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.
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.
DMS delivers customized analytical services including descriptive profile analyses and predictive modeling to help clients improve marketing decision making. Their experienced researchers have developed models across industries to predict behaviors like response rates, conversion, revenue, and fraud. DMS works closely with clients to understand their unique needs and develop the most effective models, whether targeting prospects or enhancing current customer profiles and cross-selling. Common types of models include response, reactivation, cloning, and revenue models built using statistical techniques like logistic and multiple regression.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Data mining is the process of discovering patterns in large data sets. The overall goal is to extract useful information that can be understood and used. Key tasks include classification, regression, clustering, summarization, and dependency modeling. Common data mining methods are statistical analysis, decision trees, association rules, and neural networks. Data mining has various applications like direct marketing, market segmentation, customer churn prediction, and market basket analysis. It allows for more effective decision making, prediction, and privacy concerns need to be addressed.
Predicting online user behaviour using deep learning algorithmsArmando Vieira
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data during the pre-train phase. They prove also to be more convenient to deal with severe class imbalance.
This document outlines a proposal to analyze customer relationship management (CRM) data to predict young female customers' propensity to apply for a debit card. The objective is to test hypotheses about factors that influence application rates. The analysis would involve segmenting customers, predictive modeling using logistic regression, and multivariate testing of marketing campaigns on social media. The expected results are identification of key customer parameters, predictive models to increase conversion rates, and insights to improve targeted advertisements.
Boosting conversion rates on ecommerce using deep learning algorithmsArmando Vieira
This document summarizes an approach to use deep learning algorithms to predict the probability that online shoppers will purchase a product based on their website interactions. The approach involves using stacked auto-encoders to reduce the high dimensionality of the product interaction data before applying classification algorithms. Testing on various datasets showed that random forest outperformed logistic regression and that incorporating time data and more training examples improved prediction performance. Further work proposed applying stacked auto-encoders and deep belief networks to fully leverage the large amount of product interaction data.
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 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.
DMS delivers customized analytical services including descriptive profile analyses and predictive modeling to help clients improve marketing decision making. Their experienced researchers have developed models across industries to predict behaviors like response rates, conversion, revenue, and fraud. DMS works closely with clients to understand their unique needs and develop the most effective models, whether targeting prospects or enhancing current customer profiles and cross-selling. Common types of models include response, reactivation, cloning, and revenue models built using statistical techniques like logistic and multiple regression.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Data mining is the process of discovering patterns in large data sets. The overall goal is to extract useful information that can be understood and used. Key tasks include classification, regression, clustering, summarization, and dependency modeling. Common data mining methods are statistical analysis, decision trees, association rules, and neural networks. Data mining has various applications like direct marketing, market segmentation, customer churn prediction, and market basket analysis. It allows for more effective decision making, prediction, and privacy concerns need to be addressed.
Predicting online user behaviour using deep learning algorithmsArmando Vieira
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data during the pre-train phase. They prove also to be more convenient to deal with severe class imbalance.
This document outlines a proposal to analyze customer relationship management (CRM) data to predict young female customers' propensity to apply for a debit card. The objective is to test hypotheses about factors that influence application rates. The analysis would involve segmenting customers, predictive modeling using logistic regression, and multivariate testing of marketing campaigns on social media. The expected results are identification of key customer parameters, predictive models to increase conversion rates, and insights to improve targeted advertisements.
Boosting conversion rates on ecommerce using deep learning algorithmsArmando Vieira
This document summarizes an approach to use deep learning algorithms to predict the probability that online shoppers will purchase a product based on their website interactions. The approach involves using stacked auto-encoders to reduce the high dimensionality of the product interaction data before applying classification algorithms. Testing on various datasets showed that random forest outperformed logistic regression and that incorporating time data and more training examples improved prediction performance. Further work proposed applying stacked auto-encoders and deep belief networks to fully leverage the large amount of product interaction data.
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.
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.
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.
Marketing Analytics Meets Artificial Intelligence: Six Strategies for SuccessMiguel Mello
This document outlines six strategies for using artificial intelligence (AI) to enhance marketing analytics and improve understanding of customers. It discusses using machine learning and automation to enable real-time decision making and next best offers. It also covers using AI and machine learning to improve cross-selling and up-selling efforts. Additionally, it discusses using cognitive computing and sentiment analysis to better understand customer feedback and using cognitive computing and natural language processing to enhance customer service. The document also outlines transforming web analytics into digital intelligence and optimizing marketing with analytics and machine learning.
