of 7

Behavioral animation, crowd, context, character attributes

Published on June 2016 | Categories: Types, Research, Internet & Technology | Downloads: 31 | Comments: 0
258 views

Journal of Computing, eISSN 2151-9617, http://www.JournalofComputing.org

Comments

Content

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 11, NOVEMBER 2011, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING WWW.JOURNALOFCOMPUTING.ORG

34

A Novel Study on Mining Customer Preferences
K. Vijayalakshmi, Dr. R. Dhanapal, B.Balaji Selva Ganesh
Abstract— The paper highlights the Critical factors for the Customer preferences in the business markets using the Data mining. The customer purchase patterns approach, using the association rules mining technique, is an effective way of extracting the rules from the raw data and inferring the buying patterns among them. The success of proper implementation of these techniques in the business firms is mixed. This is due to the fact that trends and taste of the customers are highly unpredictable. Hence this implementation requires planning regarding the factors which need to be considered before going for the new innovative ideas. These factors may vary from firm to firm but the general factor for effective implementation of the customer preference is essential. This factor termed as Critical factors of Customer preferences (CFCP) decides the failure or success of the implementation. Marketing efforts usually focus on minimizing churn because the cost of bringing a customer back is usually much greater than the cost of retaining the customer in the first place. The paper highlights those key essential factors which need to be considered before automating the process of searching the mountain of customer’s related data using Data mining to find patterns or a model that helps the business people to predict the behaviors of the customer to achieve their long term goals, vision & mission. Index Terms— Customer preference, Data mining, Churn, Association rule mining, Patterns.

——————————  ——————————

1 INTRODUCTION

M

arketing research has long ago applied data mining and machine learning techniques in retail transactions in which large amounts of purchase data have been analyzed [12]. Market basket analysis discovers association patterns in retail transactions and lays the foundations for applications such as product bundling, cross category dependency identification as well as consumer profiling [10]. Typically, a system compares a user profile to some reference characteristics, and seeks to predict the 'rating' that a consumer would give to an item they had not yet considered. These characteristics may be derived from the item itself or the user's social environment [1]. An applying this system’ technique to market basket data faces several challenges.  Provides limited insight in the underlying structure of the user preferences.  Technical issues relating to the most common recommendation techniques.  Association rules tend to ignore large itemsets, and memory-based collaborative learning lacks scalability [12].  Content based recommenders are inappropriate since information about retail products is neither readily available nor appropriately detailed.
————————————————

Latent topic models provide a model that effectively recommends products to consumers based on their preferences. ([4] [9],[10]). Also the Database marketers must identify the market segments containing customers with higher potential and build and execute campaigns that possibly impact the individual’s behavior. But in great struggle, the marketers have to exercise the massive data through the details to find the piece of valuable information. Data Mining uses well- established statistical and machine learning techniques to build models to identify the various customer behaviors. The technology enhances the procedure by automating the mining process, integrating it with commercial data warehouses, and presenting it in a relevant way for business users. Due to the behavior characteristics, the customer maintenance, online conversations, sales & purchase rates, direct marketing response and fund raising profit can be increased.

2

NEED FOR CUSTOMER BEHAVIOR AND DATA MINING

 Ms. K. Vijayalakshmi is with the Department of M.C.A, Reva Institute of Technology & Management, Bangalore. Research Scholar, Mother Terasa University, Kodaikanal.  Dr. Dhanapal is with the Department of Computer Applications, Easwari Engineering College, Affiliated to Anna University, Chennai.  B.Balaji selva Ganesh, final year MCA student of research, Dept. of Computer Applications, Easwari Engineering College, Chennai. His Area of interest is computer Graphics.

