting information from data. Many of the companies are developing an online internet presence to sell or promote their products and services. Most of the internet users are aware of on-line shopping concepts and techniques to own a product. The e-commerce landscape is the relation between customer relationship management (sales, marketing & support), internet and suppliers.
The document discusses how big data is creating challenges for retailers in providing a unified view of customers and products across channels in real-time. It finds that 70% of retailers grapple with at least 8 disparate data sources, making analysis difficult. While real-time insights could improve operations, only a third of retailers currently share cross-channel customer and product data. The document provides recommendations to help retailers better utilize big data.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
Cross Selling Through Database MarketingAnkit Gupta
This document proposes a new statistical model called a mixed data factor analyzer to help companies better predict which existing customers would be interested in purchasing additional products or services from the company (cross-selling). The model combines transaction data about customers' purchase histories from a company's database with survey data from a sample of customers. It can handle different types of data, like binary, count, and rating data. The model is tested on transaction and survey data from a large bank. It is shown to more accurately predict customers' ownership of different financial services compared to an alternative model. The goal is to help companies identify the best prospects for cross-selling each service in order to increase customer retention and profits.
Module 2 - Improving current business with your own data - Online caniceconsulting
The document discusses how companies can improve their current business using their own internal data. It provides tips on locating internal data sources within a company, implementing data enrichment, and using data to build a company's brand. The key internal data sources discussed include transactional data, customer relationship management systems, internal documents/archives, and data from other business applications and device sensors. Data enrichment is presented as an important part of big data projects, to integrate and extract more value from existing data.
Analysis of Sales and Distribution of an IT Industry Using Data Mining Techni...ijdmtaiir
The goal of this work is to allow a corporation to
improve its marketing, sales, and customer support operations
through a better understanding of its customers. Keep in mind,
however, that the data mining techniques and tools described
here are equally applicable in fields ranging from law
enforcement to radio astronomy, medicine, and industrial
process control. Businesses in today’s environment
increasingly focus on gaining competitive advantages.
Organizations have recognized that the effective use of data is
the key element in the next generation is to predict the sales
value and emerging trend of technology market. Data is
becoming an important resource for the companies to analyze
existing sales value with current technology trends and this
will be more useful for the companies to identify future sales
value. There a variety of data analysis and modeling techniques
to discover patterns and relationships in data that are used to
understand what your customers want and predict what they
will do. The main focus of this is to help companies to select
the right prospects on whom to focus, offer the right additional
products to company’s existing customers and identify good
customers who may be about to leave. This results in improved
revenue because of a greatly improved ability to respond to
each individual contact in the best way and reduced costs due
to properly allocated resources. Keywords: sales, customer,
technology, profit.
This document discusses data mining techniques for customer relationship management (CRM). It defines data mining as the extraction of implicit and novel knowledge from large datasets. The document outlines common data mining applications in retail, banking, telecommunications and other industries. It also discusses how data mining can be used across different stages of the customer lifecycle in CRM, such as up-selling, cross-selling and customer retention. Finally, it provides an overview of common predictive and descriptive data mining techniques like decision trees, rule induction, clustering and association rule mining.
Data Mining Concepts with Customer Relationship ManagementIJERA Editor
Data mining is important in creating a great experience at e-business. Data mining is the systematic way of extracting information from data. Many of the companies are developing an online internet presence to sell or promote their products and services. Most of the internet users are aware of on-line shopping concepts and techniques to own a product. The e-commerce landscape is the relation between customer relationship management (sales, marketing & support), internet and suppliers.
The document discusses how big data is creating challenges for retailers in providing a unified view of customers and products across channels in real-time. It finds that 70% of retailers grapple with at least 8 disparate data sources, making analysis difficult. While real-time insights could improve operations, only a third of retailers currently share cross-channel customer and product data. The document provides recommendations to help retailers better utilize big data.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
Cross Selling Through Database MarketingAnkit Gupta
This document proposes a new statistical model called a mixed data factor analyzer to help companies better predict which existing customers would be interested in purchasing additional products or services from the company (cross-selling). The model combines transaction data about customers' purchase histories from a company's database with survey data from a sample of customers. It can handle different types of data, like binary, count, and rating data. The model is tested on transaction and survey data from a large bank. It is shown to more accurately predict customers' ownership of different financial services compared to an alternative model. The goal is to help companies identify the best prospects for cross-selling each service in order to increase customer retention and profits.
Module 2 - Improving current business with your own data - Online caniceconsulting
The document discusses how companies can improve their current business using their own internal data. It provides tips on locating internal data sources within a company, implementing data enrichment, and using data to build a company's brand. The key internal data sources discussed include transactional data, customer relationship management systems, internal documents/archives, and data from other business applications and device sensors. Data enrichment is presented as an important part of big data projects, to integrate and extract more value from existing data.
Analysis of Sales and Distribution of an IT Industry Using Data Mining Techni...ijdmtaiir
The goal of this work is to allow a corporation to
improve its marketing, sales, and customer support operations
through a better understanding of its customers. Keep in mind,
however, that the data mining techniques and tools described
here are equally applicable in fields ranging from law
enforcement to radio astronomy, medicine, and industrial
process control. Businesses in today’s environment
increasingly focus on gaining competitive advantages.
Organizations have recognized that the effective use of data is
the key element in the next generation is to predict the sales
value and emerging trend of technology market. Data is
becoming an important resource for the companies to analyze
existing sales value with current technology trends and this
will be more useful for the companies to identify future sales
value. There a variety of data analysis and modeling techniques
to discover patterns and relationships in data that are used to
understand what your customers want and predict what they
will do. The main focus of this is to help companies to select
the right prospects on whom to focus, offer the right additional
products to company’s existing customers and identify good
customers who may be about to leave. This results in improved
revenue because of a greatly improved ability to respond to
each individual contact in the best way and reduced costs due
to properly allocated resources. Keywords: sales, customer,
technology, profit.