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.
Optimization of digital marketing campaignsArmando Vieira
This document discusses using machine learning techniques to optimize digital marketing campaigns. Specifically, it analyzes data from campaigns using clustering, visualization and predictive models. Unsupervised learning methods like k-means clustering, PCA, MDS and SOM are used to identify patterns in large digital data. Supervised models like SVMs and random forests predict conversions. The goal is to extract actionable insights to improve ROI, engagement and sales through optimization of parameters like ad design, keywords, bids, channels and budget allocation.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
This document describes a proposed algorithm for improving recommendation systems for e-services. It involves the following key steps:
1. Clustering customer transaction histories to group similar purchase patterns and derive customer-based recommendations.
2. Using incremental association rule mining on the transaction data to detect frequently purchased item sets and relationships between items.
3. Developing a fuzzy model to classify customers and provide dynamic recommendations tailored to different customer types. The recommendations will be based on matching customer preferences and purchase histories to specific product sets.
4. The algorithm clusters transactions, mines association rules incrementally as new data is added, and generates recommendations by classifying customers and matching them to relevant product clusters. This provides a personalized and
Customer Churn Prediction using Association Rule Miningijtsrd
Customer churn is one of the most important metrics for a growing business to evaluate. It is a business term used to describe the loss of clients or customers. In the retail sales and marketing company, customers have multiple choices of services and they frequently switch from one service to another. In these competitive markets, customers demand best products and services at low prices, while service providers constantly focus on getting hold of as their business goals. An increase in customer retention of just 5 can create at least a 25 increase in profit. Therefore, customer churn rate is important because it costs more to acquire new customers than it does to retain existing customers. In this paper, we apply the method to the retail sales and marketing company customer churn data set. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It will help the retail sales and marketing company to present the targeted customers with the estimated loss of clients or customers for the promotion in direct marketing. Mie Mie Aung | Thae Thae Han | Su Mon Ko "Customer Churn Prediction using Association Rule Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26818.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26818/customer-churn-prediction-using-association-rule-mining/mie-mie-aung
Dart builds sophisticated customer segmentation models using statistical techniques and intuition. The goal is to create distinct customer segments that are predictive of behavior and can be implemented for marketing purposes. Dart analyzes customer, transaction, and demographic data to develop segments. The segmentation process involves data preparation, analysis, model development, and finalizing the segments with descriptive profiles and financial analysis. Segments are monitored over time and recalibrated as needed to keep the segmentation strategy relevant.
This document discusses customer retention and churn prediction. It explains that traditional churn prediction models built by data scientists take 6 months to a year to develop and implement, while out-of-the-box solutions like Manthan's Customer360 can identify at-risk customers and execute retention campaigns immediately. Customer360 uses logistic regression to predict churn risk and helps marketers design personalized campaigns to retain profitable customers, like a retailer who stopped $6 million in revenue loss by retaining 22% of at-risk customers in a valuable segment.
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.
Project for System Analysis and Design (IS-6410).
By performing customer segmentation following are the three objectives which can be achieved
with the implementation of this new analytics system:
1. We can track the difference between loyal customers vs visitors, perform heat map
analysis of their browsing patterns.
2. Understanding customer demographics and to focus on high profitable segments.
3. Finally empowering our Marketing department to make better strategic decisions in
terms of online Ads/campaigns.
The Portuguese bank wants to increase sales of long-term deposits through a telemarketing campaign. The authors use logistic regression, decision trees, and neural networks on previous campaign data to build predictive models. They find that including external economic variables improves on a benchmark model using only internal variables. The decision tree and neural network models perform best at predicting successful calls. Combining the three models further increases profits from the campaign.
Consumer Behavior project. Examine and define best ways for Consumer Research Company (Equitec) to target and reach new customers, along with suggesting new ways for the company to market itself.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then discusses why data mining is needed due to the explosive growth of data from various sources. Data mining is defined as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. The knowledge discovery process involves identifying a problem, mining data to transform it into actionable information, acting on the information, and measuring the results. The document outlines different types of data that can be mined, including structured, transactional, time-series, spatial, multimedia, and web data. Common data mining tasks are also described such as classification, prediction, clustering,
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.