The emergence of the Business-to-Customer (B2C) markets has resulted in various studies on developing and improving customer retention and profit. The abundance of customer information enables marketers to take advantage of individual-level purchase models for direct marketing and targeting decisions. But in practical, it becomes cumbersome to draw meaningful conclusions from such huge raw data. The consumer’s are the ultimate users and hence the business analyst needs not to mine in depth about their views of purchase. Perhaps, the customer’s are may or may not be a consumer but in turn purchase products. In this the business analyst has to do

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 11, NOVEMBER 2011, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING WWW.JOURNALOFCOMPUTING.ORG

35

the critical job of mining their purchase preferences. It means they need to mine the purchase attributes of a customer regarding their interest towards the type, model, accessibility, cost, usage, quality, trend of any product. The major customer characteristics that are used to measure the purchase behavior to provide information on what customers do like 1. Purchase gap. 2. Frequency in purchase of certain items. 3. Total expenditure for the purchase. It helps to segregate the customers into groups having different characteristics based on those values. Data mining helps marketing professionals improve their understanding of customer behavior. The key is to find patterns relevant to the current business problems and the goal is to identify a customer, understand and predict the customer-buying pattern, identify an appropriate offer, and deliver it in a personalized format directly to the customer. Association is the primary technique to analyze the purchase patterns of a customer; in turn it works through the other two techniques clustering & classification. Associating the customer purchase behavior, the business analyst can easily classify them into different groups (clusters) based on their preferences. Hence marketing certain products can be made obvious only for the respective groups whose confidence & support to that product is more. Due to that, time spent on the whole group of customers will get minimized & effectively define business strategies to enhance & promote the business activities to the higher level.

Univariate model/simple scheme has only one behavioral variable determines buying behaviour. Multivariate model/reduced form scheme has numerous independent variables were assumed to determine buyer behavior. System of equations model/structural scheme or process scheme has numerous functional relations (either univariate or multi-variate) interact in a complex system of equations. The third model is capable of expressing the complexity of buyer decision processes. The key factors involved in analyzing the buyer decision models are Brand attributes, Environmental factors, Consumer's attributes, Organization’s attributes, Message attributes , Consumer decoding , Search and Evaluation . Brand attributes: 1. An idea: captures customers’ attention and loyalty by filling an unmet or unsatisfied need. 2. Uniqueness: differentiate from the other organization. 3. Attractiveness: brand appeal to people. 4. Honesty: Customers want to believe the promises made and sure the brand promises are achievable. 5. Consistency: key attribute of a great brand that maintains the values in the brand. 6. Long term thinking: makes easier for a brand to explore worldwide, go beyond cultural barriers, connect to multiple consumer segments, create economies of scale, and operate at the higher end of the positioning spectrum. 7. Relevancy: performs the way people want it to. Environmental factors: The Factors that influences the environment are technology, Government, culture, people & economics. The higher the rating, the more positive is the factor for your business. The description of the rating is based on the competitive alternatives, government regulations, fashion trends, changes in income levels, and changes in average age. Customer Attributes: The attributes rely on the level of involvement and the personal, social, and economic significance of the purchase. Three characteristics of highinvolvement purchase are: expensive, serious personal consequences and could reflect on one’s social image. Organization attributes: The attributes that clearly defines the operational & strategic nature of the business organizations. They are  Have a clear vision of the organization.  Set short or long term goals.  Recruit a great team of human resource.  Divide tasks equitably & exhibit each with greater efforts.  Keep control on the people that are accountable.  Stay the course of work into action & have the dreams or the vision/mission come true. 

2.1 Decision Process
It is the decision making process undertaken by customers in regard to a potential market transaction before, during, and after the purchase of a product or service. More generally, decision making is the cognitive process of selecting a course of action from among multiple alternatives. Decision making is a psychological construct. It is a construction that imputes commitment to action. In general there are three ways of analyzing customer buying decisions. They are: Economic Considerations: are largely quantitative and are based on the assumptions of rationality and near perfect knowledge. The customer is seen to maximize their utility. Psychological Considerations: are concentrate on psychological and cognitive processes such as motivation and need recognition. They are qualitative rather than quantitative and build on sociological factors like cultural influences and family influences. Customer Behavior considerations: are very practical towards the loyalty & commitment of the product services. They typically blend both economic and psychological models.

