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
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
Data mining allows companies to analyze large amounts of customer data to discover patterns and trends that can help target new customers and increase profits. It involves extracting, transforming, and storing transaction data, then analyzing it to find useful business insights. Popular data mining algorithms include statistical analysis, neural networks, and nearest neighbor methods. While data mining provides benefits, privacy is a concern as customer information may be shared with third parties without consent.
Data Mining Based Store Layout Architecture for SupermarketIRJET Journal
This document discusses using data mining techniques to develop an efficient store layout for supermarkets. It proposes using association rule mining on transaction data to uncover frequent itemsets purchased together by customers. This can help determine what products to place near each other to increase sales. The document first provides background on data mining and how it can help with decision support. It then describes how association rule mining and the Apriori algorithm can be applied to market basket analysis to analyze customer purchasing patterns and generate rules on related products. The goal is to develop a more customer-oriented store layout based on these rules rather than traditional category-based layouts.
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.
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.
This document discusses how a big box retailer utilized big data to improve its business. It outlines the steps the retailer took:
1) It identified where big data could create advantages, such as predictive analytics to forecast sales declines. This would allow the retailer to be more proactive.
2) It built future capability scenarios to determine how to leverage big data, such as using social media data to predict problems.
3) It defined the benefits and roadmap for implementing big data, including investing millions over 5 years for a positive return. Benefits would include more consistent, faster information and insights.
The document provides details on how the retailer methodically planned and aligned its big data strategy to its business needs
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
What is data mining?
Why data mining is required?
Data mining Applications
Data mining in Retail Industry
Marketing
Risk Management
Fraud Detection
Customer Acquisition and Retention
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
Data mining allows companies to analyze large amounts of customer data to discover patterns and trends that can help target new customers and increase profits. It involves extracting, transforming, and storing transaction data, then analyzing it to find useful business insights. Popular data mining algorithms include statistical analysis, neural networks, and nearest neighbor methods. While data mining provides benefits, privacy is a concern as customer information may be shared with third parties without consent.
Data Mining Based Store Layout Architecture for SupermarketIRJET Journal
This document discusses using data mining techniques to develop an efficient store layout for supermarkets. It proposes using association rule mining on transaction data to uncover frequent itemsets purchased together by customers. This can help determine what products to place near each other to increase sales. The document first provides background on data mining and how it can help with decision support. It then describes how association rule mining and the Apriori algorithm can be applied to market basket analysis to analyze customer purchasing patterns and generate rules on related products. The goal is to develop a more customer-oriented store layout based on these rules rather than traditional category-based layouts.
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.
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.
This document discusses how a big box retailer utilized big data to improve its business. It outlines the steps the retailer took:
1) It identified where big data could create advantages, such as predictive analytics to forecast sales declines. This would allow the retailer to be more proactive.
2) It built future capability scenarios to determine how to leverage big data, such as using social media data to predict problems.
3) It defined the benefits and roadmap for implementing big data, including investing millions over 5 years for a positive return. Benefits would include more consistent, faster information and insights.
The document provides details on how the retailer methodically planned and aligned its big data strategy to its business needs
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
What is data mining?
Why data mining is required?
Data mining Applications
Data mining in Retail Industry
Marketing
Risk Management
Fraud Detection
Customer Acquisition and Retention
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.
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.
This document discusses operational business intelligence and how it helps agile enterprises. It discusses how vendors are developing products that provide real-time intelligence and decision-making capabilities. It also discusses how tools like machine learning, semantic search, and data visualization are helping organizations gain insights from large amounts of structured and unstructured data to make better operational decisions.
Data Mining in Life Insurance BusinessAnkur Khanna
The document proposes using data mining techniques for an insurance company. It discusses using data mining to establish insurance rates, acquire new customers, retain existing customers, develop new product lines, detect fraudulent claims, perform marketing campaigns, and coordinate different departments. Specific techniques mentioned include classification, estimation, prediction, profiling, and affinity grouping/association. The document also outlines the data mining process and provides examples of how US life insurers and other companies use data mining.
This document discusses techniques for customer relationship management (CRM) using data mining. It begins by introducing common data mining applications in retail, banking, and telecommunications. It then discusses how data mining can be used throughout the customer lifecycle to perform tasks like up-selling, cross-selling, and customer retention. The document proceeds to explain various data mining techniques including descriptive techniques like clustering and association rule mining as well as predictive techniques like classification, regression, and decision trees. It concludes by discussing major issues in the field of data mining.
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.
