Data mining techniques are widely used in various areas of marketing management for extracting useful information.Particularly in a business-to-customer (B2C) setting, it plays an important role in customer segmentation. A retailernot only tries to improve its relationship with its customers,but also enhances its business in a manufacturer-retailer-consumer chainwith respect to this information.Although there are various approaches for customer segmentation, we have used an analytic hierarchical process based data mining technique in this regard. Customers are segmented into six clusters based on Davis-Bouldin (DB) index and K-Means algorithm.Customer lifetime value (CLV)along four dimensions, viz., Length (L), Recency(R), Frequency (F) and Monetary value (M) are considered for these clusters. Then, we apply Saaty’s analytical hierarchical process (AHP) to determine the weights of these criteria, which in turn, helps in computing the CLV value for each of the clusters and their individual rankings. This information is quite important for a retailer to design promotional strategies for improving relationship between the retailer and its customers. To demonstrate the effectiveness of this methodology, we have implemented the model, taking a real life data-base of customers of an organization in the context of an Indian retail industry.
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.
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.
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.
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.
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.
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.
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
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.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
Abstract—Web mining is the amalgamation of information accumulated by traditional data mining methodologies and techniques with information collected over the World Wide Web. A Recommendation system is a profound application that comforts the user in a decision-making process, where they lack of personal experience to choose an item from the confound set of alternative products or services. The key challenge in the development of recommender system is to overcome the problems like single level recommendation and static recommendation, which are exists in the real world e-services. The goal is to achieve and enhance predicting algorithm to discover the frequent items, which are feasible to be purchasable. At this point, we examine the prior buying patterns of the customers and use the knowledge thus procured, to achieve an item set, which co-ordinates with the purchasing mentality of a particular set of customers. Potential recommendation is concerned as a link structure among the items within E-commerce website, which supports the new customers to find related products in a hurry. In Existing system, a fuzzy set consists of user preference and item features alone, so the recommendations to the customers are irrelevant and anonymous. In this paper, we suggest a recommendation technique, which practices the wild spreading and data sharing competency of a huge customer linkage and also this method follows a fuzzy tree- structured model, in which fuzzy set techniques are utilized to express user preferences and purchased items are in a clustered form to develop a user convenient recommendations. Here, an incremental association rule mining is employed to find interesting relation between variables in a large database.
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
A simulated decision trees algorithm (sdt)Mona Nasr
The customer's information contained in
databases has increased dramatically in the last few years.
Data mining is a good approach to deal with this volume of
information to enhance the process of customer services.
One of the most important and powerful techniques of data
mining is decision trees algorithm. It appropriate for large
and sophisticated business area but it's complicated, high
cost and not easy to use by not specialists in the field. To
overcome this problem SDT is proposed which is a simple,
powerful and low-cost proposed methodology to simulate the
decision trees algorithm for different business scopes and
nature. SDT methodology consists of three phases. The first
phase is the data preparation which prepare data for
computing calculations, the second phase is SDT algorithm
which represents a simulation of decision trees algorithm to
find the most important rules that distinguish specific type of
customers, the third phase is to visualize results and rules for
better understanding and clarifying the results. In this paper
SDT methodology is tested by a dataset consists of 1000
instants for German Credit Data belongs to one of German
bank customers. SDT selects the most important rules and
paths that reaches the selected ratio and tested cluster of
customers successfully with interesting remarks and finding.
Data Analytics for Digital Marketing Lecture for Advanced Digital & Social Me...Valters Lauzums
Unlock the power of data analytics with our comprehensive slide deck from the Advanced Digital Strategy MGMT X 466.05 course at UCLAx. This presentation reviews the fundamental concepts and practical applications of data analytics in business and marketing.
Key Topics Covered:
Overview & Concepts: Learn how data analytics uses statistics, predictive modeling, and machine learning to enhance business performance.
Types of Data: Understand the differences between structured and unstructured data, and how to leverage quantitative and qualitative data.
Key Techniques: Explore descriptive, diagnostic, predictive, and prescriptive analytics to transform raw data into actionable insights.
Common Tools: Get acquainted with popular tools like Google Analytics, Google Looker, Adobe Analytics, and HubSpot for effective data tracking and analysis.
Data Analysis Process: Follow a step-by-step guide to collecting, cleaning, modeling, and interpreting data to drive informed decision-making.
Optimizing Campaigns: Learn how to use A/B testing and past campaign performance data to enhance future marketing efforts.
Defining Audiences: Discover how to segment target audiences using demographic data, purchase histories, and online behaviors for more precise marketing strategies.
Advanced Methods: Dive into advanced data analysis techniques like cohort analysis, cluster analysis, sentiment analysis, and regression analysis.
Customer Journey Analytics: Visualize the customer journey and identify key engagement moments to optimize the customer experience.
Data Visualization & Storytelling: Master the art of communicating data insights effectively through visualizations and contextual storytelling.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant
contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity
logs and product items, accurate and efficient recommendation is a challenging computational task. This
paper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC)
algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data for
collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be
analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of
applications according to the accessibility of data sources by carefully adjust the weights of different data
sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product
co-clusters. The results show that our proposed approach provide more meaningful recommendation
results, and outperforms existing item-based and user-based collaborative filtering recommendations in
terms of accuracy and ranked position.
An Investigation into Brain Tumor Segmentation Techniques IIRindia
A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.