The document discusses customer relationship management and data mining. It provides an overview of the data mining process and applications of data mining such as reducing churn, increasing customer profitability, and reducing marketing costs. It also discusses two case studies of companies that used data mining for CRM: Credite Est, a bank in France that used it to acquire new customers, and Yapi Kredi, a bank in Turkey that used it to reduce churn.
Customer Churn Prediction using Association Rule Miningijtsrd
Customer churn is one of the most important metrics for a growing business to evaluate. It is a business term used to describe the loss of clients or customers. In the retail sales and marketing company, customers have multiple choices of services and they frequently switch from one service to another. In these competitive markets, customers demand best products and services at low prices, while service providers constantly focus on getting hold of as their business goals. An increase in customer retention of just 5 can create at least a 25 increase in profit. Therefore, customer churn rate is important because it costs more to acquire new customers than it does to retain existing customers. In this paper, we apply the method to the retail sales and marketing company customer churn data set. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It will help the retail sales and marketing company to present the targeted customers with the estimated loss of clients or customers for the promotion in direct marketing. Mie Mie Aung | Thae Thae Han | Su Mon Ko "Customer Churn Prediction using Association Rule Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26818.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26818/customer-churn-prediction-using-association-rule-mining/mie-mie-aung
SPSS is a leading provider of predictive analytics software, services, and solutions. It has 40 years of experience and 250,000 customers worldwide. SPSS uses statistics, data mining, and other advanced analytical techniques to analyze data, understand customers and other groups, predict future events, and help customers make better decisions.
The document discusses using data mining techniques in e-commerce. It provides an introduction to data mining and e-commerce, describing common data mining tasks like classification, clustering, and association rule mining. The document outlines the basic data mining process and some popular data mining tools. It explains how data mining can be used in e-commerce for applications like customer profiling, personalization, basket analysis, sales forecasting, and market segmentation. The advantages of using data mining in e-commerce are also summarized.
Information technologies & Analytics for Telcos & ISPsGeorge Krasadakis
Datamine is a Greek analytics company founded in 2005 that provides data-driven solutions like CRM, business intelligence, and customer loyalty programs. It has a team with experience in statistics, data mining, and telecom/banking. Major customers include Greek banks and telecom companies for projects involving campaign management, data warehousing, customer risk assessment, and more. Datamine offers services around data preparation, modeling, reporting, and analytical applications to help customers better understand their data and customers.
Consumer analytics is the process businesses adopt to capture and analyze customer data to make better business decisions via predictive analytics. It is a method of turning data into deep insights to predict customer behavior. It may also be regarded as the process by which data can be turned into predictive insights to develop new products, new ways to package existing products, acquire new customers, retain old customers, and enhance customer loyalty. It helps businesses break big problems into manageable answers. This paper is a primer on consumer analytics. Matthew N. O. Sadiku | Sunday S. Adekunte | Sarhan M. Musa "Consumer Analytics: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33511.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/33511/consumer-analytics-a-primer/matthew-n-o-sadiku
This document discusses customer data clustering using data mining techniques to identify high-profit, low-risk customers. It begins with an abstract describing how classification and pattern extraction from customer data is important for business decision making. It then discusses using demographic clustering algorithms on customer data from a retail store to identify valuable customer clusters, focusing on a cluster that represents 10-20% of customers but yields 80% of revenue. The document outlines the two phase clustering process of data cleansing followed by cluster generation and profiling to find the best clusters. It then describes experiments using IBM Intelligent Miner to cluster the retail store customer transaction data using demographic clustering and analyzes the results.
A Study on 21st Century Business Intelligence Anit Thapaliya
This document provides an analysis of business intelligence (BI). It begins with an introduction that defines BI and discusses its goals and benefits. Section 2 provides background on BI, including its history and factors influencing it. Section 3 contains an analysis, including advantages and disadvantages of BI, examples from companies like Dell and Walmart, and how tools from Microsoft and social media platforms have impacted revenue. The conclusion discusses the future of BI and trends like real-time analytics and increased user access to information.
Business Intelligence for Consumer Goods CompaniesCognizant
Despite the focus that the Consumer Goods industry places on business intelligence and data insights, not many companies are truly leveraging this valuable resource to its full potential.
1. Business users gain insights from activity-based costing (ABC) information on which products, services, channels and customers are relatively more or less profitable. However, ABC alone does not provide sufficient insight into what differentiates highly profitable from less profitable customers.
2. Data mining and advanced analytics techniques like decision trees and recursive partitioning can identify the key drivers that best explain differences in profitability between high-profit and low-profit customers. Knowing these drivers can guide actions to increase profit lift from customers.
3. The paper describes how these analytical techniques were applied to determine differentiating characteristics, like customer location, that correlated with profitability levels and provided guidance on targeted marketing and sales strategies.
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
Capturing Data Relationships to Develop Meaningful Customer EngagementPrecisely
In an omnichannel world, organizations struggle to gain complete, 360-degree views of engagement with current and potential customers. Organizations are digitally transforming business processes and customers’ engagement preferences are changing due to upheavals such as the coronavirus pandemic, so traditional data management cannot deliver a complete view. To get anywhere close, organizations have to spend valuable time and resources knitting together numerous data silos and dealing with complicated replication and redundant data preparation. They must lean on specialists who can code and model routines that should be part of data management.
It’s time to tap innovations in data management such as graph databases and geolocation intelligence to gain faster, easier, and more complete views of customer engagement. Organizations need to reduce friction in how they find, connect, and share customer data points, and they need to evaluate how nontraditional data management can help.
Join this TDWI Webinar to learn how you can take advantage of innovations to drive smarter personalization, targeted marketing across channels, and more satisfying customer engagement.