IRJET-User Profile based Behavior Identificaton using Data Mining TechniqueIRJET Journal
This document presents a model for analyzing customer behavior on online shopping sites using data mining techniques. Clickstream data is collected from customers and analyzed to predict shopping behaviors and provide recommendations. The Naive Bayes algorithm is used to classify customers into categories based on likely purchased and viewed product categories. Recommendations are then provided to customers in their predicted interested categories. The model aims to increase sales by understanding customer interests and loyalty to specific product types.
A potential objective of every financial organization is to retain existing customers and attain new
prospective customers for long-term. The economic behaviour of customer and the nature of the
organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking.
Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with
high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk;
and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk
for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are
many existing systems on customer retention as well as customer attrition systems in bank, these rigorous
methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used
records of business customers of a retail commercial bank in the city including rural and urban area of
(Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a
model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for
analysing the challenging issues, where pruned decision tree classification technique has been used to
develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to
build up a model to predict prospective business sectors in retail banking.
Making Customer Data Actionable With Predictive Analytics In The Automotive M...Outsell
High performing dealer marketing teams routinely use analytics, statistical modeling and data mining to understand customer behavior, preferences and anticipate next actions. What most marketers want are the analytic model results and scores that they can put to use, not sophisticated analytic tools. In general, marketers don't have (and have difficulty hiring for) the skills necessary. There are new tools and techniques available that can bring the power of statistical modeling to your marketing team to help anticipate auto consumer needs, detect preferences, improve message timing, increase relevancy, and improve sales. Ultimately, delivering the answers you're seeking.
This document discusses customer relationship management (CRM) strategies in the airline industry. It explains that CRM aims to acquire new customers, grow existing customers, and retain valuable customers. Data mining and analysis are important for airline CRM to understand customer behavior. The document also outlines e-CRM systems that allow airlines to manage customer relationships online. Specific benefits of implementing a CRM strategy for airlines include improved marketing and service. Challenges include overcoming obstacles like lack of data sharing between departments.
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’.
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.
The use of web analytics for digital-marketing-pe 2015 industrial-marketing-adnan haidar
This study examines how industrial companies can use web analytics (WA) to measure digital marketing performance. The authors conducted a case study analysis of one company that successfully harnessed WA and two others that did not realize benefits, despite using WA. They found that the benefits of using WA depend on three dimensions: 1) the metrics selected for analysis, 2) how the WA data is processed, and 3) the organizational context surrounding WA use. The case study illustrates how industrial companies with complex sales can leverage WA to demonstrate how digital marketing activities benefit their business.
Direct marketing with the application of data miningAlexander Decker
1. Direct marketing involves sending targeted messages directly to consumers through methods like email, telemarketing, and mail. It is more effective than mass marketing as it focuses on specific customer groups.
2. Data mining techniques like supervised classification can be used to analyze customer data and classify customers as loyal or unloyal for direct marketing purposes. Decision trees are a popular technique to visualize customer classifications.
3. Building accurate customer classifications is challenging due to issues like imbalanced class distributions in the data. Ranking and lift analysis can help address these issues and identify the most promising potential customers to target.
11.direct marketing with the application of data miningAlexander Decker
This document summarizes a research paper on using data mining techniques for direct marketing. It discusses how direct marketing focuses on specific customer groups rather than mass marketing. Data mining algorithms like decision trees are used to classify customers as loyal or unloyal based on attributes in customer data. This helps direct marketing efforts towards the most beneficial customers. The document also outlines some common problems in classification for direct marketing like imbalanced data and issues with predictive accuracy, and provides solutions like lift analysis.
Data mining involves using analytical techniques to discover patterns in large data sets. It is used to gain insights into business problems like predicting customer behavior or identifying fraud. The key steps in data mining include requirement analysis, data collection/preparation, exploration of techniques, implementation/evaluation, and visualization of results. Applications include prediction, relationship marketing, customer profiling, outlier detection, and customer segmentation.
Arun Gupta, Customer Care Associate and Group Chief Technology Officer, Shoppers Stop presented at the Premier Business Leadership Series 2010, http://www.sas.com/theserieshk.
With many retailers worldwide struggling to maintain revenues, how do you grow in such a tough competitive landscape? As a leading Indian retailer and pioneer in using technology, especially business analytics, Shoppers Stop is not only thriving but has helped revolutionise the retail sector. Gupta will share insights on using analytics to drive business value, reduce operational costs and provide better products and customer experience.
1) The document discusses how Avelo, a UK financial software vendor, can leverage big data to provide additional value to its clients in the financial services sector.
2) It examines potential big data projects Avelo could implement related to performance management, data exploration, social analytics, and decision science.
3) It also discusses how Avelo has begun constructing a big data capability by developing a common enterprise data model to unify its applications and data.