Agricultural sector is the backbone of our country and it plays a vital role in the overall economic growth of our nation. India has about 59% of its total area for agricultural purpose. The contribution of agricultural sector to our GDP is about 17%. Advanced techniques or the betterment in the arena of agriculture will as certain to increase the competence of certain farming activities. In this paper we introduce a concept for smart farming which utilizes wireless sensor web technology with a web based application. This will play a crucial role in helping farmers. It will aim for the betterment in the facilities given to the farmers and by focussing on the measurement of production of the crops. With the help of data mining techniques and algorithms like K-nearest, decision tree we will gather each and every data related to the farming and it should be updated frequently so that farmers and the consumers will get the right knowledge of the respective crops and about the suitable equipments related to farming. Existing system are not so much efficient in displaying such data characteristics. Our main aim is to enhance the growth in the agriculture sector and make the existing system smarter so that the decision- maker can define the expansion of agriculture activities to empower the different forces in existing agriculture sector
More Related Content
Similar to AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime Value
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
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.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
Abstract—Web mining is the amalgamation of information accumulated by traditional data mining methodologies and techniques with information collected over the World Wide Web. A Recommendation system is a profound application that comforts the user in a decision-making process, where they lack of personal experience to choose an item from the confound set of alternative products or services. The key challenge in the development of recommender system is to overcome the problems like single level recommendation and static recommendation, which are exists in the real world e-services. The goal is to achieve and enhance predicting algorithm to discover the frequent items, which are feasible to be purchasable. At this point, we examine the prior buying patterns of the customers and use the knowledge thus procured, to achieve an item set, which co-ordinates with the purchasing mentality of a particular set of customers. Potential recommendation is concerned as a link structure among the items within E-commerce website, which supports the new customers to find related products in a hurry. In Existing system, a fuzzy set consists of user preference and item features alone, so the recommendations to the customers are irrelevant and anonymous. In this paper, we suggest a recommendation technique, which practices the wild spreading and data sharing competency of a huge customer linkage and also this method follows a fuzzy tree- structured model, in which fuzzy set techniques are utilized to express user preferences and purchased items are in a clustered form to develop a user convenient recommendations. Here, an incremental association rule mining is employed to find interesting relation between variables in a large database.
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
A simulated decision trees algorithm (sdt)Mona Nasr
The customer's information contained in
databases has increased dramatically in the last few years.
Data mining is a good approach to deal with this volume of
information to enhance the process of customer services.
One of the most important and powerful techniques of data
mining is decision trees algorithm. It appropriate for large
and sophisticated business area but it's complicated, high
cost and not easy to use by not specialists in the field. To
overcome this problem SDT is proposed which is a simple,
powerful and low-cost proposed methodology to simulate the
decision trees algorithm for different business scopes and
nature. SDT methodology consists of three phases. The first
phase is the data preparation which prepare data for
computing calculations, the second phase is SDT algorithm
which represents a simulation of decision trees algorithm to
find the most important rules that distinguish specific type of
customers, the third phase is to visualize results and rules for
better understanding and clarifying the results. In this paper
SDT methodology is tested by a dataset consists of 1000
instants for German Credit Data belongs to one of German
bank customers. SDT selects the most important rules and
paths that reaches the selected ratio and tested cluster of
customers successfully with interesting remarks and finding.
Data Analytics for Digital Marketing Lecture for Advanced Digital & Social Me...Valters Lauzums
Unlock the power of data analytics with our comprehensive slide deck from the Advanced Digital Strategy MGMT X 466.05 course at UCLAx. This presentation reviews the fundamental concepts and practical applications of data analytics in business and marketing.
Key Topics Covered:
Overview & Concepts: Learn how data analytics uses statistics, predictive modeling, and machine learning to enhance business performance.
Types of Data: Understand the differences between structured and unstructured data, and how to leverage quantitative and qualitative data.
Key Techniques: Explore descriptive, diagnostic, predictive, and prescriptive analytics to transform raw data into actionable insights.
Common Tools: Get acquainted with popular tools like Google Analytics, Google Looker, Adobe Analytics, and HubSpot for effective data tracking and analysis.
Data Analysis Process: Follow a step-by-step guide to collecting, cleaning, modeling, and interpreting data to drive informed decision-making.
Optimizing Campaigns: Learn how to use A/B testing and past campaign performance data to enhance future marketing efforts.
Defining Audiences: Discover how to segment target audiences using demographic data, purchase histories, and online behaviors for more precise marketing strategies.
Advanced Methods: Dive into advanced data analysis techniques like cohort analysis, cluster analysis, sentiment analysis, and regression analysis.
Customer Journey Analytics: Visualize the customer journey and identify key engagement moments to optimize the customer experience.
Data Visualization & Storytelling: Master the art of communicating data insights effectively through visualizations and contextual storytelling.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant
contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity
logs and product items, accurate and efficient recommendation is a challenging computational task. This
paper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC)
algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data for
collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be
analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of
applications according to the accessibility of data sources by carefully adjust the weights of different data
sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product
co-clusters. The results show that our proposed approach provide more meaningful recommendation
results, and outperforms existing item-based and user-based collaborative filtering recommendations in
terms of accuracy and ranked position.
Similar to AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime Value (20)
An Investigation into Brain Tumor Segmentation Techniques IIRindia
A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.
Agricultural sector is the backbone of our country and it plays a vital role in the overall economic growth of our nation. India has about 59% of its total area for agricultural purpose. The contribution of agricultural sector to our GDP is about 17%. Advanced techniques or the betterment in the arena of agriculture will as certain to increase the competence of certain farming activities. In this paper we introduce a concept for smart farming which utilizes wireless sensor web technology with a web based application. This will play a crucial role in helping farmers. It will aim for the betterment in the facilities given to the farmers and by focussing on the measurement of production of the crops. With the help of data mining techniques and algorithms like K-nearest, decision tree we will gather each and every data related to the farming and it should be updated frequently so that farmers and the consumers will get the right knowledge of the respective crops and about the suitable equipments related to farming. Existing system are not so much efficient in displaying such data characteristics. Our main aim is to enhance the growth in the agriculture sector and make the existing system smarter so that the decision- maker can define the expansion of agriculture activities to empower the different forces in existing agriculture sector
A Survey on the Analysis of Dissolved Oxygen Level in Water using Data Mining...IIRindia
Data Mining (DM) is a powerful and a new field having various techniques to analyses the recent real world problems. In DM, environmental mining is one of the essential and interesting research areas. DM enables to collect fundamental insights and knowledge from massive volume of environmental data. The water quality is determining the condition of water in the environment. It represents the concentration and state (dissolved or particulate) of some or all the organic and inorganic material present in the water, together with certain physical characteristics of the water. The Dissolved Oxygen (DO) is one of the important aspects of water quality. The DO is the quantity of gaseous oxygen (O2) incorporated into the water. The DO is essential for keeping the water organisms alive. The amount of DO level in the water can be detected by various methods. The data mining techniques are properly used to find DO Level in the different types of water. A number of DM methods used to analyze the DO level such as Multi-Layer Perceptron, Multivariate Linear Regression, Factor Analysis, and Feed Forward Neural Network. This survey work discusses about such type of methods, particularly used for the analysis of DO level elaborately.