Topics to be discussed include:
- Common pain points organizations are facing in trying to gain 360-degree views of customer engagement and how to overcome them with innovative data management
- Graph databases: how they can improve views of data relationships, enhance customer analytics, and take burdens off data scientists, analysts, and users
- Important trends in unifying data about customers and their behavior, including graph databases, geolocation intelligence, master data management, and semantic data integration
- Governance, security, and customer data privacy: how graph databases and related innovations can help
Subscriber Data Mining in TelecommunicationNarayan Kandel
This document outlines a project to perform subscriber data mining on telecommunication data for business reporting and decision making. The project aims to perform customer segmentation, new campaign planning, customer relationship management, and churn prediction. It involves collecting call detail records, preprocessing the data, designing a data mart and OLAP cube, applying clustering and other data mining algorithms, and visualizing the results. The goals are to help telecommunications companies better understand customer behaviors and improve customer retention through targeted campaigns.
INTEGRATION OF MACHINE LEARNING TECHNIQUES TO EVALUATE DYNAMIC CUSTOMER SEGME...IJDKP
The telecommunications industry is highly competitive, which means that the mobile providers need a
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
IMPLICATIONS OF MARKETING INFORMATION SYSTEMLibcorpio
MARKETING MANAGEMENT, MARKETING INFORMATION SYSTEM, MKIS, NEED OF THE MARKETING INFORMATION SYSTEM, COMPONENTS OF MKIS, ADVANTAGES & DISADVANTAGES MKIS, FUNCTIONS OF MKIS, ORGANIZATION STUDY, LIBCORPIO786, BUSINESS ADMINISTRATION, MANAGEMENT SCIENCE, EDUCATION AND LEARNING,
The document discusses the importance of marketing metrics and analytics for building accountability and respect within an organization. It argues that marketing should measure metrics that matter to executives like revenue, profits, and growth. The document also emphasizes that marketing should plan for ROI from the start of a program by establishing goals and estimating ROI, designing measurable programs, and focusing on decisions that can improve marketing performance.
Acquire Grow & Retain customers - The business imperative for Big DataIBM Software India
The emergence of Big Data and Analytics has changed the way marketing decisions are made. Marketing has moved away from traditional ‘generalisation’ practices such as customer segmentation, geographical targeting etc. and is focussing more on the individual – the ‘Chief Executive Customer’.
This document provides an overview of HR analytics and business analytics. It defines HR analytics as using analytical processes and data to improve employee performance and retention. Business analytics involves collecting, analyzing, and modeling business data to gain insights. The document discusses the evolution of business analytics from operations research during WWII to modern tools like Google Analytics. It also covers the scope, advantages, and challenges of business analytics, as well as its applications in different business domains like finance, e-commerce, and aviation.
O relatório monitorou o termo "#naovaitercopa" no Twitter entre 18 e 24 de janeiro de 2014, encontrando 11.832 tweets. Nenhum tweet foi classificado como positivo, negativo ou importante. Uma nuvem de termos associados e uma timeline geral também foram fornecidas.
The document discusses customer relationship management and data mining. It provides an overview of the data mining process and applications of data mining such as reducing churn, increasing customer profitability, and reducing marketing costs. It also discusses two case studies of companies that used data mining for CRM: Credite Est, a bank in France that used it to acquire new customers, and Yapi Kredi, a bank in Turkey that used it to reduce churn.
Customer Churn Prediction using Association Rule Miningijtsrd
Customer churn is one of the most important metrics for a growing business to evaluate. It is a business term used to describe the loss of clients or customers. In the retail sales and marketing company, customers have multiple choices of services and they frequently switch from one service to another. In these competitive markets, customers demand best products and services at low prices, while service providers constantly focus on getting hold of as their business goals. An increase in customer retention of just 5 can create at least a 25 increase in profit. Therefore, customer churn rate is important because it costs more to acquire new customers than it does to retain existing customers. In this paper, we apply the method to the retail sales and marketing company customer churn data set. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It will help the retail sales and marketing company to present the targeted customers with the estimated loss of clients or customers for the promotion in direct marketing. Mie Mie Aung | Thae Thae Han | Su Mon Ko "Customer Churn Prediction using Association Rule Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26818.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26818/customer-churn-prediction-using-association-rule-mining/mie-mie-aung
SPSS is a leading provider of predictive analytics software, services, and solutions. It has 40 years of experience and 250,000 customers worldwide. SPSS uses statistics, data mining, and other advanced analytical techniques to analyze data, understand customers and other groups, predict future events, and help customers make better decisions.
The document discusses using data mining techniques in e-commerce. It provides an introduction to data mining and e-commerce, describing common data mining tasks like classification, clustering, and association rule mining. The document outlines the basic data mining process and some popular data mining tools. It explains how data mining can be used in e-commerce for applications like customer profiling, personalization, basket analysis, sales forecasting, and market segmentation. The advantages of using data mining in e-commerce are also summarized.
Information technologies & Analytics for Telcos & ISPsGeorge Krasadakis
Datamine is a Greek analytics company founded in 2005 that provides data-driven solutions like CRM, business intelligence, and customer loyalty programs. It has a team with experience in statistics, data mining, and telecom/banking. Major customers include Greek banks and telecom companies for projects involving campaign management, data warehousing, customer risk assessment, and more. Datamine offers services around data preparation, modeling, reporting, and analytical applications to help customers better understand their data and customers.
Consumer analytics is the process businesses adopt to capture and analyze customer data to make better business decisions via predictive analytics. It is a method of turning data into deep insights to predict customer behavior. It may also be regarded as the process by which data can be turned into predictive insights to develop new products, new ways to package existing products, acquire new customers, retain old customers, and enhance customer loyalty. It helps businesses break big problems into manageable answers. This paper is a primer on consumer analytics. Matthew N. O. Sadiku | Sunday S. Adekunte | Sarhan M. Musa "Consumer Analytics: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33511.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/33511/consumer-analytics-a-primer/matthew-n-o-sadiku
This document discusses customer data clustering using data mining techniques to identify high-profit, low-risk customers. It begins with an abstract describing how classification and pattern extraction from customer data is important for business decision making. It then discusses using demographic clustering algorithms on customer data from a retail store to identify valuable customer clusters, focusing on a cluster that represents 10-20% of customers but yields 80% of revenue. The document outlines the two phase clustering process of data cleansing followed by cluster generation and profiling to find the best clusters. It then describes experiments using IBM Intelligent Miner to cluster the retail store customer transaction data using demographic clustering and analyzes the results.