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.
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.
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.
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.
This document discusses operational business intelligence and how it helps agile enterprises. It discusses how vendors are developing products that provide real-time intelligence and decision-making capabilities. It also discusses how tools like machine learning, semantic search, and data visualization are helping organizations gain insights from large amounts of structured and unstructured data to make better operational decisions.
Data Mining in Life Insurance BusinessAnkur Khanna
The document proposes using data mining techniques for an insurance company. It discusses using data mining to establish insurance rates, acquire new customers, retain existing customers, develop new product lines, detect fraudulent claims, perform marketing campaigns, and coordinate different departments. Specific techniques mentioned include classification, estimation, prediction, profiling, and affinity grouping/association. The document also outlines the data mining process and provides examples of how US life insurers and other companies use data mining.
This document discusses techniques for customer relationship management (CRM) using data mining. It begins by introducing common data mining applications in retail, banking, and telecommunications. It then discusses how data mining can be used throughout the customer lifecycle to perform tasks like up-selling, cross-selling, and customer retention. The document proceeds to explain various data mining techniques including descriptive techniques like clustering and association rule mining as well as predictive techniques like classification, regression, and decision trees. It concludes by discussing major issues in the field of data mining.
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.
IRJET-User Profile based Behavior Identificaton using Data Mining TechniqueIRJET Journal
This document presents a model for analyzing customer behavior on online shopping sites using data mining techniques. Clickstream data is collected from customers and analyzed to predict shopping behaviors and provide recommendations. The Naive Bayes algorithm is used to classify customers into categories based on likely purchased and viewed product categories. Recommendations are then provided to customers in their predicted interested categories. The model aims to increase sales by understanding customer interests and loyalty to specific product types.
A potential objective of every financial organization is to retain existing customers and attain new
prospective customers for long-term. The economic behaviour of customer and the nature of the
organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking.
Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with
high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk;
and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk
for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are
many existing systems on customer retention as well as customer attrition systems in bank, these rigorous
methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used
records of business customers of a retail commercial bank in the city including rural and urban area of
(Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a
model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for
analysing the challenging issues, where pruned decision tree classification technique has been used to
develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to
build up a model to predict prospective business sectors in retail banking.
Making Customer Data Actionable With Predictive Analytics In The Automotive M...Outsell
High performing dealer marketing teams routinely use analytics, statistical modeling and data mining to understand customer behavior, preferences and anticipate next actions. What most marketers want are the analytic model results and scores that they can put to use, not sophisticated analytic tools. In general, marketers don't have (and have difficulty hiring for) the skills necessary. There are new tools and techniques available that can bring the power of statistical modeling to your marketing team to help anticipate auto consumer needs, detect preferences, improve message timing, increase relevancy, and improve sales. Ultimately, delivering the answers you're seeking.
This document discusses customer relationship management (CRM) strategies in the airline industry. It explains that CRM aims to acquire new customers, grow existing customers, and retain valuable customers. Data mining and analysis are important for airline CRM to understand customer behavior. The document also outlines e-CRM systems that allow airlines to manage customer relationships online. Specific benefits of implementing a CRM strategy for airlines include improved marketing and service. Challenges include overcoming obstacles like lack of data sharing between departments.
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’.
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.
The use of web analytics for digital-marketing-pe 2015 industrial-marketing-adnan haidar
This study examines how industrial companies can use web analytics (WA) to measure digital marketing performance. The authors conducted a case study analysis of one company that successfully harnessed WA and two others that did not realize benefits, despite using WA. They found that the benefits of using WA depend on three dimensions: 1) the metrics selected for analysis, 2) how the WA data is processed, and 3) the organizational context surrounding WA use. The case study illustrates how industrial companies with complex sales can leverage WA to demonstrate how digital marketing activities benefit their business.
Direct marketing with the application of data miningAlexander Decker
1. Direct marketing involves sending targeted messages directly to consumers through methods like email, telemarketing, and mail. It is more effective than mass marketing as it focuses on specific customer groups.
2. Data mining techniques like supervised classification can be used to analyze customer data and classify customers as loyal or unloyal for direct marketing purposes. Decision trees are a popular technique to visualize customer classifications.
3. Building accurate customer classifications is challenging due to issues like imbalanced class distributions in the data. Ranking and lift analysis can help address these issues and identify the most promising potential customers to target.
11.direct marketing with the application of data miningAlexander Decker
This document summarizes a research paper on using data mining techniques for direct marketing. It discusses how direct marketing focuses on specific customer groups rather than mass marketing. Data mining algorithms like decision trees are used to classify customers as loyal or unloyal based on attributes in customer data. This helps direct marketing efforts towards the most beneficial customers. The document also outlines some common problems in classification for direct marketing like imbalanced data and issues with predictive accuracy, and provides solutions like lift analysis.