Kidney Failure Due to Diabetics – Detection using Classification Algorithm in...IIRindia
In order to analyse the chosen data from various points of view, data mining is used as the effective process. This process is also used to sum-up all those views into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression, Segmentation algorithms, association algorithms, sequence analysis algorithms, etc.,. The classification algorithm can be used to bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original data set S as the root node. An unutilised attribute of the data set S calculates the entropy H(S) (or Information gain IG (A)) of the attribute. Upon its selection, the attribute should have the smallest entropy (or largest information gain) value. The prime objective of this paper is to analyze the data from a Kidney disorder due to diabetics by using classification technique to predict class accurately.
Silhouette Threshold Based Text Clustering for Log AnalysisIIRindia
Automated log analysis has been a dominant subject area of interest to both industry and academics alike. The heterogeneous nature of system logs, the disparate sources of logs (Infrastructure, Networks, Databases and Applications) and their underlying structure & formats makes the challenge harder. In this paper I present the less frequently used document clustering techniques to dynamically organize real time log events (e.g. Errors, warnings) to specific categories that are pre-built from a corpus of log archives. This kind of syntactic log categorization can be exploited for automatic log monitoring, priority flagging and dynamic solution recommendation systems. I propose practical strategies to cluster and correlate high volume log archives and high velocity real time log events; both in terms of solution quality and computational efficiency. First I compare two traditional partitional document clustering approaches to categorize high dimensional log corpus. In order to select a suitable model for our problem, Entropy, Purity and Silhouette Index are used to evaluate these different learning approaches. Then I propose computationally efficient approaches to generate vector space model for the real time log events. Then to dynamically relate them to the categories from the corpus, I suggest the use of a combination of critical distance measure and least distance approach. In addition, I introduce and evaluate three different critical distance measures to ascertain if the real time event belongs to a totally new category that is unobserved in the corpus.
Analysis and Representation of Igbo Text Document for a Text-Based SystemIIRindia
The advancement in Information Technology (IT) has assisted in inculcating the three Nigeria major languages in text-based application such as text mining, information retrieval and natural language processing. The interest of this paper is the Igbo language, which uses compounding as a common type of word formation and as well has many vocabularies of compound words. The issues of collocation, word ordering and compounding play high role in Igbo language. The ambiguity in dealing with these compound words has made the representation of Igbo language text document very difficult because this cannot be addressed using the most common and standard approach of the Bag-Of-Words (BOW) model of text representation, which ignores the word order and relation. However, this cause for a concern and the need to develop an improved model to capture this situation. This paper presents the analysis of Igbo language text document, considering its compounding nature and describes its representation with the Word-based N-gram model to properly prepare it for any text-based application. The result shows that Bigram and Trigram n-gram text representation models provide more semantic information as well addresses the issues of compounding, word ordering and collocations which are the major language peculiarities in Igbo. They are likely to give better performance when used in any Igbo text-based system.
A Survey on E-Learning System with Data MiningIIRindia
E-learning process has been widely used in university campus and educational institutions are playing vital role to enhance the skill set of students. Modern E-learning done by many electronic devices, such as smartphones, Tabs, and so on, on existing E-learning tools is insufficient to achieve the purpose of online training of education. This paper presents a survey of online e-Learning authoring tools for creating and integrating reusable e-learning tool for generation and enhancing existing learning resources with them. The work concentrates on evaluation of the existing e-learning tools a, and authoring tools that have shown good performance in the past for online learners. This survey work takes more than 20 online tools that deal with the educational sector mechanism, for the purpose of observations, and the outcome were analyzed. The findings of this paper are the main reason for developing a new tool, and it shows that educators can enhance existing learning resources by adding assessment resources, if suitable authoring tools are provided. Finally, the different factors that assure the reusability of the created new e-learning tool has been analysed in this paper.E-learning environment is a guide for both students and tutorial management system. The useful on the e-learning system for apart from students and distance learning students. The purpose of using e-learning environment for online education system, developed in data mining for more number of clustering servers and resource chain has been good.
Image Segmentation Based Survey on the Lung Cancer MRI ImagesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
The Preface Layer for Auditing Sensual Interacts of Primary Distress Conceali...IIRindia
Resting anterior brain electrical activity, self-report measures of Behavioral Approach System (BAS) and Behavioral Inhibition System (BIS) strength, and common levels of Positive Affect (PA) and Negative Affect (NA) were composed from 46 unselected undergraduates two split occasions Electroencephalogram (EEG) measures of prefrontal asymmetry and the self-report measures showed excellent internal reliability, steadiness and tolerable test-retest stability. Strong connection betweens the unconstrained facial emotional expressions and the full of feeling states correlated cerebrum movement. When seeing dreadful as contrasted with unbiased faces, members showed larger amounts of actuation inside the privilege average prefrontal cortex (PFC). To propose a multimodal method to deal with assess Efficient Practical near Infrared Spectroscopy (EPNIS) signals and EEG signals for full of feeling state identification. Outcomes demonstrate that proposed technique with EPNIS enhances execution over EPNIS methodologies. Based on
Feature Based Underwater Fish Recognition Using SVM ClassifierIIRindia
An approach for underwater fish recognition based on wavelet transform is presented in this paper. This approach decomposes the input image into sub-bands by using the multi resolutional analysis known as Discrete Wavelet Transform (DWT). As each sub-band in the decomposed image contains useful information about the image, the mean values of every sub-band are assumed as features. This approach is tested on Underwater Photography - A Fish Database. The database contains 7953 pictures of 1458 different species. The database is considered for the classification based on Support Vector machine (SVM) classifier. The result shows that maximum recognition accuracy of 90.74% is achieved by the wavelet features.