2.2 Key Factors of Customer’s Purchase Behaviour
Frank Nicosia identified three types of buyer decision making models. They are

© 2011 Journal of Computing Press, NY, USA, ISSN 2151-9617 http://sites.google.com/site/journalofcomputing/

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 11, NOVEMBER 2011, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING WWW.JOURNALOFCOMPUTING.ORG

36

Message attributes: The message attributes highly rely on the communication in which the information from whom, says what, in which channel, to whom, with what effect. Mostly the information about the product & its services reaches the customer at the right time through the tools like: 1. Advertising: Any paid form of non-personal presentation and promotion of ideas, goods or services by an identified sponsor. 2. Sales Promotion: Short-term incentives to encourage purchase or sale of a product or service. 3. Publicity: demand for a product, service or business unit by planting commercially significant news about it in a published medium or upon radio, television or stage. 4. Personal Selling: Oral presentation in a conversation with one or more prospective purchasers or the purpose of making sales. Customer decoding: The customer’s attitudes and expectations change towards the companies, linkage between the customer experience and their buying process becomes more crucial. Advertisers fine-tune innovative ways to engage customers with greater efficiency bringing tighter integration between search marketing, social media and marketing. First, search marketing is efficient, measurable, and it captures a customers’ expression of intent. Most marketers are familiar with the four key stages of the buying process that improve the customer experience and influence the final purchase decision. Awareness: actual “need or want” for a product or service is recognized. The objective of the awareness stage is to build general and favorable awareness of a company, product or service in the marketplace. Information Search: Customers need some form of information search to help them through their purchase decision. Sources of information could be family, friends and neighbors who already have the product. Alternatively they may search the internet, read print publications or talk to sales people directly. Evaluation: evaluate the products through attribute factors that brand to purchase. This means that customers form individual opinions on what features, functions, locations, and pricing will provide the most value. Purchase and After-Sale Service: Through the evaluation process discussed above customers will reach their final purchase decision and they reach the final process of going through the purchase action e.g. the process of going to the shop to buy the product or engage the service. Purchase of the product can either be through the store, the web, or over the phone. Post purchase behavior and research shows that after-sale engagement is critical. Search and Evaluation: A customer can search information from several sources: 1. Personal sources: family, friends, neighbours 2. Commercial sources: advertising, salespeople, retail-

ers, dealers, packaging, point-of-sale displays 3. Public sources: newspapers, radio, television, consumer organizations; specialist magazines 4. Experimental sources: handling, examining, using the product The usefulness and influence of these sources of information will vary by product and by customer. The challenge for the marketing team is to identify which information sources are most influential in their target markets. In the evaluation stage, the customer must choose between the alternative brands, products and services. An important determinant of the extent of evaluation is whether the customer feels “involved” in the product. By involvement, the degree of perceived relevance and personal importance accompanies the choice. High-involvement purchases include those involving high expenditure or personal risk. For example buying a house, a car or making investments. Low involvement purchases (e.g. buying a soft drink, choosing some breakfast cereals in the supermarket) have very simple evaluation processes.   Need recognition & problem awareness         Information search       Evaluation of alternatives       Past purchase analysis       Model that compares the past purchase with   the customer expectations       Score       Move to other equal Purchase / Rebuy the   brand product     Purchase / not rebuy     Fig 2.2.1  From the figure 2.2.1, the model calculates the score between the customer expectation level & the level of satisfaction provided by the product. The satisfaction level is obtained from the past purchase details. If the score is more than the expected level, then the customer surely purchase / rebuy or still recommend the product to others. If the score is equal to the expected level, then the customer may purchase but not recommend the product

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 11, NOVEMBER 2011, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING WWW.JOURNALOFCOMPUTING.ORG

37

to others. If the score is less than the expected level, then the customer surely move to the other brand. The marketing team needs to provide customers in different buying situations. In high-involvement decisions, the marketer needs to provide a good deal of information about the positive consequences of buying. The postpurchase evaluation is common for customers to experience concerns after making a purchase decision. The customer, having bought a product, may feel that an alternative would have been preferable. To manage the post-purchase stage, it is the job of the marketing team to persuade the potential customer that the product will satisfy his or her needs. Then after having made a purchase, the customer should be encouraged that he or she has made the right decision.

and an understanding of the customer is achieved.