Data mining involves using analytical techniques to discover patterns in large data sets. It is used to gain insights into business problems like predicting customer behavior or identifying fraud. The key steps in data mining include requirement analysis, data collection/preparation, exploration of techniques, implementation/evaluation, and visualization of results. Applications include prediction, relationship marketing, customer profiling, outlier detection, and customer segmentation.
Arun Gupta, Customer Care Associate and Group Chief Technology Officer, Shoppers Stop presented at the Premier Business Leadership Series 2010, http://www.sas.com/theserieshk.
With many retailers worldwide struggling to maintain revenues, how do you grow in such a tough competitive landscape? As a leading Indian retailer and pioneer in using technology, especially business analytics, Shoppers Stop is not only thriving but has helped revolutionise the retail sector. Gupta will share insights on using analytics to drive business value, reduce operational costs and provide better products and customer experience.
1) The document discusses how Avelo, a UK financial software vendor, can leverage big data to provide additional value to its clients in the financial services sector.
2) It examines potential big data projects Avelo could implement related to performance management, data exploration, social analytics, and decision science.
3) It also discusses how Avelo has begun constructing a big data capability by developing a common enterprise data model to unify its applications and data.
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.
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.
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasProf Dr Mehmed ERDAS
Big Data Analytics for TELCOs Customer Experience Management Permission Based Marketing for Location and Movement Data Data Modelling Business Use Cases Data Mining BSS OSS COTS OTT Churm Modeling Markov Processes HANA HADOOP INtegration Video Streaming Test cases
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.
Data mining software analyzes stored transaction data to identify relationships and patterns. It can group data into classes, clusters, or identify associations and sequential patterns. Data mining is used to predict trends, discover previously unknown patterns, and drive business decisions for marketing, finance, manufacturing, and government. However, privacy issues arise from personal data collection and security issues from data theft, requiring proper handling of private information.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
Big Data Analytics for Predicting Consumer BehaviourIRJET Journal
This document discusses using big data analytics and machine learning techniques to predict consumer behavior and sales trends. It begins with an introduction to consumer behavior and an overview of how analyzing customer data can provide insights. The document then discusses using data mining methods on customer data to build predictive models for tasks like sales forecasting. It proposes using a combination of random forest and linear regression algorithms on a dataset from various stores. The implementation section outlines the steps, including data preprocessing, feature extraction, applying algorithms to the data, comparing results and building the best predictive model. The goal is to determine the most accurate approach for understanding customer behavior and how they will respond in different situations.
1) Big data is defined as large volumes of structured and unstructured data that is growing exponentially. It can be analyzed to provide more accurate insights and better decision making.
2) The key aspects of big data are volume, velocity, variety, and variability of data from multiple sources.
3) Companies that effectively analyze big data can improve marketing ROI by 15-20% and increase productivity and profits by 5-6% over peers.
The document discusses key trends in data management identified by global research. It finds organizations are increasingly focused on understanding customers as individuals to offer personalized service. However, inaccurate and incomplete data undermines customer experience for many. Experts recommend using data to develop a single view of each customer by linking all available information. This would allow real-time insights and responses tailored to individual customers, improving relationships and sales. Achieving accurate and comprehensive customer data remains a challenge for most organizations.
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETAM Publications
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for today’s competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of today’s customer relationship management and Decision making for different companies that deals with big data.
An impact of knowledge mining on satisfaction of consumers in super bazaarsIAEME Publication
This document summarizes research on using knowledge mining techniques to study customer satisfaction levels in super bazaars. It first introduces the importance of customer satisfaction for super bazaars and defines knowledge mining. It then describes various knowledge mining techniques that can be applied, including classification, regression, time series analysis, clustering, and association rule mining. The document proposes a model for conducting customer satisfaction surveys, applying knowledge mining techniques to the data, and using the results to enhance customer satisfaction. The goal of the research is to better understand customer preferences and behaviors to improve business performance for super bazaars.