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
The objective of this research work is focused on the right cluster creation of lung cancer data and analyzed the efficiency of k-Means and k-Medoids algorithms. This research work would help the developers to identify the characteristics and flow of algorithms. In this research work is pertinent for the department of oncology in cancer centers. This implementation helps the oncologist to make decision with lesser execution time of the algorithm.It is also enhances the medical care applications. This work is very suitable for selection of cluster development algorithm for lung cancer data analysis.Clustering is an important technique in data mining which is applied in many fields including medical diagnosis to find diseases. It is the process of grouping data, where grouping is recognized by discovering similarities between data based on their features. In this research work, the lung cancer data is used to find the performance of clustering algorithms via its computational time. Considering a limited number attributes of lung cancer data, the algorithmic steps are applied to get results and compare the performance of algorithms. The partition based clustering algorithms k-Means and k-Mediods are selected to analyze the lung cancer data.The efficiency of both the algorithms is analyzed based on the results produced by this approach. The finest outcome of the performance of the algorithm is reported for the chosen data concept.
A Study on MRI Liver Image Segmentation using Fuzzy Connected and Watershed T...IIRindia
A comparison study between automatic and interactive methods for liver segmentation from contrast-enhanced MRI images is ocean. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to refer five error measures that highlight different aspects of segmentation accuracy. The measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods like Fuzzy Connected and Watershed Methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques. In this paper only Fuzzy Connected and Watershed Methods are discussed.
A Clustering Based Collaborative and Pattern based Filtering approach for Big...IIRindia
With web services developing and aggregating in application range, benefit revelation has turned into a hot issue for benefit organization and service management. Service clustering gives a promising approach to part the entire seeking space into little areas in order to limit the disclosure time successfully. In any case, semantic data is a basic component amid the entire arranging process. Current industrialized Web Service Portrayal Language (WSPL) does not contain enough data for benefit depiction. Thusly, a service clustering technique has been proposed, which upgrades unique WSPL report with semantic data by methods for Connected Open Information (COI). Examination based genuine service information has been performed, and correlation with comparable techniques has additionally been given to exhibit the adequacy of the strategy. It is demonstrated that using semantic data from COI improves the exactness of service grouping. Furthermore, it shapes a sound base for promote thorough preparing with semantic data.
Hadoop and Hive Inspecting Maintenance of Mobile Application for Groceries Ex...IIRindia
Numerous movable applications on secure groceries expenditure and e-health have designed recently. Health aware clients respect such applications for secure groceries expenditure, particularly to avoid irritating groceries and added substances. However, there is the lack of a complete database including organized or unstructured information to help such applications. In the paper propose the Multiple Scoring Frameworks (MSF), a healthy groceries expenditure search service for movable applications using Hadoop and MapReduce (MR). The MSF works in a procedure behind a portable application to give a search service for data on groceries and groceries added substances. MSF works with similar logic from a web search engine (WSE) and it crawls over Web sources cataloguing important data for possible utilize in reacting to questions from movable applications. MSF outline and advancement are featured in the paper during its framework design, inquiry understanding, its utilization of the Hadoop/MapReduce infrastructure, and activity contents.
Performance Evaluation of Feature Selection Algorithms in Educational Data Mi...IIRindia
Educational Data mining(EDM)is a prominent field concerned with developing methods for exploring the unique and increasingly large scale data that come from educational settings and using those methods to better understand students in which they learn. It has been proved in various studies and by the previous study by the authors that data mining techniques find widespread applications in the educational decision making process for improving the performance of students in higher educational institutions. Classification techniques assumes significant importance in the machine learning tasks and are mostly employed in the prediction related problems. In machine learning problems, feature selection techniques are used to reduce the attributes of the class variables by removing the redundant and irrelevant features from the dataset. The aim of this research work is to compares the performance of various feature selection techniques is done using WEKA tool in the prediction of students’ performance in the final semester examination using different classification algorithms. Particularly J48, Naïve Bayes, Bayes Net, IBk, OneR, and JRip are used in this research work. The dataset for the study were collected from the student’s performance report of a private college in Tamil Nadu state of India. The effectiveness of various feature selection algorithms was compared with six classifiers and the results are discussed. The results of this study shows that the accuracy of IBK is 99.680% which is found to be
A Review of Edge Detection Techniques for Image SegmentationIIRindia
Edge detection is a key stride in Image investigation. Edges characterize the limits between areas in a image, which assists with division and article acknowledgment.Edge discovery is a image preparing method for finding the limits of articles inside Image. It works by distinguishing irregular in brilliance and utilized for Image division and information extraction in zones, for example, Image preparing, PC vision and Image vision. There are likely more algorithms in a writing of upgrading and distinguishing edges than whatever other single subject.In this paper, the principle is to concentrate most usually utilized edge methods for Image segmentation.
Leanness Assessment using Fuzzy Logic Approach: A Case of Indian Horn Manufac...IIRindia
Lean principles are being implemented by many industries today that focus on improving the efficiency of the operations for reducing the waste, efforts and consumption. Organizations implementing lean principles can be assessed using the some tools. This paper attempts to assess the lean implementation in a leading Horn manufacturing industry in South India. The twofold objectives are set to be achieved through this paper. First is to find the leanness level of a manufacturing organization for which a horn manufacturing company has been selected as the case company. Second is to find the critical obstacles for the lean implementation. The fuzzy logic computation method is used to extract the perceptions about the particular variables by using linguistic values and then match it with fuzzy numbers to compute the precise value of the leanness level of the organization. Based on the results obtained from this analysis, it was found that the case study company has performed in the lean to vey lean range and the weaker areas have been identified to improve the performance further.