Predicting Bank Customer Churn Using ClassificationVishva Abeyrathne
This document describes a study that used classification models to predict customer churn for a bank. The authors collected a dataset of 10,000 bank customers from Kaggle and preprocessed the data. They then explored relationships between features and the target variable of whether a customer churned. Two classification models were tested - KNN and Decision Tree. After hyperparameter tuning, Decision Tree achieved the best accuracy of 84.25%, outperforming KNN. However, both models struggled to accurately predict customers who would churn. The authors concluded Decision Tree was the best model but recommend collecting more data on churning customers.
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.
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.
The document discusses marketing information systems and marketing research. It explains that marketing managers need regular information from various sources to deliver value to customers. An effective marketing information system gathers, analyzes and distributes accurate information to help managers make better decisions. The marketing research process involves defining problems/objectives, developing a research plan, implementing primary/secondary research, interpreting findings and reporting results. Both qualitative and quantitative research methods are discussed.
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.
1. Data mining in marketing involves analyzing customer data to discover useful patterns that can help increase sales and profits.
2. Several steps are typically involved in creating a data mining model, including defining problems, preparing and exploring data, building models, validating models, and deploying models.
3. Decision trees are a common algorithm used that classify customers based on attributes and predict outcomes based on paths from the tree's root nodes to leaf nodes.
Data Science Use Cases in Retail & Healthcare Industries.pdfKaty Slemon
Data science has many useful applications in retail and healthcare. In retail, it allows for personalized recommendations, fraud detection, price optimization, and sentiment analysis. In healthcare, it facilitates medical imaging analysis, genomic research, drug discovery, predictive analytics, disease tracking and prevention, and monitoring through wearable devices. By analyzing customer, patient, and other relevant data, data science helps these industries better meet needs, enhance experiences and outcomes, and improve operations and decision making.
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.
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.
Marketing Analytics Meets Artificial Intelligence: Six Strategies for SuccessMiguel Mello
This document outlines six strategies for using artificial intelligence (AI) to enhance marketing analytics and improve understanding of customers. It discusses using machine learning and automation to enable real-time decision making and next best offers. It also covers using AI and machine learning to improve cross-selling and up-selling efforts. Additionally, it discusses using cognitive computing and sentiment analysis to better understand customer feedback and using cognitive computing and natural language processing to enhance customer service. The document also outlines transforming web analytics into digital intelligence and optimizing marketing with analytics and machine learning.
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.
Optimization of digital marketing campaignsArmando Vieira
This document discusses using machine learning techniques to optimize digital marketing campaigns. Specifically, it analyzes data from campaigns using clustering, visualization and predictive models. Unsupervised learning methods like k-means clustering, PCA, MDS and SOM are used to identify patterns in large digital data. Supervised models like SVMs and random forests predict conversions. The goal is to extract actionable insights to improve ROI, engagement and sales through optimization of parameters like ad design, keywords, bids, channels and budget allocation.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
This document describes a proposed algorithm for improving recommendation systems for e-services. It involves the following key steps:
1. Clustering customer transaction histories to group similar purchase patterns and derive customer-based recommendations.
2. Using incremental association rule mining on the transaction data to detect frequently purchased item sets and relationships between items.
3. Developing a fuzzy model to classify customers and provide dynamic recommendations tailored to different customer types. The recommendations will be based on matching customer preferences and purchase histories to specific product sets.
4. The algorithm clusters transactions, mines association rules incrementally as new data is added, and generates recommendations by classifying customers and matching them to relevant product clusters. This provides a personalized and
Customer Churn Prediction using Association Rule Miningijtsrd
Customer churn is one of the most important metrics for a growing business to evaluate. It is a business term used to describe the loss of clients or customers. In the retail sales and marketing company, customers have multiple choices of services and they frequently switch from one service to another. In these competitive markets, customers demand best products and services at low prices, while service providers constantly focus on getting hold of as their business goals. An increase in customer retention of just 5 can create at least a 25 increase in profit. Therefore, customer churn rate is important because it costs more to acquire new customers than it does to retain existing customers. In this paper, we apply the method to the retail sales and marketing company customer churn data set. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It will help the retail sales and marketing company to present the targeted customers with the estimated loss of clients or customers for the promotion in direct marketing. Mie Mie Aung | Thae Thae Han | Su Mon Ko "Customer Churn Prediction using Association Rule Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26818.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26818/customer-churn-prediction-using-association-rule-mining/mie-mie-aung
Dart builds sophisticated customer segmentation models using statistical techniques and intuition. The goal is to create distinct customer segments that are predictive of behavior and can be implemented for marketing purposes. Dart analyzes customer, transaction, and demographic data to develop segments. The segmentation process involves data preparation, analysis, model development, and finalizing the segments with descriptive profiles and financial analysis. Segments are monitored over time and recalibrated as needed to keep the segmentation strategy relevant.