A Survey on Bigdata Analytics using in Banking Sectorsijtsrd
Current days, banking industry is generating large amount of data. Already, most banks have failed to utilize this data. However, nowadays, banks have starts using this data to reach their main objectives of marketing. By using this data, many secrets can be discovering like money movements, thefts, failure. This paper aims to find out how big data analytics can be used in banking sector to find out spending patterns of customer, sentiment and feedback analysis etc. Big data analytics can aid banks in understanding customer behavior based on the inputs receive from their investment patterns, shopping trends, motivation to invest and personal or financial backgrounds. This data plays a necessary role in leading customer loyalty by designing personalized banking solutions for them. Gagana H. S | Roja H. N | Gouthami H. S "A Survey on Bigdata Analytics using in Banking Sectors" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31016.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31016/a-survey-on-bigdata-analytics-using-in-banking-sectors/gagana-h-s
A Survey on Customer Analytics Techniques for the Retail IndustryIRJET Journal
This document summarizes several techniques for customer analytics in the retail industry that are discussed in existing literature, including customer churn prediction, customer segmentation, and market basket analysis. It provides an overview of common algorithms used for each technique, such as classification algorithms for churn prediction, clustering algorithms for segmentation, and association rule mining for market basket analysis. It then reviews seven research papers that evaluate these techniques on retail transaction and customer data, comparing the performance of algorithms like K-means clustering, decision trees, and neural networks. The papers demonstrate how these analytical approaches can provide actionable insights for retailers to improve customer retention, target marketing, and optimize product assortments.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
DEMOGRAPHIC DIVISION OF A MART BY APPLYING CLUSTERING TECHNIQUESIRJET Journal
This document discusses demographic segmentation of customers at a mart by applying clustering techniques. It begins with an abstract that outlines the goal of employing advanced techniques like machine learning to target customer needs and increase sales. The introduction provides context on the increasing competitiveness of business and need for customer segmentation. The literature review summarizes several papers on topics like using machine learning for customer segmentation, comparing clustering algorithms on retail data, and dividing bank customers into clusters. The implementation section outlines the steps taken - data collection, cleaning, applying K-Means and agglomerative clustering, and exploratory data analysis. The proposed system aims to recognize the current customer situation, consolidate prior work, discover customer-attribute relationships, perform unsupervised clustering analysis and model evaluation,
This document is a quarterly publication that provides insights for boards and audit committees. It discusses how boards can help organizations embrace data analytics to derive value from big data. It also explores how strengthening internal controls can help tackle corruption risks. Additionally, it highlights an interview discussing the role of nomination committees in selecting directors and evaluating board performance, with a focus on both monetary and non-monetary criteria.
IRJET- Customer Buying Prediction using Machine-Learning Techniques: A SurveyIRJET Journal
1) The document discusses using machine learning techniques to predict customer purchasing and churn based on their personal and behavioral data.
2) It reviews several machine learning algorithms that have been used for prediction, including random forest, logistic regression, naive bayes, and support vector machines.
3) Deep learning techniques are also discussed, including the use of convolutional neural networks to reveal hidden patterns in customer data and predict purchases and churn.
Big data is playing an increasingly important role in the retail industry. The document discusses how retailers can use big data analytics to gain competitive advantages through improved marketing, merchandising, operations, supply chain management, and new business models. Specifically, big data enables retailers to better understand customer behavior, personalize offerings, optimize pricing and inventory, and process customer information in real-time to improve the shopping experience.
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.
Similar to Customer Churn Prediction using Association Rule Mining (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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retail sales and marketing company decides to remove as a
subscriber. They are churned for fraud, non-payment and
those whodon‘t use theservice.Voluntarychurnisdifficultto
determine because it is the decision of the customer to
unsubscribe from the service provider. Voluntary churn can
further be classified as incidental and deliberate churn . The
former occurs without any prior planning by the churn but
due to change in the financial condition, location, etc. Most
operators are trying to deal with these types of churns
mainly.
1.2 Churn Management
Churn management is very important forreducing churns as
acquiring a new customer is more expensive than retaining
the existing ones. Churn rate is the measurement for the
number of customers moving out and in during a specific
period of time. If the reason for churning is known, the
providers can then improve their services to fulfill the needs
of the customers. Churns can be reduced by analyzing the
past history of thepotentialcustomerssystematically.Alarge
amount of information is maintained by the retail sales and
marketing company for each of their customers that keep on
changing rapidly due to a competitive environment. The
information includes the details about billing, calls and
network data. The huge availability of information arises the
scope of using Data mining techniques in the company’s
database. The information available can be analyzed in
different perspectives to provide various ways to the
operators to predict and reduce churning. Only the relevant
details are used in the analysiswhichcontributestothestudy
from theinformation given. Data mining techniquesareused
for discovering the interesting patterns within data and it
helps to learn to predict whetheracustomerwillchurnornot
based on customer‘s data stored in the database.