A Study on 21st Century Business Intelligence Anit Thapaliya
This document provides an analysis of business intelligence (BI). It begins with an introduction that defines BI and discusses its goals and benefits. Section 2 provides background on BI, including its history and factors influencing it. Section 3 contains an analysis, including advantages and disadvantages of BI, examples from companies like Dell and Walmart, and how tools from Microsoft and social media platforms have impacted revenue. The conclusion discusses the future of BI and trends like real-time analytics and increased user access to information.
Business Intelligence for Consumer Goods CompaniesCognizant
Despite the focus that the Consumer Goods industry places on business intelligence and data insights, not many companies are truly leveraging this valuable resource to its full potential.
1. Business users gain insights from activity-based costing (ABC) information on which products, services, channels and customers are relatively more or less profitable. However, ABC alone does not provide sufficient insight into what differentiates highly profitable from less profitable customers.
2. Data mining and advanced analytics techniques like decision trees and recursive partitioning can identify the key drivers that best explain differences in profitability between high-profit and low-profit customers. Knowing these drivers can guide actions to increase profit lift from customers.
3. The paper describes how these analytical techniques were applied to determine differentiating characteristics, like customer location, that correlated with profitability levels and provided guidance on targeted marketing and sales strategies.
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
Capturing Data Relationships to Develop Meaningful Customer EngagementPrecisely
In an omnichannel world, organizations struggle to gain complete, 360-degree views of engagement with current and potential customers. Organizations are digitally transforming business processes and customers’ engagement preferences are changing due to upheavals such as the coronavirus pandemic, so traditional data management cannot deliver a complete view. To get anywhere close, organizations have to spend valuable time and resources knitting together numerous data silos and dealing with complicated replication and redundant data preparation. They must lean on specialists who can code and model routines that should be part of data management.
It’s time to tap innovations in data management such as graph databases and geolocation intelligence to gain faster, easier, and more complete views of customer engagement. Organizations need to reduce friction in how they find, connect, and share customer data points, and they need to evaluate how nontraditional data management can help.
Join this TDWI Webinar to learn how you can take advantage of innovations to drive smarter personalization, targeted marketing across channels, and more satisfying customer engagement.
Topics to be discussed include:
- Common pain points organizations are facing in trying to gain 360-degree views of customer engagement and how to overcome them with innovative data management
- Graph databases: how they can improve views of data relationships, enhance customer analytics, and take burdens off data scientists, analysts, and users
- Important trends in unifying data about customers and their behavior, including graph databases, geolocation intelligence, master data management, and semantic data integration
- Governance, security, and customer data privacy: how graph databases and related innovations can help
Subscriber Data Mining in TelecommunicationNarayan Kandel
This document outlines a project to perform subscriber data mining on telecommunication data for business reporting and decision making. The project aims to perform customer segmentation, new campaign planning, customer relationship management, and churn prediction. It involves collecting call detail records, preprocessing the data, designing a data mart and OLAP cube, applying clustering and other data mining algorithms, and visualizing the results. The goals are to help telecommunications companies better understand customer behaviors and improve customer retention through targeted campaigns.
INTEGRATION OF MACHINE LEARNING TECHNIQUES TO EVALUATE DYNAMIC CUSTOMER SEGME...IJDKP
The telecommunications industry is highly competitive, which means that the mobile providers need a
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
IMPLICATIONS OF MARKETING INFORMATION SYSTEMLibcorpio
MARKETING MANAGEMENT, MARKETING INFORMATION SYSTEM, MKIS, NEED OF THE MARKETING INFORMATION SYSTEM, COMPONENTS OF MKIS, ADVANTAGES & DISADVANTAGES MKIS, FUNCTIONS OF MKIS, ORGANIZATION STUDY, LIBCORPIO786, BUSINESS ADMINISTRATION, MANAGEMENT SCIENCE, EDUCATION AND LEARNING,
The document discusses the importance of marketing metrics and analytics for building accountability and respect within an organization. It argues that marketing should measure metrics that matter to executives like revenue, profits, and growth. The document also emphasizes that marketing should plan for ROI from the start of a program by establishing goals and estimating ROI, designing measurable programs, and focusing on decisions that can improve marketing performance.
Acquire Grow & Retain customers - The business imperative for Big DataIBM Software India
The emergence of Big Data and Analytics has changed the way marketing decisions are made. Marketing has moved away from traditional ‘generalisation’ practices such as customer segmentation, geographical targeting etc. and is focussing more on the individual – the ‘Chief Executive Customer’.
This document provides an overview of HR analytics and business analytics. It defines HR analytics as using analytical processes and data to improve employee performance and retention. Business analytics involves collecting, analyzing, and modeling business data to gain insights. The document discusses the evolution of business analytics from operations research during WWII to modern tools like Google Analytics. It also covers the scope, advantages, and challenges of business analytics, as well as its applications in different business domains like finance, e-commerce, and aviation.
O relatório monitorou o termo "#naovaitercopa" no Twitter entre 18 e 24 de janeiro de 2014, encontrando 11.832 tweets. Nenhum tweet foi classificado como positivo, negativo ou importante. Uma nuvem de termos associados e uma timeline geral também foram fornecidas.
Ligflat - FlatBlue - Media Empresa - Conta ControladaLigFlat Telecom
O documento descreve um estudo de caso de redução de custos com telefonia fixa e celular em uma empresa médio porte no Brasil. A solução proposta, chamada FlatBlue, oferece linhas SIP ilimitadas para ligações fixas e celulares por um preço fixo mensal, resultando em uma redução estimada de 32-54% nos custos totais com telefonia da empresa.