4

THE CRITICAL PREFERENCES

FACTORS

OF

CUSTOMER

3

RELEVANCE OF DATA CUSTOMER PREFERENCE

MINING

TOWARDS

The purchase behavior highly relies on two factors, need and want. If there is a need, then the customer always goes for immediate purchase, or else if there is a want, then they go for specific requirement. The Psychological factors motivation, emotions, moods, perception, learning, values carried by them, beliefs, attitude and life style influences more on Customer purchase Behavior and makes them to purchase or show their response towards brand/product. If (product performance > expectation level) then customers are delighted. else if (product performance = = expectation level) then customers are satisfied. else customers are not satisfied. A. Motivation and Personality: From lowest to highest, the hierarchy is 1. Physiological needs: ba- 2. Safety needs: selfsic to survival. preservation, physical wellbeing. 3. Social needs: love, 4. Self-actualization needs: friendship, achievement, personal fulfillment. status, prestige, self- respect. B. Perception: the process of using the senses to acquire information about the surrounding environment or situation 1. Selective Perception: In the human brains attempt to organize and interpret information. 2. Perceived Risk: Anxieties felt by the Customers who cannot anticipate the outcomes of a purchase. Also believe that there may be negative consequences. Marketers try to reduce perceived risk and encourage purchases by strategies such as providing ambience of retail outlet, service provided by the sellers, providing discounts, offers, gifts, warranties and guarantees. C. Learning: Repeated experience, Thinking. 1. Behavioral Learning: Customers learn from repeated experience through the following variables ; a. Drive: need moves an individual to action. b. Cue: stimulus/symbol perceived by consumers. c. Response: action taken by a customer to satisfy the drive. d. Reinforcement: The reward. 2. Cognitive learning: Involves making decision between two or more ideas and observe the outcomes of others’ behaviors 3. Brand loyalty: consistent purchase of a single brand over time & Brand loyalty differs across geographic areas. D. Values, Beliefs, and Attitudes 1. Attitude Formation: an opinion or general feeling about something. Attitude: respond consistently favorable or unfavorable way which are shaped by our values and beliefs,

How to learn more about customers and their inclination towards particular products, use that information to make appropriate choices to customers, and understand which marketing strategies can succeed in long term customer satisfaction and retention. Managers can understand their customer by evaluating customer behavior, customer segregation, customer profiles, loyalty and profitability. Data Mining helps managers to identify valuable patterns contained in raw data and their relations so as to help the major decisions. The basic evaluation is shown in fig. 3.1. The model can have two initiating pts. Firstly, the action by customer, in which he does some purchase and then the data, is measured and evaluated. Secondly, the action by company, mines the evaluated data and then they can have an understanding of the patterns that the customer shows while purchasing. With the help of that data, the organization can formulate its steps to maximize or optimize its business plans. The organization takes some action for improving the customer’s satisfaction by making a good informative offer, and then studies the actions taken by the customer. Starting Point

Customer Action

Organisation Action

Measurement Evaluation

Customer understanding

Fig 3.1 Then the actions of the customer are again evaluated

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 11, NOVEMBER 2011, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING WWW.JOURNALOFCOMPUTING.ORG

38

which are learned. The customer may have an attitude of either of the listed below or all. i. Overall satisfaction ii. Product-level satisfaction iii. Importance vs. satisfaction iv. Timeliness of delivery v. Customer service process satisfaction vi. Returns and exchange process satisfaction vii. Interest in new potential products and services Values: personally or socially preferable modes of conduct or states of existence that are enduring. Beliefs: consumer's subjective perception of how well a product or brand performs on different attributes. 2. Attitude Change: Changing beliefs about the extent to which a brand has certain attributes. Changing the perceived importance of attributes and adding new attributes to the product. E. Lifestyle: Lifestyle is a mode of living that is identified by activities a person spends time and resources interests. A person considers important in the environment opinions and thinks of self and the world. The continuous relationship between the customer and the firm is long-standing only if the degree of achieving the following are at the better level. Loyalty is the firm’s intention to continue the relationship with a customer. Affective Commitment is the firm’s affective attachment to continue a relationship. Continuous Commitment perceived cost associated with quitting relationship. Valueadded Service is of convenience of value-added services. Service Quality is the firm’s perception to which core service fulfill its requirements, desires, goals, and so forth. Investment which is intrinsic and extrinsic resources attached to the relationship that would disappear when relationship is ended. Attractiveness of Alternatives is the attractiveness which a firm senses in other viable competing business organization. To retain their customers always in the competitive market, the business firms have to focus on the following success key factors. They are the right organizational culture, an innovation strategy, resource commitment, top management support, portfolio management, and a multistage, disciplined, new-product development process. Any business organization has to maintain the following factors for the customer satisfaction. They are outlined below: 1. Timeliness: Customers need all their queries to be answered on time and their problem resolved in a timely manner. 2. Attitude: When customers are treated with respect, courtesy and professionalism they are most receptive to having a satisfactory outcome. 3. Empathy: usually calm down the situation when they handled with empathy. 4. Ownership: Take responsibility for the situation. 5. Active Listening: Listen first, act second. 6. Expertise: Be knowledgeable about your product or service.