2. RELATED WORKS
Berry and Linoff (2000) defines data mining as the process
of exploring and analyzing huge datasets, in order to find
patterns and rules which can be important to solve a
problem. Berson et al. (1999); Lejeune extract or detect
hidden patterns or information from large databases. Data
mining is motivated by the need for techniques to support
thedecision maker in analyzing, understanding and
visualizing the huge amounts of data that have been
gathered from business and are stored in data warehouses
or other information repositories. Data mining is an
interdisciplinary domain that gets together artificial
intelligence, database management, machine learning, data
visualization, mathematic algorithms, and statistics data
mining is considered by some authors as the core stage of
the Knowledge Discovery in Database (KDD) process and
consequently it has received by far the most attention in the
literature (Fayyad et al., 1996a). Data mining applications
have emerged from a variety of fields including marketing,
banking, finance, manufacturing and health care (Brachman
et al., 1996). Moreover, data mining has also been applied to
other fields, such as spatial, telecommunications, web and
multimedia.
3. THEORETICAL BACKGROUND
Data Mining is very famous technique for churn prediction
and it is used in many fields. It refers to the process of
analyzing data in order to determine patterns and their
relationships. It is an advanced technique which goes deep
into data and uses machine learning algorithms to
automatically shift through each record and variable to
uncover the patterns and information that may have been
hidden. Data mining is used to solve the customer churn
problem by identifying the customer behavior from large
number of customer data. Its techniques have been used
widely in churn prediction context such as Support Vector
Machines (SVM), Decision Tree (DT), Artificial Neural
Network (ANN) and Logistic regression.
3.1 Customer Churn Prediction Model
Customer Relationship Management (CRM) system have
been developed and it is applied in order to improve
customer acquisition and retention. Increase of profitability
and to support important analytical tasks such as predictive
modeling and classification; CRM applications hold a huge
set of information regarding each individual customer. This
information is gained from customers’ activity at the
company, data entered by the customer in the process of
registration. The size of gathered data is usually very large,
which results in high dimensionality, making to analyze a
complex and challengingtask.Therefore, beforebeginningto
use a churn prediction method a data reduction techniqueis
used, deciding with application domain knowledge which
attributes can be of use and which can be ignored. Missing
values should also be regarded – on attribute level these can
be ignored if they are with low significance, whereas on
record level they have to be replaced with a reasonable
estimate. Providing a good estimate for these missingvalues
is an important issue for proper churn prediction.
Figure.1 Customer Churn Prediction Model
3.2 Association Rule Mining
Association rule mining, one of the most important and well
researched techniques of data mining, was first introduced
in. It aims to extract interesting correlations, frequent
patterns, associations or casual structures among sets of
items in the transaction databases orotherdata repositories.
Association rules are widely used in various areas such as
telecommunication networks,marketandrisk management,
inventory control etc. Various associationminingtechniques
and algorithms will be briefly introduced and compared
later. Association rule mining is to find out association rules
that satisfy the predefinedminimumsupportandconfidence
from a given database. The problem is usually decomposed
into two subproblems. One is to find those itemsets whose
occurrences exceed a predefined threshold in the database;
those itemsets are called frequent or large itemsets. The
second problem is to generate association rules from those
large itemsets with the constraints of minimal confidence.
Suppose one of the large itemsets is Lk, Lk = {I1, I2, … , Ik},
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association rules with this itemsets are generated in the
following way: the first rule is {I1, I2, … , Ik-1}⇒ {Ik}, by
checking the confidence this rule can be determined as
interesting or not. Then other rule are generated by deleting
the last items in the antecedent and inserting it to the
consequent, further the confidences of the new rules are
checked to determine the interestingness of them. Those
processes iterated until the antecedent becomes empty.
Since the second subproblem is quite straight forward,most
of the researches focus on the first subproblem. The first
sub-problem can be further divided into two sub-problems:
candidate large itemsets generation process and frequent
itemsets generation process. We call those itemsets whose
support exceed the support threshold as large or frequent
itemsets, those itemsets that are expected or have the hope
to be large or frequent are called candidate itemsets.
Association Rule Mining can be viewed as a two-step
process:
1. Find all frequent item sets
Apriori Method
FP Growth Method (Frequent Pattern)
2. Generate strong association rules from thefrequent
item sets:
By definition, these rules must satisfyminimumsupport
and minimum confidence
3.3 Basic Concepts & Basic Association Rules
Algorithms
Let I=I1, I2, … , Im be a set of m distinct attributes, T be
transaction that contains a set of items such that T ⊆ I,Dbea
database with different transaction records Ts. An
association rule is an implication in the form of X⇒Y, where
X, Y ⊂ I are sets of items called itemsets, and X ∩ Y =∅. X is
called antecedent while Y is called consequent, the rule
means X implies Y. There are two important basic measures
for association rules, support(s) and confidence(c).Sincethe
database is large and users concern about only those
frequently purchased items, usually thresholds of support
and confidence are predefined by users to drop those rules
that are not so interesting or useful. The two thresholds are
called minimal support andminimalconfidencerespectively.