The document is a resume for A.H.M. Rezuanal Haque summarizing his career objective and experience. He has over 8 years of experience working in sales and administration roles for Airtel Bangladesh Ltd and is seeking a position where he can contribute his skills and professionalism. His experience includes communicating with distributors, managing sales reports, and handling facility operations and maintenance responsibilities. He holds a Master's degree in Commerce and is proficient in Microsoft Office, databases, and communication in both Bangla and English.
A study of Data Mining concepts used in Customer Relationship Management (CRM...IJSRD
Customer relationship management (CRM) has evolved as an approach based on generating positive relationships with customers, increasing customer loyalty, and expanding customer lifetime value [1]. To understand the needs of customers and providing value-added services are recognized as factors that regulate the success or failure of the organizations. In the recent years, technology enhancement made customer relationship easier in various fields such as marketing, sales, service and Management Information Technology [2]. To deliver customer value, there are concepts such as data mining and data warehousing with the use of technology. Even through data mining concepts, organizations can easily find out their valuable customers and helps in making better decisions. There are data mining tools which answer business questions that were time-consuming consuming in the past. These tools simplify these questions and make customer relationship management effective [3]. This researcher work is focused on understanding the consumer’s behavior for themed weddings. The themed weddings management strategies are based on technology, business and customer perspectives. The customer preferences are measured using Regency, Frequency and Monetary (RFM) method. Business strategies are defined to understand the customer preference towards themed weddings management and the technologies such as WEB 2.0 and data mining tool Weka are used. The survey technique, and thematic content analysis using data mining tools, to accomplish the goals of today’s customer relationship management philosophy for themed weddings management.
This document discusses shifting paradigms in strategic customer relationship management (SCRM). SCRM systems must do more than just track customer interactions - they must analyze information, spot trends, and enable sales forces. Next-generation SCRM integrates customer data throughout the entire organization, from sales and marketing to engineering and operations. It provides benefits like improved communication, identifying key accounts, and turning data into actionable insights. SCRM must be designed around collaboration and information sharing across departments to facilitate real-time customer insights. Today's approach to innovation and value creation is driven by customer co-creation and intimacy, requiring SCRM solutions to manage broader customer relationships and interactions.
Driven by challenges on competition, rising customer expectation and shrinking
margins, banks have been using technology to reduce cost. Apart from competitive
environment, there has been deregulation as to rate of interest, technology intensive
delivery channel like Internet Banking, Tele Banking, Mobile banking and Automated
Teller Machines (ATMs) etc have created a multiple choice to user of the bank. The
banking business is becoming more and more complex with the changes emanating from
the liberalization and globalization. For a new bank, customer creation is important, but
an established bank it is the retention is much more efficient and cost effective
mechanism.
- Customer data management is important to understand customers better and improve service, but traditional data management tools create siloed and inconsistent data.
- NexJ Customer Data Management provides an "Enterprise Customer View" that integrates customer data from multiple sources to create a holistic understanding of each customer.
- This unified view of customer data can be used across an organization to drive digital transformation initiatives, enhance customer insights, and meet compliance requirements.
Data mining involves using statistical, machine learning, and artificial intelligence techniques to discover patterns in large data sets. It is a component of business intelligence that helps organizations understand their data, customers, and markets. The key steps in data mining include data preparation, model building, validation, and deployment of results. Data mining is used in applications like customer segmentation, risk management, and fraud detection across industries like banking, retail, and healthcare. It plays an important role in customer relationship management by helping companies better understand customer behavior.
MBA Projects, synopsis, and synopsis of various regular as well as distance learning undergraduate and postgraduate courses for various institutions like SMU – Sikkim Manipal University, SMUDE, AIMA, AMITY, IGNOU, SCDL, JAMIA, AMU, JHU etc.
This document provides a summary of a research report on customer relationship management in the banking sector. It discusses:
1) How CRM has become important for retaining customers and maximizing their lifetime value in the competitive banking industry.
2) The methodologies used in the research project, including a literature review, survey questionnaire, and analysis of customer perceptions of banks' CRM strategies and technologies.
3) The objectives of examining CRM's impact on customer satisfaction and offering suggestions to improve banks' CRM practices.
This document provides an overview of Customer Relationship Management (CRM) in the context of non-banking financial services in India. It discusses how CRM can help automate lending operations, boost sales, improve customer experience and loyalty. However, challenges include creating a unified customer view across multiple systems and products. The document also outlines various CRM techniques used by non-banks like mobile and online banking. It emphasizes the importance of embracing new technologies like artificial intelligence, analytics and cloud-based solutions to gain insights, manage growth and stay compliant with regulations.
This document discusses Customer Relationship Management (CRM) in the context of non-banking financial services. It provides an introduction to CRM and highlights that most institutions take a narrow view of CRM, limiting benefits. A successful CRM strategy incorporates business activities, channel management, relationship management, and back-office/front-office integration within a customer-centric approach. The document then discusses concepts, benefits, challenges and importance of CRM for non-banks. It also covers CRM techniques used by non-banks in India and future trends in CRM.
Employee Performance and CRM Analysis of UCI Dataset using Machine Learning A...IRJET Journal
1. The document discusses a research project analyzing employee performance and customer relationship management (CRM) using a machine learning approach on a dataset from the UCI repository.
2. The project performs CRM analysis on the dataset using techniques like data preprocessing, feature extraction, and applying machine learning algorithms like KNN, linear regression, and random forest.
3. The goal of the project is to better understand how factors like employee performance and turnover impact an organization's economic stability and profits through CRM analysis and machine learning models.
Customer Relationship Management (CRM) is a business strategy focused on developing and managing long-term relationships with customers to increase their lifetime value and profitability for the organization. The philosophy of CRM recognizes that maintaining strong customer relationships provides a competitive advantage through increased customer retention, loyalty, and repeat business. CRM uses customer data, technology, and processes to better understand customer needs and provide personalized customer service across sales, marketing, and service channels.