Comparative Analysis of Weighted Emphirical Optimization Algorithm and Lazy C...IIRindia
Health care has millions of centric data to discover the essential data is more important. In data mining the discovery of hidden information can be more innovative and useful for much necessity constraint in the field of forecasting, patient’s behavior, executive information system, e-governance the data mining tools and technique play a vital role. In Parkinson health care domain the hidden concept predicts the possibility of likelihood of the disease and also ensures the important feature attribute. The explicit patterns are converted to implicit by applying various algorithms i.e., association, clustering, classification to arrive at the full potential of the medical data. In this research work Parkinson dataset have been used with different classifiers to estimate the accuracy, sensitivity, specificity, kappa and roc characteristics. The proposed weighted empirical optimization algorithm is compared with other classifiers to be efficient in terms of accuracy and other related measures. The proposed model exhibited utmost accuracy of 87.17% with a robust kappa statistics measurement and roc degree indicated the strong stability of the model when compared to other classifiers. The total penalty cost generated by the proposed model is less when compared with the penalty cost of other classifiers in addition to accuracy and other performance measures.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Final project report on grocery store management system..pdf
AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime Value
1. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.28-34
ISSN: 2278-2419
28
AHP Based Data Mining for Customer
Segmentation Based on Customer Lifetime Value
Manidatta Ray1
,B. K. Mangaraj2
1
Asst. Professor (Decision Science and Operations Management Area), Birla Institute of Management Technology (BIMTECH),
Bhubaneswar, India
2
Professor (Production, Operations and Decision Sciences Area), XLRI, Jamshedpur, India
E-Mail: manidatta.ray@gmail.com, manidatta.ray@bimtech.ac.in
Abstract- Data mining techniques are widely used in various
areas of marketing management for extracting useful
information. Particularly in a business-to-customer (B2C)
setting, it plays an important role in customer segmentation. A
retailer not only tries to improve its relationship with its
customers, but also enhances its business in a manufacturer-
retailer-consumer chain with respect to this information.
Although there are various approaches for customer
segmentation, we have used an analytic hierarchical process
based data mining technique in this regard. Customers are
segmented into six clusters based on Davis-Bouldin (DB)
index and K-Means algorithm. Customer lifetime value (CLV)
along four dimensions, viz., Length (L), Recency (R),
Frequency (F) and Monetary value (M) are considered for
these clusters. Then, we apply Saaty’s analytical hierarchical
process (AHP) to determine the weights of these criteria, which
in turn, helps in computing the CLV value for each of the
clusters and their individual rankings. This information is quite
important for a retailer to design promotional strategies for
improving relationship between the retailer and its customers.
To demonstrate the effectiveness of this methodology, we have
implemented the model, taking a real life data-base of
customers of an organization in the context of an Indian retail
industry.
Keywords: B2C marketing, Clustering, Customer life- time
value; Value-based segmentation
I. INTRODUCTION
The field of data mining (DM) has its origins in statistics and
machine learning. It involves an iterative process within which
progress is defined by discovery, either through automatic or
manual methods. It is the process of extracting valuable
information and knowledge from huge amounts of data
(Hanand Kamber, 2006) that uses various techniques, viz.,
computing, mathematical, optimization, and statistical to
extract and identify hidden patterns and subsequently gain
knowledge from large databases. For example, from a
customer data-base, the technology involved in data mining
can provide business intelligence to generate new opportunities
and hence, is most useful in an exploratory analysis scenario
where there are no predetermined notions about what will
constitute an ‘‘interesting’’ outcome. A major benefit of using
a data mining technique is that, it bypasses the knowledge
acquisition bottleneck by unearthing the patterns or knowledge
from the data itself. However, these methods obviate the need
for eliciting knowledge from human experts. The major task of
data mining can be classified into two categories, viz.,
descriptive and predictive. Clustering is an instance of
descriptive methods, whereas classification is an example of
predictive methods (Han and Kamber, 2006).
The techniques of data mining help to accomplish its goal by
extracting or detecting hidden customer characteristics and
behaviours from large databases. However, the main feature of
it is to build a model from data where the technique can also
perform one or more of the following types of data modelling:
(1) Association; (2) Classification; (3) Clustering; (4)
Forecasting; (5) Regression; (6) Sequence discovery; (7)
Visualization etc.. Some of the widely used data mining
algorithms involve: (1) Association rules; (2) Data
envelopment analysis; (3) Decision trees; (4) Genetic
algorithms; (5) Neural networks; and (6) Linear/logistic
regression analyses. Association rules try to establish
relationships that exist between data-items in a given record.
Apriori algorithms and statistical methods are commonly used
tools for association modelling. Classification is one of the
most common learning models and some of the frequently used
techniques for this are neural networks, decision-trees and if-
then-else rules. Clustering is the process of segmenting a
heterogeneous population into a number of homogenous
clusters where neural networks and discrimination analyses are
used to determine these clusters. From a record’s pattern,
forecasting estimates the future value of an outcome based on
commonly used tools that include neural networks, survival
analyses, regression analyses etc. Linear regression and logistic
regression are some of the popularly used tools for forecasting.
Sequence discovery is the process of identification of
associations or patterns over time and tools, such as, set theory
and statistical methods are used for the purpose. Visualization
refers to the presentation of data so that users can view
complex patterns. This is also used in conjunction with other
data mining models to provide a clearer understanding of
discovered patterns. Examples of visualization model are 3D
graphs, ‘‘Hygraphs” and ‘‘SeeNet”.
.Various techniques of data mining are used in different areas
of marketing management, such as, customer relationship
management (CRM) (Ngai et al., 2009), market basket analysis
(Berry and Linoff, 2004), customer churn prediction
(Coussement and Van den Poel, 2008) etc. Customer
segmentation is also another important application area of data
mining, particularly for clustering in CRM. It involves
partitioning the customer-base into a number of smaller
homogeneous customer segments according to their similarity
based on several techniques as discussed. From the literature, it
can be seen that, most of the researches in the CRM area
belong to the B2B (business-to-Business) setting. However, in
this study, we have considered a B2C (Business-to-Customer)
2. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.28-34
ISSN: 2278-2419
29
set-up and addressed the issue taking a real life case of a
manufacturer-retailer-consumer chain (MRCC). This is
because, the role of retailers is crucial in persuading consumers
to purchase products of a typical manufacturer in such a chain.