Support(s) of an association rule is defined as the
percentage/ fraction of records that contain X ∪ Y to the
total number of records inthedatabase.Supposethesupport
of an item is 0.1%, it means only 0.1 percent of the
transaction contain purchasing of this item.Confidenceof an
association rule is defined as the percentage/fraction of the
number of transactions that contain X ∪ Y to the total
number of records that contain X. Confidenceis ameasure of
strength of the association rules, suppose the confidence of
the association rule X⇒Y is 80%, it means that 80% of the
transactions that contain X also contain Y together. In
general, a set of items (such as the antecedent or the
consequent of a rule) is called an itemset. The number of
items in an itemset is called the length of an itemset.
Itemsets of some length k are referred to as k-itemsets.
Generally, an associationrules mining algorithmcontains the
following steps:
The set of candidate k-itemsets is generated by 1-
extensions of the large (k -1)-itemsets generated in the
previous iteration.
Supports for the candidatek-itemsetsaregenerated bya
pass over the database.
Itemsets that do not have the minimum support are
discarded and the remaining itemsets are calledlargek-
itemsets.
This process is repeated until no more large itemsets are
found. The AIS algorithm was the first algorithm proposed
for mining association rule. In this algorithm only one item
consequent association rules are generated, which means
that the consequent of those rules only contain one item, for
example we only generate rules like X ∩ Y⇒Z but not those
rules as X⇒Y∩ Z. The main drawback of the AIS algorithm is
too many candidate itemsets that finally turned out to be
small are generated, which requires more space and wastes
much effort that turned out to be useless. At the same time
this algorithm requires too many passes over the whole
database.
Apriori is more efficient during the candidate generation
process. Apriori uses pruningtechniques toavoidmeasuring
certain itemsets, while guaranteeing completeness. These
are the itemsets that the algorithm can prove will not turn
out to be large. However there are two bottlenecks of the
Apriori algorithm. One is the complex candidate generation
process that uses most of the time, space and memory.
Another bottleneck is the multiple scan of the database.
Based on Apriori algorithm, many new algorithms were
designed with some modifications or improvements.
3.4 Frequent Pattern Growth (FP Growth)
Finding frequent item sets without candidate generation
1. First, compress the database representing frequent
items into a frequent pattern tree or Data classification
is a two-step process. In the first FP tree, which retains
the itemset association information. FP-tree is an
extended prefix-tree structure storing crucial,
quantitative information about frequent patterns. Only
frequent length-1 items will have nodes in the tree, and
the tree nodes are arranged in such a way that more
frequently occurring nodes will have better chances of
sharing nodes than less frequently occurring ones. FP-
Tree scales much better than Apriori because as the
support threshold goes down, the number as well as the
length of frequent itemsets increase dramatically. The
candidate sets that Apriori must handle become
extremely large, and the pattern matching with a lot of
candidates by searching through the transactions
becomes very expensive. The frequent patterns
generation process includes two sub processes:
constructing the FT-Tree, and generating frequent
patterns from the FP-Tree. Theminingresultisthesame
with Apriori series algorithms. To sum up, the efficiency
of FP-Tree algorithm account for threereasons.Firstthe
FP-Tree is a compressed representation of the original
database because only those frequent items are used to
construct the tree, other irrelevant information are
pruned. Secondly this algorithm onlyscans thedatabase
twice. Thirdly, FP-Tree uses a divide and conquer
method that considerably reduced the size of the
subsequent conditional FP-Tree.
2. Then devide the compressed database into a set of
conditional databases ( a special kind of projected
database), each associated with one frequent item or
“pattern fragment”, mines each such database
separately.