7. Dependability: Make a commitment to respond, and then respond. The Critical factors for the Customer preferences of any firm using data mining may vary from firm to firm. The general considerations for the effective implementation may be common in view to the following: it should be customer centric who is the core element. Top management should be committed toward the successful implementation of the project and should be aware about the pros and cons of the implementation. The implementation requires the skilful trained staff to work on the software. As the implementation process may take time, it is required that the schedule and plan of the whole work is made in advance. The implementation requires the feedback of the system. The communication of the revealed pattern should be shared to the various departments. Privacy and security issue is very important as the data mining reveals some secret and personal information also and should be taken care in advance. Particularly through data mining the extraction of hidden predictive information from large databases organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by historyoriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Firms today are concerned with increasing customer value through analysis of the customer lifecycle. In the traditional process, the marketing goal is to reach more customers and expand the customer base. But given the high cost of acquiring new customers, it makes better sense to conduct business with current customers. In so doing, the marketing focus shifts away from the breadth of customer base to the depth of each customer’s needs. Businesses do not just deal with customers in order to make transactions; they turn the opportunity to sell products into a service experience and endeavor to establish a long-term relationship with each customer. As on-line information becomes more accessible and abundant, consumers become more informed and sophisticated. They are aware of all that is being offered, and they demand the best. To cope with this condition, businesses have to distinguish their products or services in a way that avoids the undesired result of becoming mere commodities. One effective way to distinguish themselves is with systems that can interact precisely and consistently with customers. By collecting the customer demographics and behavior data, makes precision targeting possible. Customized catalogues, personalized business portals, and targeted product offers can simplify the procurement process and improve efficiencies for both companies. E-mail alerts and new product information tailored to different roles in the buyer company can help increase the effectiveness of the sales pitch. Trust and au-

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 11, NOVEMBER 2011, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING WWW.JOURNALOFCOMPUTING.ORG

39

thority are enhanced if targeted academic reports or industry news are delivered to the relevant individuals.  

5 CONCLUSIONS AND FUTURE WORK
With the purchase patterns of customer purchase behaviour reports, companies may use it to advertise their products with greater efficiency. Data mining represents the link from the data stored over many years through various interactions with customers in diverse situations, and the knowledge necessary to be successful in relationship marketing concepts. Businesses that use customer data and personal information resources effectively will have an advantage in becoming successful. The primary focus is on analyzing customer information for economic benefits. We have presented an overview of some of the notable factors that explore the behaviors of the customer. The paper attempts to present the parameter which firms should consider before implementing the Data mining techniques. As the process of implementation require the technical and non-technical points, firms need to make a review of the past purchase details to check the implementation feasibility. By continuing to improve customer prediction techniques it will become a necessity rather than a convenient commodity for businesses to use customer analytics. With this valuable information there is an opportunity to fine-tune business operations and manager decisions. Rapid decision making will increase in speed and effectiveness in the future as tools and information become more easily accessible. The future work reveals the implementation of the data mining techniques like association analysis, clustering & classification with the information driven by these critical factors.