As product homogeneity for any product increases the number
of choices for consumers, it complicates the decision-making
process and in such a situation, any recommendation from the
retailer regarding a particular brand or product influences
customers' purchase decisions. As a result, by improving its
relationship with customers, a retailer can gain greater benefits
and in this regard, customer segmentation enables retailers to
better understand them in order to adopt right and segment-
specific marketing strategies for them. Consequently,
execution of such a segment-specific marketing program leads
to a profitable as well as long-term relationship between
retailer and its customers.
In this study, we propose a methodology for value-based
customer segmentation using a data mining technique
involving analytic hierarchical process (AHP), as suggested by
Saaty (1980). Four dimensions of customer life- time value
(CLV) are considered as the criteria to determine the weights
of these segments. In section 2, we discuss the relevance of
B2C marketing in the context of a retailing environment and
the concept of CLV for customer segmentation in terms of four
dimensions, viz., viz., Length (L), Recency (R), Frequency (F)
and Monetary value (M). We also highlight the necessity of
identifying optimum clusters and associating weighted LRFM
values to each of the clusters for their differentiation. Section 3
discusses the AHP based methodology for the study in detail.
In section 4, an empirical study, taking a real life data of a firm
in the context of an Indian retail industry, is presented
considered for the implementation. Section 5 concludes the
study stating the scope for further research in this direction.
II. BACKGROUND
2.1 Business-to-Customer market segmentation
Business-to-Business (B2B) organizations don’t sell their
products or services to end customers directly and they do that
via intermediaries. For example, the manufacturer of a typical
product distributes its products to some retailers, and then they
sell those items to the end-customers. However, the success of
a B2B organization depends on its intermediaries. For instance,
in a MRCC, the manufacturer needs to rely on the co-
operations from the retailers in order to sell a large volume of
products to make profit. Therefore, identifying the high value
and profitable retailers is an essential task for the manufacturer,
and in this regard, segmentation tools help in identifying
different groups of retailers. Some studies have been made in
this area, which focused on customer loyalty. For instance,
Lam et al. (2004) proposed and analyzed a conceptual
framework for identifying factors affecting customer loyalty in
a B2B context, including customer perceived value, customer
satisfaction, and switching costs. Davis-Sramek et al. (2009)
also investigated factors influencing retailer’s loyalty in the
supply chain for consumer durable productsHowever, in the
era of modern retailing, business-to-customer (B2C) approach
is very much important due to varied type of customers in the
Indian marketing environment. Hence, in this context,
customer segmentation for a retailer plays an important role in
designing various customer-specific marketing strategies.
There are numerous approaches in this regard and are divided
into customer need-based, characteristics-based (Greengrove,
2002) and value-based segmentation (Kim et al., 2006). In the
literature, several data mining techniques have used these
concepts to group customers in different businesses and
industries, such as hardware retailing (Liu and Shih, 2005),
retail industry (Ho Ha, 2007), textile manufacturing (Li et al.,
2011), electric utility (Lopez et al., 2011) and so on. In the
present study, we have used value-based segmentation in a
MRCC to identify different groups of customers according to
their differential values. Customer segmentation based on
customer value is an approach that identifies profitable
customers and to develop strategies to target them. Customer
value is often known as LTV (Life Time Value), CLV
(Customer Lifetime Value), CE (Customer Equity) and
customer profitability (Kim et al., 2006). According to Kottler
(1974), CLV is “the present value of the future profit stream
expected over a given time horizon of transacting with the
customer”. Various models are developed in the literature for
measuring CLV (Gupta et al., 2006), among which, the RFM
(Recency, Frequency and Monetary) model developed by
Hughes (1994) is an important one. Chang and Tasy (2004)
extended the RFM model by adding another dimension, i.e.,
the customer relation length (L) to it, thereby developed the
LRFM model.
III. WEIGHTED LRFM
The RFM model has three dimensions: (1) Recency: is the time
interval between the last purchase and a present time reference;
the shorter the time interval, the bigger R is; (2) Frequency: is
the number of customer’s purchases in a particular period; a
higher frequency is more valuable and (3) Monetary value: the
total amount of money consumed by the customer over a
particular time period; the higher the monetary value, the
bigger is the contribution to business. Although RFM and its
successor LRFM made it possible to assess CLV, there are also
some challenges to use them in an effective manner. The major
challenge relates to the importance of four variables, viz., L, R,
F, and M, followed by the determination of their corresponding
importance in the assessment environment. Experts have
differing views on this issue. For instance, regarding the RFM
model, Hughes (1994) showed that the importance (weight) of
the three variables is equal, while Stone (1995) considered
different weights for the RFM variables. However, the weight
of each RFM variable depends on the characteristics of the
industry. Some researchers have already used the weighted
RFM model (e.g. Liu and Shih, 2005; Seyed Hosseini et al.,
2010) in their studies. It is important to note that, in the studies
that used weights in the RFM and LRFM models (e.g. Liu and
Shih, 2005; Seyed Hosseini et al., 2010), no relationship was
found between the variables. As a result, the LRFM variables
were considered as independent. For example, the high
frequency does not affect the high monetary value and vice
versa. In this context, we determine the weights (relative
importance) of each LRFM variable based on the inputs of a
survey.
IV. CLUSTERING
Clustering, which is a subset of unsupervised learning
techniques, is the process of grouping a set of objects into
3. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.28-34
ISSN: 2278-2419
30
classes of similar objects. There are many clustering methods,
including partitioning methods, hierarchical methods, density-
based methods, grid-based methods, and model based methods
(Han and Kamber, 2006). K-means clustering forms the
category of partitioning methods and is the most widely used
clustering algorithm in CRM and marketing. In this regard, this
algorithm introduced by MacQueen (1967) can process large
amounts of data quickly. The operation of K-means clustering
is as follows: (1) selecting K initial centroids; (2) assigning
each object to its closest centroid; (3) updating the centroid of
each cluster to the mean of its constituent instances; and (5)
repeating steps 2 and 3 until centroids stop changing.