Data Mining and Business Analytics by Seyed Ziae Mousavi Mojabzmojab
Data mining is a process of discovering patterns in large data sets involving artificial intelligence, machine learning, statistics, and database systems. It can be used to extract valuable knowledge from data sets and predict unknown data by adjusting models. In business, data mining techniques like customer segmentation, behavior prediction, and direct marketing response prediction can be used to increase profits by better understanding customers and targeting the most profitable ones. A typical data mining process includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
A Study on CLTV Model in E Commerce Domains using Pythonijtsrd
Customer Relationship Management CRM system is an information management and analysis tool that can help businesses and other organizations manage their interactions with customers. CRMs were originally designed to target large corporations, but the internet has allowed small business owners to take advantage of these tools as well. Customer data is collected in a CRM database, which allows for advanced analysis such as customer segmentation and contact history. Customer relationship management system CRMs is a process in which a business or other organization administers its interactions with customers, typically using data analysis to study large amounts of information. In this article, we will be explaining how you can a E commerce company can apply their customer relationship management system to analyze their customer base by CLTV, a key marketing metric that allows you to evaluate the impact and outcomes of the firm’s customer relationship management strategies and tactics. In order to increase revenue through better marketing campaigns. E commerce companies consider that customers are their most important asset and that it is essential to estimate the potential value of this asset. Hence, a model for calculating customers value is essential in these domains. We describe a general modeling approach, based on BG NBD and Gamma Gamma models, for calculating customer value in the e commerce domain. This model extends existing models from the field of direct marketing, by taking into account a sample set of variables required for evaluating customers value in an e commerce environment. In addition, we present an algorithm for generating this model from historical data, as well as an application of this modeling approach for the creation of a model for e commerce. This model provides more accurate predictions than existing models regarding the future income generated by customers using Python. Rasamallu Sai Bharath Reddy | Dr. T. Narayana Reddy "A Study on CLTV Model in E-Commerce Domains using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51952.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/51952/a-study-on-cltv-model-in-ecommerce-domains-using-python/rasamallu-sai-bharath-reddy
This document provides an overview of strategic customer relationship management (SCRM). It discusses how SCRM has evolved from basic contact management systems to become a core business strategy. SCRM involves integrating customer data and insights across an organization to better understand customers, identify opportunities, and drive profitable outcomes. The document also introduces the concept of next generation SCRM, which takes a more holistic view of customers by involving all business functions beyond just sales, marketing, and support. It argues next generation SCRM should be a company-wide initiative led from the top-down to realize its full benefits.
This document provides an overview of strategic customer relationship management (SCRM). It discusses how SCRM has evolved from basic contact management systems to become a core business strategy. SCRM involves integrating customer data and insights across an organization to better understand customers, identify opportunities, and drive profitable outcomes. The document also introduces the concept of next generation SCRM, which takes a more holistic view of customers by involving all business functions beyond just sales, marketing, and support. It argues next generation SCRM should be a company-wide initiative led from the top-down to fully realize its benefits.
This document discusses strategic customer relationship management and the evolution of CRM systems. It begins by explaining that while many organizations recognize the value of CRM, many existing systems are just contact managers that don't help analyze customer data or drive profitable outcomes. True strategic CRM integrates customer information throughout an organization to better acquire, develop and retain customers. The document then discusses how CRM has evolved from sales tools to core business elements, and that next generation strategic CRM looks beyond just sales and marketing to involve all parts of a business. It concludes that next generation strategic CRM encompasses the entire business and ties business processes like ERP to individual customers.
Enterprise-Level Preparation for Master Data Management.pdfAmeliaWong21
Master Data Management (MDM) continues to play a foundational role in the Data Management Architecture of every 21st century enterprise. In a forward-looking organization, MDM is significant in the Enterprise Integration Hub.
The document discusses various customer relationship management (CRM) modeling tools. It describes tools for customer database management, data mining, simulation, and SAP CRM. Customer database management software helps analyze customer data to determine investments, craft messages, execute campaigns, and improve retention. Data mining tools can be used for acquisition, optimization, and understanding customer interactions while protecting privacy. Simulation tools enable scenario analysis and collaboration. SAP CRM supports marketing, sales, and service functions to focus on customer-driven growth and differentiation.
EssenceMediacom's report on the evolving role of CMOs shows that alongside an increased budget, marketing leaders expect to expand their teams and 29% will see the most growth in data and technology teams.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
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We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
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HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
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We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
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Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
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This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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1. S.Thiripura Sundari, Dr.A.Padmapriya / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.107-111
Structure Of Customer Relationship Management Systems In
Data Mining
S.Thiripura Sundari*, Dr.A.Padmapriya**
*(Department of Computer Science and Engineering, Alagappa University, Karaikudi-630 003
** (Department of Computer Science and Engineering, Alagappa University, Karaikudi-630 003
ABSTRACT
As a young research field, data mining The center of customer becomes the inevitable choice
has made broad and significant progress. Today, of enterprise management strategy. Therefore, the
data mining is used in a vast array of areas, and profits from customer relationship has become the
numerous commercial data mining systems are blood of all enterprises, and the three basic ways to
available. On the basis of detailed analysis of the increase profits which are obtaining new customers,
existing CRM structure, a new design scheme of increasing the profitability of existing customers and
customer relationship management systems based extending customer relationships have gained wide
on data mining is presented and the design details attention.
of which are illustrated in detail.