ACKNOWLEDGMENT
We take this opportunity to thank all the people who involved for completing this work more comfortably and successfully. Also we extend our thanks to our management for the extended support and motivation they have given ever for the academic promotional activities.  

REFERENCES
[1] Riccardo Bellazzi , Blaz Zupan , “Predictive data mining in clinical medicine: Current issues and guidelines”, International Journal of Medical Informatics 77 (2008) 81-97. [2] Patricia E.N. Lutu , Andries P. Engelbrecht , “A decision rulebased method for feature selection in predictive data mining”,Expert Systems with Applications 37 (2010) 602-609. [3] Stockwell I, (2008), Introduction to Correlation and Regression analysis, SAS Global Forum, Paper-364, pp. 1-8. [4] Tzung-Pei Hong , Chyan-Yuan Horng , Chih-Hung Wu , ShyueLiang Wang, “An improved data mining approach using predictive itemsets”, Expert Systems with Applications 36 (2009) 72-80.

[5] John R. Davies, Stephen V. Coggeshall, Roger D. Jones, and Daniel Schutzer, "Intelligent Security Systems," in Freedman, Roy S., Flein, Robert A., and Lederman, Jess, Editors (1995). Artificial Intelligence in the Capital Markets. Chicago: Irwin. ISBN 1-55738-811-3. [6] Higgins J, (2005), The Radical Statistician, Prentice Hall Publishing. [7] An Introduction to Regression Analysis, Alan O. Sykes, Chicago Working Paper in Law & Economics, http: // www.law.uchicago.edu/ files /file / 20. Sykes_Regression.pdf [8] Stepwise Multiple Linear Regression Analysis http: // marketing.byu.edu / htmlpages / books / pcmds / REGRESS.html [9] Agresti, Alan. (2002). Categorical Data Analysis. New York: Wiley-Interscience. ISBN 0-471-36093-7. [10] Hilbe, Joseph M. (2009). Logistic Regression Models. Chapman & Hall/CRC Press. ISBN 978-1-4200-7575-5. [11] Kannan K. Senthamarai, Sekar P. Sailapathi, Sathik M. Mohamed, (2010), Financial Stock Market Forecast using Data Mining Techniques, Proceedings of the International MultiConference Engineers and Computer Scientists, ISBN: 978-988-17012-8-2, Hong Kong. [12] Weiss, S., Indurkhya, N., 1998. Predictive Data Mining: A Practical Guide. Morgan Kaufmann, Los Altos, CA. [13] Abdullah Al-Mudimigh, Farrukh Saleem, Zahid Ullah Department of Information System: Efficient implementation of data mining: improve customer's behavior, 2009 IEEE ,(2009),pp.7-10. [14] Sung Ho Ha , Sang Chan Park, Sung Min Bae : Customer’s timevariant purchase behavior and corresponding marketing strategies: an online retailer’s case, Computers & Industrial Engineering 43 (2002) 801–820, (2002),pp.801-806. [15] Euiho Suh, Seungjae Lim, Hyunseok Hwang, Suyeon Kim : A prediction model for the purchase probability of anonymous customers to support real time web marketing: a case study, Expert Systems with Applications 27 ,(2004), pp. 245-250. [16] Mu-Chen Chen , Hsu-Hwa Chang, Ai-Lun Chiu : Mining changes in customer behavior in retail marketing, Expert Systems with Applications 28 ,(2005), pp. 773-776. [17] Sriram Thirumalai, Kingshuk K. Sinha : Customer satisfaction with order fulfillment in retail supply chains: implications of product type in electronic B2C transactions, Journal of Operations Management 23 ,(2005), pp. 291-296. [18] Ian H. Witten & Eibe Frank : Data Mining : Practical machime learning tools and techniques, San Francisco, Morgan Kaufmann publishers, (2005), pp. 112-118,136-139. [19] Edelstein H. Data mining: exploiting the hidden trends in your data. DB2 Online Magazine. http://www.db2mag.com/9701edel.htm [20] IDC & Cap Gemini. Four elements of customer relationship management. Cap Gemini White Paper. [21] Freeman M. The 2 customer lifecycles. Intelligent Enterprise 1999;2(16):9. [22] Thearling K. Data mining and CRM: zeroing in on your best customers. DM Direct. December, 1999. http://www.dmreview.com/editorial/dmreview/print— action.cfm?EdID1744 [23] Thearling K, Exchange Applications, Inc. Increasing customer value by integrating data mining and campaign management software. Exchange Applications White Paper, 1998. http://www.crmforum. com/crm—forum—white— papers/icv/sld01.htm [24] Thearling K, Exchange Applications, Inc. Increasing customer value by integrating data mining and campaign management soft-