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No Variable Name Description
1 Age, Gender, Occupation Demographic variables considered
2 The number of purchase Identifies the number of customer is purchased
3 Frequently used purchase Identifies the most frequently purchase by the consumer
4 Churn Identifies whether customer have changed company or not
5 Product innovation Determines whether product innovation is necessary for sustaining customers
6 Product purchase amount (DpM) Approximates the amount used to purchase product a month
7 Credit purchase amount (CpM) Approximates the amount used to purchase call credits a month
8 Tariffs The type of customer, whether a prepaid or post-paid customer
9 Tenure Length of time a customer has been with a particular subscriber
Table1: The Variables Used In Dataset for This Research
3.5 FP-growth Algorithm
In this section we examine the FP-growth algorithm over a
hypothetical dataset for a sailing company. This example is
picked up from the textbook Data-Mining Concepts and
Techniques (Han & Kamber., 2006). The dataset is a
collection of transaction records. Each transaction has a
unique ID and each item is represented by an index Ij. The
dataset is represented in Table 1. The algorithm starts with
the first scan of the database which derives the set of
frequent items (1-itemsets) and their support counts
(frequencies). Let the minimumsupport countis 2.Thesetof
frequent items is sorted in the order of descending support
count. This resulting set or list is denoted as L. Thus, we
have:
L = {I2: 7, I1: 6, I3: 6, I4: 2, I5: 2}
TID List of items Ids
T100 I1, I2, I5
T200 I2, I4
T300 I2, I3
T400 I1, I2, I4
T500 I1, I3
T600 I2, I3
T700 I1, I3
T800 I1, I2, I3, I5
T900 I1, I2, I3
Table2: Transactional Data for a Sailing Company
An FP-tree is then constructed as follows. First, create the
root of the tree, labeled with “null”. ScandatabaseDa second
time. The items in each transaction are processed in L order
(i.e., sorted according to descending support count), and a
branch is created for each transaction.
Figure2: An FP-tree registers compressed, frequent
pattern information.
The tree obtained after scanning all of the transactions is
shown in Figure 1 with the associated node-links. In this
way, the problem of mining frequentpatterns indatabases is
transformed to that of mining the FP-tree. The FP-tree is
mined as follows: Start from each frequent length-1 pattern
(as an initial suffix pattern); constructitsconditional pattern
base (a “sub database” which consists of the set of prefix
paths in the FP-tree co-occurring with the suffix pattern),
then construct its (conditional) FP-tree, and perform mining
recursively on such a tree. Mining of the FP-tree is
summarized in Table 3.
Item
Conditional
Pattern Base
Conditional
FP-tree
Frequent
Pattern
15
{{I2,I1:1},
{I2,I1,I3:1}}
<I2:2,I1:2> {I2,I5:2},
{I1,I5:2},
{I2,I1,I5:2}
14
{{I2,I1:1},
{I2:1}}
<I2:2> {I2,I1:2}
13
{{I2,I1:2},
{I2:2},
{I1:2}}
<I2:4,I1:2>,
<I1:2>
{I2,I3:4},{I
1,I3:4},{I2,I
1,I3:2}
12 {{I2:4}} <I2:4> {I2,I1:4}
Table3: Mining the FP-tree by creating conditional
(sub-) pattern bases
4. CONCLUSION
This paper deals with the customer churn analysis and
predicting the most profitable customer in the retail sales
and marketing system. Customer churn is one of the most
important metrics for a growing business to evaluate. As
churn management is a major task for companies to retain
valuable customers, the ability to predict customer churn is
necessary. This paper mainly focused on the customer
classification and prediction in Customer Relationship
Management concerned with data mining based on FP
Growth technique. This technique is usedtofindingfrequent
item sets without candidate generation.
References
[1] A Lemmens, & S. Gupta, “Managing Churn to Maximize
Profit”, Harvard Business Schol Working Paper, (14-
020), (2013).
[2] A. Fazlzadeh, M. M. Tabrizi, & K. Mahboobi, “Customer
Relationship Management in Small-Medium
Enterprises”, the case of science and technology parks
of Iran, African Journal of Business
Management.5(15),6160-6168,(2011).
5. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD26818 | Volume – 3 | Issue – 5 | July - August 2019 Page 1890
[3] C. Rygielski, J. C. Wang, & D. C. Yen, “Data Mining
Technique for Customer Relationship Management”,
Technology in society, 24(4), 483-502(2002).
[4] D. Pyle, “Data Preparation for Data Mining”, Morgan
Kaufmann Publishers, Los Altos, California, (1999).
[5] Sharma, D. Panigrahi, & P. Kumar, “A Neural Network
Based Approach for Predicting Customer Churn in
Cellular Network Services”, arXiv preprint arXiv:
1309.3945,(2013).
[6] Jiawei Han, Jian Pei, Yiwen Yin: Mining Frequent
Patterns without Candidate Generation in Proceedings
of the 2000 ACM SIGMOD international Conference on
Management of Data (Dallas, Texas, United States,May
15-18, 2000). SIGMOD’00. ACM Press,New York,NY,1-
12.
[7] V. Umayaparvathi and K. Iyakutti, "A Survey on
Customer Churn Prediction in Telecom Industry:
Datasets,Methods andMetrics,"International Research
Journal of Engineering and Technology(IRJET),vol.03,
no. 04, April 2016.