II. CRM AND DATA MINING
Keywords –Customer Relationship Management, Since 80s of 20th century, the applications
Data Mining. of various high technologies make it’s difficult to cut
down vast majority of products’ cost. At the same
I. INTRODUCTION time, the tendency of economic globalization makes
Customer Relationship Management the competition in market more intense. In such
(CRM), is favored by more and more enterprises double pressure, more and more enterprises turn their
under the impact of modern information technology. gaze to customers. Customer relationship
There is a growth of understanding of that market management generate in this context. Gartner Group
competition is the competition for customer put forward a complete CRM concept in 1993. The
resources. Enterprises must rely on customers to group think that “CRM is organizing businesses by
achieve profitable. In the fierce market competition, focusing on customer segmentation, it encourages
in order to maintain superiority and long-term and acts of meeting customers’ needs and achieve the link
stable development, we must attach importance to between customers and suppliers and using other
customer relationship management. And only means to increase profits, revenue and customer
continued to gain and maintain valuable customers satisfaction. It’s a business strategy across the entire
and achieve customer demand for personalized, in- enterprise.”
depth excavation in order to achieve the corporation-
customer win-win. This kind of customer relationship
management, customer-centric business strategy, is
In the market environment with high degree based on information technology. It restructures the
of disturbance, the intensified competition intensity, work processes in order to give businesses better
the highly refined products, the increasingly saturated customer communication skills, maximize customer
market, the increasingly convergence of the product profitability. Customer relationship management
quality and service characteristic, and the short typically includes the whole processes that determine,
product life cycles make the customer choose more select, seek, develop and maintain their clients to
ample, the buyer market increasingly growing, the implement. The main objective of its management is
customer transfer costs declined and the customer life to increase efficiency, expand markets and retain
cycle shortened. Under such circumstances, how to customers.
improve the customer transfer costs, extend the
customer life cycle and maximize the customer And data mining, also known as Knowledge
profits become problems the enterprises need to Discovery in Database, KDD, is from a large
solve. At the same time, the uncertain increase of database or data warehouse to extract people are
customer demand, the growing trend of individuality interested in knowledge that is implicit in advance
and diversification, and the intensified changes make unknown, the potential useful information. Data
the operational risks of enterprise increased mining is data processing in a high-level process,
significantly. The high market disturbance makes that which from the data sets to identify in order to model
the business concept which regards product to represent knowledge. The so-called high-level
management as the centre faces enormous challenges process is a multi-step processing, multi-step
and leads to that the relationship management with interaction between the repeated adjustments, to form
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2. S.Thiripura Sundari, Dr.A.Padmapriya / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.107-111
a spiral process. The knowledge of mining expresses
as Concepts, Rules, Regularities and other forms.
CRM Data mining is from a large number of relevant
customer data in the excavated implicit, previously
unknown; the right business decisions have the
potential value of knowledge and rules. Technically,
customer relationship management data mining
system using infiltrated the way; you can
automatically produce some of the required
information. Deeper mining are also needed
enterprises statistics, decision sciences and computer
science professionals to achieve.
Clustering Analysis is a widely used data
mining analytical tools. After clustering, the same
types of data together as much as possible, while the
separation of different types of data as possible. Will
be a collection of objects divided into several
categories, each category of objects is similar, but
other categories of objects are not similar. Adoption
of clustering people can identify dense and sparse
regions, and thus found that the overall distribution
patterns and data among the interesting properties of
mutual relations. Such as the enterprise customer
classification, through the customer’s buying
behavior, consumption habits, and the background to
identify the major clients, customers, and tap Fig. 1 Data Mining Basic Process
potential customers. There are many clustering
algorithms, this means using K-means algorithm. K- 1.1 Data mining in CRM system
means algorithm is a specific means: for a given Data mining technology is not only the
number of classes K, the n objects assigned to a class simple retrieval, query and transfer faced to special
K to go, making within-class similarity between database, it also should do microcosmic and
objects the most, while the similarity between the macroscopically statistics, analysis, synthesis and
smallest class. reasoning to guide the solving of practical problems,
find the relationship between events, and even use the
III. DATA MINING IN CRM SYSTEM existing data to predict future activities. In order to
Data mining uses some mature algorithms achieve data mining, now a number of software tools
and technologies in artificial intelligence, such as have been developed and formed many aspects of a
artificial neural networks, genetic algorithms, number of products in the customer relationship
decision tree, neighboring search methods, rule-based management, such as customer evaluation and
reasoning and fuzzy logic. According to the different subdivision, customer behavior analysis, customer
functions, the analyzed methods of data mining can communication and personalized services. The action
be divided into the following types: classification, of data mining in CRM is represented as the
valuation, correlated analysis and clustering. following aspects:
Data mining technology is not only the 1.1.1 Customer characteristics multi-
simple retrieval, query and transfer faced to special dimensional analysis
database, it also should do microcosmic and It refers to analyzing the customer
macroscopically statistics, analysis, synthesis and characteristic demand. The customer attribute
reasoning to guide the solving of practical problems, description should include address, age, sex, income,
find the relationship between events, and even use the occupation, education level, and many other fields,
existing data to predict future activities. and can be carried multi-dimensional combination
The basic data mining process is composed analysis and quickly presented with the list and the
by the following steps number of customers which accord with the
conditions. For example: customer subdivision model
is an effective tool of typical customer identification
and analysis and the compendia data provides the
basis for customer categorization and subdivision.
The course, a subdivision standard must be
defined firstly, for example, according to the profit
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3. S.Thiripura Sundari, Dr.A.Padmapriya / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.107-111
contribution of the customers they can be subdivided
into high-profit customer groups, the profitable
customer groups, profit margin customer groups,
non-profit customer groups and deficit customer
groups, which separately corresponding to gold
customer, emphasis customer, hypo-emphasis
customer, common customer and deficit customer.
1.1.2 Customer behavior analysis
It refers to analyzing the consuming
behavior of certain customer group combining
customer information. And according to different
consuming behaviors changes, individuation
marketing strategies will be established and the
emphasis customer or the customer with the trend of
loss will be selected.