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 11, NOVEMBER 2011, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING WWW.JOURNALOFCOMPUTING.ORG

40

ware. Exchange Applications White Paper, 1998. http://www.crmforum. com/crm—forum—white— papers/icv/sld02.htm [25] Book J. Customer relationship management: what is it that separates CRM contenders from CRM pretenders? DMReview.Com September, 1999. http://www.dmreview.com/editorial/dmreview/print— action.cfm?EdId1368 [26] V.Srikanth, Dr.R.Dhanapal, “A Business Review of E-Retailing in India”. International Journal of Business Research and Management (IJBRM), Vol. 1, Issue 3, pp. 105 -121, 2011. [26] Dr.R.Dhanapal, Gayathri Subramanian. “E-Business Operating Trust and Security”. IFRSA’s International Journal of Computing Vol. 1, Issue 1, pp. 59 - 66, 2011. [27] Dr.R.Dhanapal, Gayathri Subramanian, Jobin M Scaria. “Customer Retention Using Data Mining Techniques”. International Journal of Computer Applications, Vol. 11, No.5, pp. 32 - 34, 2010. Mrs. K. Vijayalakshmi obtained her M.C.A from Bharathidasan University & M.Phil from Manonmaniam Sundaranar University, Tamil Nadu, India. She is currently Associate Professor in the Department of M.C.A, Reva Institute of Technology & Mangement, Affiliated to V.T.U, Bangalore, Karnataka, India. She has 11 years of teaching experience & presented papers in national conferences. Prof.Dr.R.Dhanapal obtained his Ph.D in Computer Science from Bharathidasan University, Tamil Nadu, India. He is currently Professor, Research Department of Computer Applications, Easwari Engineering College, Affiliated to Anna University Chennai, Tamil Nadu, India. He has 25 years of teaching, research and administrative experience which includes 21 years of Government Service. Besides being Professor, he is also a prolific writer, having authored twenty one books on various topics in Computer Science. He has served as Chairman of Board of Studies in Computer Science of Bharathidasan University, member of Board of Studies in Computer Science of several universities and autonomous colleges. Member of standing committee of Artificial Intelligence and Expert Systems of IASTED, Canada and Senior Member of International Association of Computer Science and Information Technology (IACSIT), Singapore and member of International Association of Engineers, Hongkong. He has Visited USA, Japan, Malaysia, and Singapore for presenting papers in the International conferences and to demonstrate the software developed by him. He is the recipient of the prestigious ‘Lifetime Achievement’ and ‘Excellence’ Awards instituted by Government of India. He served as Principal Investigator for UGC and AICTE, New Delhi funded innovative, major and minor research projects worth of 1.7 crore especially in the area of Intelligent systems, Data Mining and Soft Computing. He is the recognized supervisor for research programmes in Computer Science leading to Ph.D and MS by research in several universities including Anna University Chennai, Bharathiar University Coimbatore, Manonmaniam Sundaranar University Tirunelveli, Mother Teresa University Kodaikanal and many Deemed Universities. He has got 56 papers on his credit in international and national journals. He has been serving as Editor In Chief for the International Journal of Research and Reviews in Artificial Intelligence (IJRRAI) United Kingdom and serving as reviewer and member of editorial in accredited peer reviewed national and international journals including Elsevier Journals. B.Balaji selva Ganesh, final year MCA student of research, Dept. of Computer Applications, Easwari Engineering College, Chennai. His Area of interest is Computer Graphics.

Sponsor Documents


Recommended

No recommend documents

Or use your account on DocShare.tips

Hide

Forgot your password?

Or register your new account on DocShare.tips

Hide

Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in

Close