The document discusses data mining applications and benefits in e-commerce. It describes common data mining applications like financial data analysis, retail industry analysis, telecommunications analysis, and intrusion detection. It then outlines benefits of data mining in e-commerce such as customer profiling, personalization of service, basket analysis, sales forecasting, and market segmentation.

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2. Analysis on Data Mining Applications and Benefits in E-Commerce
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Contents:
Introduction to data mining
Smart Service Model in E-Commerce
Data Mining Applications in E-Commerce
Benefits of Data Mining in E-Commerce
Conclusion

3. Introduction to data mining
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Contents:
Finds valuable information hidden in large volumes of data
Analysis of data and the use of software techniques for finding patterns and regularities in
sets of data
Finding the patterns by identifying the underlying rules and features in the data

4. Smart Service Model in E-Commerce
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Applications of Smart Service Model in E-Commerce
User group mining
User interest mining
Industry and domain
Knowledge mining
Business association mining
Shopping goods

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1.Financial Data Analysis
2.Retail Industry
3.Telecommunication Industry
4.Biological Data Analysis
5.Intrusion Detection
6.Financial Banking
Data Mining Applications in E-commerce

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 Reliable and of high quality which facilitates systematic data analysis and data
mining
 Loan payment prediction
 Customer credit policy analysis
 Classification and clustering of customers for targeted marketing
 Detection of money laundering
 Financial crimes
Financial Data Analysis

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Customer purchasing history
Goods transportation
Consumption and services
The knowledge discovery in retail industry helps in identifying customer buying
patterns and trends
Improved quality of customer service and good customer retention and satisfaction
Retail Industry

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Fax
Pager
Cellular phone
Internet messenger
Images
E-mail
Web data transmission
This system identifying the telecommunication pattern
Catch fraudulent activities
Improve quality of service
Telecommunication Industry

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Multidimensional Analysis of Telecommunication data.
Fraudulent pattern analysis
Identification of unusual patterns
Multidimensional association and sequential patterns analysis
Mobile Telecommunication services
Telecommunication data analysis is done using visualization tools
Telecommunication Industry

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Important part of bioinformatics
Discovery of structural patterns
Analysis of genetic networks
Protein pathways
Semantic integration of heterogeneous
Distributed genomic, proteomic databases
Association and path analysis
Visualization tools in genetic data analysis
Biological Data Analysis

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Detects action that threatens integrity, confidentiality, or the availability of network
resources
The intruding and attacking network prompted intrusion detection
Finds critical component of network administration
Intrusion Detection

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Areas of Intrusion Detection
The algorithms are developed for intrusion detection in data mining
The various analyses such as association and correlation, aggregation helps to select and
build discriminating attributes
Analysis of Stream data
Distributed data mining
Visualization and query tools
Intrusion Detection

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Areas of Intrusion Detection
The algorithms are developed for intrusion detection in data mining
The various analyses such as association and correlation, aggregation helps to select and
build discriminating attributes
Analysis of Stream data
Distributed data mining
Visualization and query tools
Intrusion Detection

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Finding patterns, causalities, and correlations in business information and market prices
The huge amount of data is supposed to be generated with new transactions
This information are useful for better segmenting, targeting, acquiring, retaining and
maintaining a profitable customer
Financial Banking

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The data mining applications in e-commerce area
Refers to possible areas in the field of E-commerce
The online store for shopping
These facts represent unstructured or structured data
Benefits of Data Mining in E-Commerce

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Customer Profiling
Identified as customer-oriented strategy in e-commerce
Use business intelligence through the mining of customer’s data
Plan their business activities and operations
Develop new research on products or services for prosperous e-commerce
Categorizing the customers of great purchasing potentially from the visiting data
The most of the companies can use users’ browsing data to identify browsing or buying
something
The company plans and improves their infrastructure
Benefits of Data Mining in E-Commerce

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Personalization of Service
Act to provide contents and services geared to individuals on the basis of information
Collaborative filtering
Explored intensively in the data mining community
It can be divided into three groups: Content-based, social data mining and collaborative
filtering
These systems are cultured and learned from explicit or implicit feedback of users
Daily Activities Social data mining
The personalization can be achieved by the aid of collaborative filtering
Benefits of Data Mining in E-Commerce

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Basket Analysis
The market basket analysis (MBA) is a common retail, analytic and business intelligence tool
 Helps retailers to know their customers better
The various ways to get the best out of market basket analysis
•The product affinities are identified
•Advanced market basket analytics for planning more effective marketing efforts
•Cross-sell and up-sell campaigns; these shows the products purchased together
•The planograms and product combos are used for better inventory control
•To get a glimpse of who your shoppers really are
Benefits of Data Mining in E-Commerce

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Sales Forecasting
This system involves the aspect of the time an individual customer spend to buy an item
Predict if the customer will buy again
Determine a strategy of planned obsolescence
Figure out complimentary products to sell
The cash flow can be projected into three which include the pessimistic, optimistic and the
realistic
Benefits of Data Mining in E-Commerce

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Merchandise Planning
This system is useful for both online and offline retail companies
Determine stocking options and the inventory warehousing
Market Segmentation
This system is one of the best uses of data mining
It can be broken down into different and meaningful segments like income, age, gender,
occupation of customers
This can be used by the companies are running email marketing campaigns or SEO
strategies
 It also helps a company identify its own competitors
The database segmentation of a retail company will improve the conversion rates
This also helps the retail company to understand the competitors
Satisfy the target audience in a generic way
Benefits of Data Mining in E-Commerce