Table 1: Numbers representing paired comparison judgments
Comparison
Importance
Description
1 Equal
2 Intermediate between equal and
moderately dominant
3 Moderately dominant
4 Intermediate between moderately and
strongly dominant
5 Strongly dominant
6 Intermediate between strongly and very
strongly dominant
7 Very Strongly dominant
8 Intermediate between very strongly and
extremely dominant
9 Extremely dominant
Fig. 1: The Proposed Methodology
Analytical Hierarchy process (AHP): The Analytic Hierarchy
Process (AHP) developed by Saaty (1980) is a method for
multi-criteria decision-making. It is useful for assessing
multiple alternatives with respect to multiple numbers of
criteria based on human assessment. It uses paired comparison
judgments from a fundamental scale of absolute numbers
approached by decision-makers to prioritize alternatives for a
problem in an architectural structure (Saaty, 2003). Decision-
makers assign a number for each pairwise comparison in a 1 to
9 to point scale (Table 1). This method also measures the
degree of inconsistency between judgments. If the degree of
inconsistency exceeds 0.1, then the judgments must be revised.
Methodology Our proposed methodology for customer
segmentation is shown diagrammatically in Figure 1. Here, we
use the LRFM model in determining the value of each
customer.
V. THE EMPIRICAL STUDY
5.1 Customer Data
The case study in this research is a large super- market store in
Indian context, having different sections, such as apparel, men,
ladies, kids, personal grooming, toys and gifts, home shops,
and shoes and accessories. Each of these sections has a wide
range of products. This retail firm has three stores in Odisha
and currently is also expanding to Bengaluru as a first step to
become national retailer. The management of this store is
interested to rank customer groups based on their values, so
that appropriate marketing strategies can be developed for
them. For improving retailer-customer relationship, this
requires customer segmentation as a first step that has an
important role in determining these strategies.
5.2 Data Processing
Data Processing is an important step in data mining
methodology, as it improves the accuracy and efficiency of
subsequent modelling (Han and Kamber, 2006; Tan et al.,
2005). In this paper, data processing techniques such as, data-
cleaning, data- transformation, data- integration and data-
reduction are used to improve the quality of data for clustering.
Customers who didn’t make any purchase during last one year
are removed from the data-set. After performing this step, we
reach to a data- set with 1600 customers. From the integrated
data-set, the L, R, F and M variables are extracted for each
customer. In this case, the
L value (customer relationship length) is computed six-
monthly; because of large value of L has negative effect on
clustering.
5.4 Determining LRFM weights by the AHP
In this paper, the AHP method of Saaty (1980) is used for
calculating the LRFM weights according to the opinion of
decision makers. This is done through a 3- step process,
according to the AHP explanation. First, four decision makers
from the three different management layers of the sales
department are selected for making paired comparisons. In the
second step, the inconsistency index is computed and checked
for each decision-maker’s judgement. Finally, LRFM weights
are determined by computing eigen values of the judgement
matrix and found to be 0.238, 0.088, 0.326 and 0.348
respectively.
5.3 Finding the K-optimum by the Davies-Bouldin index
Many clustering algorithms have been introduced in the past;
however, there is no best algorithm in this regard. In fact, due
to the exploratory nature of clustering, seeking the best
clustering algorithm is meaningless. Yet, the K-means
algorithm is the most popular partitioning algorithm (Jain,
2010) as can be seen in the literature. According to Jain (2010),
despite of being proposed over 50 years ago, K-means is still
one of the most widely used clustering algorithms.
4. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.28-34
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31
Fig.2. LRFM weights obtained from AHP
The main reasons for the popularity of K-means are its easy
implementation, simplicity, efficiency, and practical success.In
this study, we use the K-means algorithm for clustering
customers and require determining the number of clusters. As
the improper selection of k as the number of clusters may lead
to inaccurate results, there are useful clustering quality indexes
that can help in determining the optimal number of clusters. In
this study, we use the Davis-Bouldin index (Davis and
Bouldin, 1979) for this purpose, where the aim of this index is
to identify the sets of clusters that have small intra-cluster
distances and large inter-cluster distances. This index is
defined as:
DB =
k
i
j
i
j
i
j
i
C
C
d
a
a
k 1
)
,
(
max
1
(1)
where, k is the number of clusters; ai is the intra-cluster
distance of cluster i ; and d(Ci , Cj) represents the
inter-cluster distance between clusters i and j . The number of
clusters that minimizes the DB index is taken as the optimal
number of clusters.
5.5 Clustering by K-Means based on the LRFM variables
In this stage, customers are segmented into the number of
clusters, as identified in the previous step, using k-means and
LRFM variables. In this case, the number of clusters for the k-
means algorithm is set to be 6. Hence, after performing the
clustering algorithm, we obtain the clusters as shown in the
table- 2.
Cluste
r
#
Customer
s
Lengt
h
Recenc
y
Frequenc
y Monetary
1
151
506.83 201.19 15.28
360301.4
9
2
350
512.2 197.14 14.55
234518.5
1
3 135 504.04 215.15 14.6 41914.52
4 307 517.8 194.41 15.09 96540.38
5
305
520.86 205.48 15.3
305767.3
7
6
352
522.49 197.37 16.34
163056.8
5
Table-2 Clustering Results
5.6 Calculating the values of Clusters
To calculate the value of each customer segment, we normalise
the LRFM variables for centroids by using the Min-Max
normalization method which has been discussed by Han and
Kamber (2006). Having normalised the LRFM values, we
calculate the CLV of each cluster as follows:
CJ = WL C jL+ WR C jR+ WF C jF+ WM C jM (2)
Where Cj : LRFM rating for cluster j,
C jL, C jR, C jF, C jM: normalised values of L, R, F and M for
cluster j, and
WL, WR, WF, WM : weights of L, R, F and M obtained
from AHP.
