International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Web Mine Customer Relationship Management Krusha Belerao,Jayant Belekar, Tejas Deshmukh, Aniket Shinde, Abhishek Dhamane Prof. Krushna Belerao , Pune Mr. Aniket Shinde ,Pune Professor, Computer Department, KJ’s Trinity College Of Engineering Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------misunderstanding and human errors. High dimensional Abstract - The Internet has become popular because of its low cost, low latency and high bandwidth. Its collaborating data may contain useless data in large amount which nature delivers an association the capability to arrive into a might affect the performance of learning algorithms. close, modified discussion with separate customers. The Thus, feature selection becomes very important for concurrent development of data management technologies machine learning tasks. Heterogeneous data is like data warehousing, and data mining, have formed the ideal environment for creating CRM a much more collected from any number of sources, mainly Standardized effort than it has been in the past. we defined unknown and unlimited, and in many different formats how data analytics can be used to type various CRM methods either numeric or nominal. A new feature selection like customer segmentation, communication targeting, retention, and loyalty much more effective. We briefly define technique is proposed to resolve above issues the important technologies required to implement analytical mentioned in the CRM data set with relevant features Customer Relationship Mangement, and the organizational by incorporating an efficient data mining techniques to problems that must be judiciously fingered to make CRM a improve data quality and feature relevancy after prereality. Our goal is to reveal problems that exist with present customer relationship management, and how using data processing. The projected technique is tested on KDD analytics techniques can address them. Our hope is to get the Cup 2009 data set of Small Challenge. The projected data mining community interested in this important methodology proves its higher performance. application domain. 2. INTRODUCTION TO CRM
Key Words: Customer Relationship Management (CRM),
CRM(Customer Relationship Management) emerge from business processes such as relationship marketing and the increased importance on improved customer retention through the effective CRM. 4
CRM Implementation, Web Crawler , Dom Tree , Customer Communication.
1.INTRODUCTION CRM(customer relationship management) has turn into one of midpoint point for several businesses such as Retail, Telecommunication, Insurance and Banking. CRM takes client as the central point and optimizes the business process. But in the real-world application there are major challenges for building high performance CRM classification models. Meanwhile data quality is an important matter for CRM classifications in that several kinds of data anomaly complicate the data preparation and classification function. It is problematic to find one method that fixes all data mining difficulties in the CRM data set such as High dimensional, Heterogeneous, Simple data anomaly and Imbalanced. Normally the data set is not having all the data because of erroneous data by reluctant clients who do not provide all information, Š 2017, IRJET
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Impact Factor value: 5.181
One sight of CRM is the utilization of customer-related data to deliver proper services to customers. 3 Additional view of CRM is technologically orientated. Database technologies such as Mining of Data(Data Mining) and Data Warehousing are critical to the functionality and effectiveness of CRM systems. 1 A study led in a UK-based manufacturing company demonstrates that in real World Customer Relationship Management is a complicated combination of technological factors and Business. CRM is considered a complete procedure of obtaining, retaining and growing customers.
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