For example: through the setting of
standards users automatically monitor some basic
indicators and data of operation run and timely
compare them with conventional, standard data,
Fig. 2 CRM Structure Based on Data Mining
when more than a certain percentage the abnormal
warning will be advanced. Many customer behavior
The whole system can be divided into three
changes may mean that customers have "left"
layers: the interfacial layer, the functional layer and
tendency, therefore, their behaviors should be
the layer of support. The interfacial layer is the
identified in time and the efforts should be made
interface to interact with users, access or transmit
before the customer decision.
information, and it provides intuitive, easy-to-use
1.1.3 Customer structure analysis
interface for users to access to the necessary
Through the statistical analysis (a particular
information conveniently; the functional layer is
unit time interval for statistics) of the various types of
composed by the subsystem with basic function in
It refers to the analysis of customer contact and
CRM, each subsystem as well as includes a number
customer service. According to the analyzing result
of operations which form a operational layer; and the
of customer concerns and customer tendency,
layer of support includes database management
understanding and mastering their needs and
system and operating system.
providing the communication content they interested
1.3 Functional Design of CRM System
in the most appropriate time through their preferred
The functional structure of the system is
channels, which can enhance the attractiveness of the
designed in accordance with the top-down principle,
customers.
which is gradually target system decomposition
At the same time it can improve the
process; it divides the whole system into several
subordinate institution capacity of differentiated
subsystems and enables them to complete the
services, achieve the customer-focused product
functions of the various subsystems, the system
development and optimization, distribution channel
functional modules are shown in Figure 3
management and customer relationship optimization,
and improve the overall marketing and customer
service levels.
1.1.4 Sales analysis and sales forecast
It includes the analysis according to
products, sales promotions effect, sales channels and
methods. At the same time, it also analyzes the
different effects of different customers to business
benefit, analyzes the effects of customer behavior to
business benefit, this makes the relationship between
business and customer.
1.2 CRM system design based on data mining
1.2.1 CRM system structure
Based on the analysis of existing CRM
structure and according to the analysis of CRM core
idea, here a CRM structure based on data mining is Fig. 3 Functional Design of CRM System
built, as shown in Figure 2
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4. S.Thiripura Sundari, Dr.A.Padmapriya / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.107-111
1.3.1 CR Clustering Analysis cross-selling opportunities, provide more
CR clustering analysis module subdivides comprehensive services to customers and
the customers according to customer values. Because consequently bring greater benefits to enterprises.
that for each enterprise the customer values standards
are different, so it is needed to carry out customer 1.3.5 Customer loss analysis
clustering subdivision for the existing customers of Through the observation and analysis of the
the enterprises, set the corresponding customer level customer historical transactions, enterprise endows
and give class marker for the customers according to customer relationship management modules the
the customer value by using the clustering result. function of customer warning abnormal behaviors.
There area total of five category markers, The system makes the warning signs to the potential
that is, high-value customers, customers with the loss of customers through automatically checking the
most growth, ordinary customers, negative value customer transaction data. And the customer loss
customers and new customers. prediction analysis can help enterprises to find the
lost customers and then adopt measures to retain
1.3.2 CR value judgment them
According to the result of the customer .
clustering analysis, the CR value judgment uses C4.5 1.3.6 Data management
decision tree algorithm to establish classification The data management is to maintain and
model and describe the specific characteristics of manage the customer information, transaction
various types of customers. And in accordance with information, rule information and other related data,
the different characteristics of various types of and it enables operators to quickly find or modify the
customers it also may adopt corresponding required relevant information.
processing means, which guides enterprises to
allocate resources to the valuable customers in the 1.4 DATA MINING IMPLEMENTATION
aspects of marketing, sales, services, gives special The mining can be implemented in two
sales promotions for the valuable customers, provides steps: the first step, selecting the average customer
more personalized service to enable enterprises to purchase sum and purchase number, and using the
obtain the maximum return on minimum investment. method of clustering for the classification of
customers, thus each customer has an affirmed
1.3.3 Customer structure analysis category; second step, using decision tree model to
Through the statistical analysis (a particular build decision tree for customers, to do further
unit time interval for statistics) of the various types of classification analysis on the customer
customer characteristics, customer structure analysis characteristics.
helps the marketing manager to make marketing
decisions; and through the vertical comparison of the IV. CONCLUSION
customer structure, it provides an important basis for Many companies increasingly use data
the management work of the customer relationship mining for customer relationship management. It
evaluation and adjustment. helps provide more customized, personal service
The activities the customer relationship addressing individual customer’s needs, in lieu of
management concerned are four: access to new mass marketing. As a chain reaction, this will result
customers, keep old customers, customer structure in substantial cost savings for companies. The
upgrading and the overall customer profitability customers also will benefit to be notified of offers
improvement. And according to these, the customer that are actually of interest, resulting in less waste of
structure analysis regards the rate of new customers, personal time and greater satisfaction.
customer retention, customer promotion rates and To enterprises, CRM is not technology, but
customer profitability as the four analyzing goals a management style, but also a business strategy. It is
difficult for enterprise to meet the customer specific
1.3.4 Customer behavior analysis module needs if it only implements a generic CRM solution.
Customer behavior analysis module mainly This will require that the enterprises can implement
analyzes the customer satisfaction, customer loyalty, different solutions step-by-step and ultimately form
customer responsiveness and the cross-selling. an overall CRM solution, and can achieve
Maintaining long-term customer satisfaction and personalized management and implementation. At
customer loyalty contributes to the establishment of the same time enterprises also need to continuously
customer relations and ultimately improve the improve the CRM system to better manage the
company long-term profitability. Customer customers. It can be believed that the CRM system
responsiveness analysis can effectively guide the will become the essential system for enterprise
sales and marketing behavior, improve the past sales survival and development, and the CRM system will
without goal and reduce the cost of sales. be further mature with the development of
It also can do correlation analysis for the technology and management thinking to play a more
existing customer purchasing behavior data, find the
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5. S.Thiripura Sundari, Dr.A.Padmapriya / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.107-111
significant effect role in the whole development of BIOGRAPHY
the community.
S.Thiripura Sundari, a very enthusiastic and
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