Table 3: Clusters information
Clus
ter
#
Custo
mers
Lengt
h
Rece
ncy
Freque
ncy
Mone
tary
CLV
CL
V
Rati
ng
1 151 120.6
266
17.70
432
4.9806
75
12538
4.9
1255
28.2
1
2 350 121.9
043
17.34
857
4.7437
66
81612
.44
8175
6.44
3
3 135 119.9
608
18.93
304
4.7596
14586
.25
1472
9.91
6
4 307 123.2
367
17.10
812
4.9207
95
33596
.05
3374
1.32
5
5 305 123.9
652
18.08
241
4.9861
97
10640
7
1065
54.1
2
6 352 124.3
516
17.36
875
5.3280
63
56743
.79
5689
0.83
4
4.7 Ranking and analysing the clusters according to lifetime
values
After calculating the CLV for each cluster, we rank the clusters
according to their CLV values which can help the managers to
allocate marketing resources according to the profitability of
each segment. In addition, an in-depth analysis of each
segment with respect to LRFM also informs the firm about the
purchasing behavior of customers in each segment. This
enables marketing managers to develop effective marketing
strategies that can lead to a profitable long-term relationship
with the customers. In order to analyze the clustering results,
we employ the customer value and customer loyalty matrices
for the purpose. The customer-value matrix proposed by
Marcus (1998) uses two parameters, viz., the customer buying
frequency (F) and the monetary value (M) as its two axes.
Fig. 3: Buying Frequency (F)
Two other indicators, such as, customer relationship length (L)
and recency (R) relate to customer loyalty, and we can also
consider them in the customer loyalty matrix. According to the
5. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.28-34
ISSN: 2278-2419
32
pooled opinion of some marketing experts, new customers are
those who have launched their relationship with the firm in the
last 1.5 years (three six month periods). Based on this
assumption, we consider the customers with their L (length of
relationship) lower than 3 as the new customers, and those with
their L higher than 3 as the long life (established) customers.
For the recency indicator, the two states, viz., Low and High
are taken. If the recent transaction time of a cluster is smaller
than the average total value, it is considered a High Recency
value cluster; otherwise, it is regarded as Low Recency.
Similarly, Frequency (F) and monetary (M) dimensions have
the same states: Low and High. If the frequency/or monetary
value of a cluster is smaller than the median point, it is termed
as a Low frequency/or monetary value cluster; otherwise, it is a
High frequency/or monetary value cluster. After analyzing
each segment, we label each cluster according to its status in
these variables. Furthermore, we also suggest some possible
actions that can be taken, in order to improve the relationship
between the firm and customers.
Fig.4 : Clusters Status
Table-4 : Cluster Labeling
Cluster Cluster
Label
Description Possible
actions /
Cluster specific
Strategies
C1 Platinum
Segment
The highest
value, the
moderate
frequency, the
moderate
recency, and
the highest
lifetime.
Special
attention
should be paid
in order to
retain
customers of
this segment
C5 Diamond
Segment
The second
highest value,
second highest
frequency,
second highest
recency, and a
high lifetime.
There are many
customers
belonging to
this segment.
Strong
strategies
should be
developed in
order to
maintain
relationship
between the
retailers and
Customers of
this segment.
C2 Golden
Segment
This is an
average value
segment that
has low
moderate size.
Marketing
programs
should be
developed in
order to
increase basket
size of this
segment of
customers.
C6 Silver
Segment
This segment
has low
recency,
highest
frequency,
and lower
monetary
value.
Although they
have a long
time
relationship
with the firm,
they exhibited
very bad
performance.
In addition, the
recency of this
segment is very
low; this may
be a sign of
attrition. Strong
anti-attrition
programs
should be
developed for
this segment of
customers.
C4 New Low
Value
Customer
This segment
has lowest
recency; lower
frequency this
means that
they hardly
maintain their
relationship
with the
retailers. They
have low
frequency and
low monetary
value.
Because the
number of
customers
belonging to
this segment is
relatively high,
marketing
programs for
this segment
should
encourage
customers to
buy more
products.
VI. CONCLUSION AND SCOPE FOR FUTURE
WORK
Data mining has its importance in the field of business in
connection with finding patterns, forecasting, discovery of
knowledge etc. It has wide application domain almost in every
industry, where the data is generated from a data-base for the
utilization of most of its important activities. In the context of
marketing management, it also has broad applicability and
retailing industry is not an exception. However, as compared to
the B2B approach, not many studies have yet been done in the
B2C framework. At the same time, the evolution of modern
retailing in the marketing environment of an emerging
6. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.28-34
ISSN: 2278-2419
33
economy like India requires in-depth analysis of customer
data-base to generate useful information for the retailers.
Looking at this important aspect, the present study is designed
to analyze customer data-base using a multi-criteria decision
making technique based data mining methodology. Thus, in
this study of B2C marketing, we have segmented customers of
a retail store based on an AHP based data mining a
methodology using the concept of value-based segmentation.
The concept of customer life- time value has been used to
determine the value of each segment, so that, appropriate
marketing strategies can be undertaken to improve retailer-
customer relationship. We addressed this problem for a
manufacturer-retailer-customer chain, focusing on customers,
as customers are the end users of the products. The proposed
methodology has been implemented using the data of a retail
firm, which has got its national presence in India. The results
of this study identified and ranked six groups of customers,
according to their CLV values. We also presented an analysis
of the customers with respect to a customer-value matrix.
Finally, we provided some possible strategies that can be
considered in order to improve the relationship between the
retailer and its customers. However, this study has a lot of
scope for its improvement. For example, fuzzy AHP can be
used for generating weights of the CLV criteria instead of
AHP, where the judgment matrix represents the aggregation of
multiple judgments in terms of triangular membership
functions. Similarly, after identification of the clusters, CLV
values of the clusters can also be determined by the application
of TOPSIS (Technique for Order Preference by Similarity to
Ideal Soltions).The authors express their thanks to the
anonymous Indian Retail Chain for providing all data which
was required for the study. In addition, we would like to thank
the reviewers for their valuable comments and suggestions for
preparation of the revised manuscript.
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