This document discusses customer retention and churn prediction. It explains that traditional churn prediction models built by data scientists take 6 months to a year to develop and implement, while out-of-the-box solutions like Manthan's Customer360 can identify at-risk customers and execute retention campaigns immediately. Customer360 uses logistic regression to predict churn risk and helps marketers design personalized campaigns to retain profitable customers, like a retailer who stopped $6 million in revenue loss by retaining 22% of at-risk customers in a valuable segment.
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.
Project for System Analysis and Design (IS-6410).
By performing customer segmentation following are the three objectives which can be achieved
with the implementation of this new analytics system:
1. We can track the difference between loyal customers vs visitors, perform heat map
analysis of their browsing patterns.
2. Understanding customer demographics and to focus on high profitable segments.
3. Finally empowering our Marketing department to make better strategic decisions in
terms of online Ads/campaigns.
The Portuguese bank wants to increase sales of long-term deposits through a telemarketing campaign. The authors use logistic regression, decision trees, and neural networks on previous campaign data to build predictive models. They find that including external economic variables improves on a benchmark model using only internal variables. The decision tree and neural network models perform best at predicting successful calls. Combining the three models further increases profits from the campaign.
Consumer Behavior project. Examine and define best ways for Consumer Research Company (Equitec) to target and reach new customers, along with suggesting new ways for the company to market itself.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then discusses why data mining is needed due to the explosive growth of data from various sources. Data mining is defined as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. The knowledge discovery process involves identifying a problem, mining data to transform it into actionable information, acting on the information, and measuring the results. The document outlines different types of data that can be mined, including structured, transactional, time-series, spatial, multimedia, and web data. Common data mining tasks are also described such as classification, prediction, clustering,
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.
Predicting Bank Customer Churn Using ClassificationVishva Abeyrathne
This document describes a study that used classification models to predict customer churn for a bank. The authors collected a dataset of 10,000 bank customers from Kaggle and preprocessed the data. They then explored relationships between features and the target variable of whether a customer churned. Two classification models were tested - KNN and Decision Tree. After hyperparameter tuning, Decision Tree achieved the best accuracy of 84.25%, outperforming KNN. However, both models struggled to accurately predict customers who would churn. The authors concluded Decision Tree was the best model but recommend collecting more data on churning customers.
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.
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.
The document discusses marketing information systems and marketing research. It explains that marketing managers need regular information from various sources to deliver value to customers. An effective marketing information system gathers, analyzes and distributes accurate information to help managers make better decisions. The marketing research process involves defining problems/objectives, developing a research plan, implementing primary/secondary research, interpreting findings and reporting results. Both qualitative and quantitative research methods are discussed.
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.
1. Data mining in marketing involves analyzing customer data to discover useful patterns that can help increase sales and profits.
2. Several steps are typically involved in creating a data mining model, including defining problems, preparing and exploring data, building models, validating models, and deploying models.
3. Decision trees are a common algorithm used that classify customers based on attributes and predict outcomes based on paths from the tree's root nodes to leaf nodes.
Data Science Use Cases in Retail & Healthcare Industries.pdfKaty Slemon
Data science has many useful applications in retail and healthcare. In retail, it allows for personalized recommendations, fraud detection, price optimization, and sentiment analysis. In healthcare, it facilitates medical imaging analysis, genomic research, drug discovery, predictive analytics, disease tracking and prevention, and monitoring through wearable devices. By analyzing customer, patient, and other relevant data, data science helps these industries better meet needs, enhance experiences and outcomes, and improve operations and decision making.
Data mining techniques help companies, particularly in banking, telecommunications, insurance, and retail marketing, build accurate customer profiles based on customer behavior. Analyzing large amounts of customer data stored in data warehouses allows companies to better understand customers and make data-driven decisions in competitive environments.
Using Big Data & Analytics to Create Consumer Actionable Insights莫利伟 Olivier Maugain
The client was facing challenges like slowing growth rates, lack of understanding of customer needs, and over-reliance on their skincare category. The proposed solution was a cross-selling campaign using customer analytics to personalize communications. Customer data was analyzed using RFM, clustering, and predictive modeling to identify the most desirable targets and determine the best messages, offers, channels, and timing for each. This increased the response rate from 5% to 13.5%, saving $300,000 in costs while generating an additional $1.2 million in value for a 3.7x return on investment.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime ValueIIRindia
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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
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to properly allocated resources. Keywords: sales, customer,
technology, profit.
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Artificial Intelligence has been around for decades, but has
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analytics and to help companies both better understand their
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11.direct marketing with the application of data mining
1. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.6, 2011
Direct Marketing with the Application of Data Mining
M Suman, T Anuradha, K Manasa Veena*
KLUNIVERSITY,GreenFields,vijayawada,A.P
*Email: manasaveena_555@yahoo.com
Abstract
For any business to be successful it must find a perfect way to approach its customers. Marketing plays a
huge role in this. Mass marketing and direct marketing are the two types of it. Mass marketing targets
everybody in the society and thus it has less impact on valued customers where direct marketing
concentrates mainly on these valued and un loyal customers and promotes only to them which in turn
makes profits. For this separation of customers based on their loyalty data mining algorithms and tools are
used. In this paper we discussed the approach of implementation of data mining for direct marketing. We
mainly concentrated and studied on why we apply data mining for direct marketing, how we apply and
problems one faces while applying data mining concept for direct marketing and the solutions for them in
direct marketing.
Keywords: Direct marketing, Data mining,Decision tree.
1. INTRODUCTION
In marketing a product ,there are several forms of sub disciplines, and one of them is
direct marketing which involves messages sent directly to consumers usually through email, telemarketing
and direct mail.As the traditional forms of advertising (radio, newspapers, television,etc.) may not be the
best use of their promotional budgets, many companies or service providers with a specific market use this
method of marketing.
1.1 Where we use direct marketing
For example, a company which sells a hair loss prevention product or life insurance policy would have to
find a radio station whose format appealed to older male listeners who might be experiencing this problem.
There would be no guarantee that this group would be listening to that particular station at the exact time
the company's ads were broadcast. Money spent on a radio spot (or television commercial or newspaper ad)
may or may not reach the type of consumer who would be interested in a hair restoring product.
This is where direct marketing becomes very appealing. Instead of investing in a scattershot means of
advertising, companies with a specific type of potential customer can send out literature directly to a list of
pre-screened individuals. Direct marketing firms may also keep addresses of those who match a certain
age group or income level or special interest. Manufacturers of a new dog shampoo might benefit from
having the phone numbers and mailing addresses of pet store owners or dog show
participants. Direct marketing works best when the recipients accept the fact that their personal information
might be used for this purpose. Some customers prefer to receive targeted catalogues which offer more
variety than a general mailing.
2. DATA MINING
Data mining is the process of extracting hidden patterns from data. It is commonly used in a wide range of
profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data
from different perspectives and summarizing it into useful information - information that can be used to
increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for
analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and
summarize the relationships identified. Technically, data mining is the process of finding correlations or
patterns among dozens of fields in large relational databases.
2.1. Data mining in direct marketing
1
2. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.6, 2011
As we already discussed, in Direct marketing, concentrates on a particular group of customers( not loyal
and beneficial).So, the data mining technique called the Supervised Classification is used to classify the
customers for marketing.
2.2 The Process
DMT will extract customer data, append it with extensive demographic, financial and lifestyle information,
then identify hidden, profitable market segments that are highly responsive to promotions.
2.3 Decision tree
A Decision tree is a popular classification technique that results in flowchart like tree structure where each
node denotes test on a attribute value and each branch represents an outcome of test. The leaves represent
classes. Using Training data Decision tree generate a tree that consists of nodes that are rules and each leaf
node represents a classification or decision. The data usually plays important role in determining the quality
of the decision tree. If there are number of classes, then there should be sufficient training data available
that belongs to each of the classes. Decision trees are predictive models, used to graphically organize
information about possible options, consequences and end value. They are used in computing for
calculating probabilities.
Example-
CUSTOMER DATA
LOYAL
UNLOYAL
ACTION A ACTION B
Fig 1:A decicion tree based on customer’s loyalty
2.4 Building A Decision Tree In Direct Marketing
Decision-tree learning algorithms, such as ID3 or C4.5 are among the most powerful and popular predictive
methods for classification. So here in direct marketing we classify the customers on basis of their attributes
like sex, age, location, purchase history, feedback details etc.
2.5 Algorithm
C4.5 Builds decision trees from set of training data using the concept of Information entropy.
The training data is a set S = s1, s2... of already classified samples. Each sample si = x1, x2... is a vector
where x1, x2... represent attributes or features of the sample. The training data is augmented with a vector C
= c1, c2... where c1, c2... represent the class to which each sample belongs. At each node of the tree, C4.5
chooses one attribute of the data that most effectively splits its set of samples into subsets enriched in one
class or the other. Its criterion is the normalized information gain (difference in entropy) that results from
choosing an attribute for splitting the data. The attribute with the highest normalized information gain is
chosen to make the decision. The C4.5 algorithm then recurses on the smaller sublists. In general, steps in
C4.5 algorithm to build decision tree are:
1. Choose attribute for root node
2. Create branch for each value of that attribute
3. Split cases according to branches
4. Repeat process for each branch until all cases in the branch have the same class.
2
3. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.6, 2011
3. PROBLEMS IN CLASSIFICATION
According to Charles X.Ling and Chenghui Li,the classification of data base involves the following
situations:
In the first situation, some (say X%) of the customers in the database have already bought the product,
through previous mass marketing or passive promotion. X is usually rather small, typically around 1.Data
mining can be used to discover patterns of buyers, in order to single out likely buyers from the current non-
buyers,(100-X%)of all customers. More specifically, data mining for direct marketing in the first situation
can be discovered in:
1. Get the database of all customers, among which X% are buyers.
2. Data mining on the data set based on Geo-demographic information, transforming address and area
codes, deal with missing values, etc.
3. Applying algorithm to prepare objects, classes .
4. Evaluate the patterns formed by applying dmt on testing set.
5. Use the patterns found to predict likely buyers among the current non-buyers
6. Promote to the likely buyers(called rollout).
In the second situation, a brand new product is to be promoted to the customers in the data base, so none
of them are buyers. In this case, pilot study is conducted, in which a small portion(say 5%) of the customers
is choosen randomly as the target of promotion. Again, X% of the customers in the pilot group may
respond to the promotion. Then data mining is performed in the pilot group to find the likely buyers in the
whole database.
Specific problems encountered while data mining on data sets for direct marketing are
1. Imbalance class distribution: Because only a small amount of buyers are likely means positive but most
of the algorithms can work on this type of sets. they assume that 100% are unlikely. Many data mining and
machine learning researchers have recognized and studied this problem in recent years(Farwett &
Provost,19s96;Kubat, Holte, & Matwin; Lewis & Catleltl; Pazzani, Merz, Murphy, Ali, Hume & Brunk).
2. Predictive accuracy cannot be used as a suitable evaluation criterion for the data mining process.
Classifying can be difficult. Means considering likely buyers as non-buyers and non-buyers as buyers
should be avoided.
4.SOLUTIONS
Ranking of non-buyers makes it possible to choose any number of likely buyers for the promotion.It also
provides a fine distinction among chosen customers to apply different means of promotion.
Lift analysis has been widely used in database marketing previously(Hughes,1996).A lift reflects the
redistribution of responders in the testing set after the testing examples are ranked.
5. CONCLUSION
Direct marketing is widely used in the fields of marketing like telemarketing,direct mail marketing,email
marketing etc.,data mining is applied on this marketing strategy to avoid human flaws in classifying the
customers based on their loyalty.We discussed the problems one faces in applying the datamining for direct
marketing and discussed their solutions.
6. ACKNOWLEDGEMENTS
This work was supported by Mrs. T Anuradha Assoc.professor and Mr. M Suman Assoc. Professor
,Department of Electronics and Computer Science Engineering, KLUniversity.
7. REFERENCES
[1] J.R. Quinlan, Morgan Kaufmann, C4.5 Programs for Machine Learning. 1993.
3
4. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.6, 2011
[2] A.Berson, K. Thearling, and S.J. Smith, Building Data Mining Applications for CRM. McGraw-Hill.
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[3] G.K.Gupta, Introduction to data mining with case study ,Prentice Hall of India.2006.
[4] Mehmed Kantardzic, (2003), Data Mining: Concepts, Models,Methods, and Algorithms, John Wiley &
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[5] Farwett & Provost,19s96;Kubat, Holte, & Matwin;Lewis & Catleltl;Pazzani,Merz,Murphy,Ali, Hume &
Brunk.
[6]data-mine.com/white_papers/direct_marketing.
[7] The Application of Data Mining For Direct
Marketing Purushottam R Patil, Pravin Revankar, Prashant Joshi
Second International Conference on Emerging Trends in Engineering and Technology, ICETET-09.
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