Applying Data Mining Procedures On A Customer Relationship Management System

Over the next two and a half years, we worked to develop and refine CRISP-DM. We ran trials in live, large-scale data mining projects at Mercedes-Benz and at our insurance sector partner, OHRA. We worked on the integration of CRISP-DM with commercial data mining tools. The SIG proved invaluable, growing to over 200 members and holding workshops in London, New York, and Brussels. By the end of the EC-funded part of the project—mid-1999—we had produced what we considered a good-quality draft of the process model. Those familiar with that draft will find that a year later, although now much more complete and better presented, CRISP-DM 1.0 is by no means radically different. We were acutely aware that, during the project, the process model was still very much a work-in-progress; CRISP-DM had only been validated on a narrow set of projects. Over the past year, DaimlerChrysler had the opportunity to apply CRISP-DM to a wider range of applications. SPSS’ and NCR’s Professional Services groups have adopted CRISP-DM and used it successfully on numerous customer engagements covering many industries and business problems. Throughout this time, we have seen service suppliers from outside the consortium adopt CRISP-DM, repeated references to it by analysts as the de facto standard for the industry, and a

It has recently come to my attention that Target Co. utilizes data mining to extract a wide spectrum of information about its customers by accumulating, analyzing and storing data about customer purchases. While I understand that this practice enables Target Co. to simultaneously deliver individually targeted advertisements across its diverse customer groupings, thereby increasing the potential for sales and improving customer retention, I also understand that large amounts of data unearthed by data mining can be manipulated to uncover hidden purchasing patterns to predict and shape future purchase decisions. Therefore, although there are significant benefits to using data mining, there are also serious costs associated with data mining that

An effective Customer Relationship Management (CRM) program can be used to identify, retain, satisfy and obtain customers by using technology to optimize strategies for understanding customers’ needs to manage business interactions with current, former, and prospective customers. Additionally, CRM also enables companies to maximize internal, external, marketing and customer service operations to better address the needs of the customer building a better relationship with customers that a more profitable. (Ahmad & Buttle, 2001)

Today, customer relationship management is very important to the business world. Most of the companies established a department and the programs to manage their relationship with the customers. Customer relationship management (CRM) is a business strategy which designed to help a company to understand and look forward to the needs of its potential and current customers (Anderson & Stang, 2000). Customer data is being collected in several different areas of the company, stored in a central database, analyzed, and distributed to key points (Anderson & Stang, 2000).The business world once was “product-centric”, the companies just provided what they could produce. However, it is now become “customer-centric”, they provide products and service

CRM is information industry that helps company to manage the relationship between customer and the organization. A company builds a customer database to know their customer better. This customer database describes relationship in sufficient details so that the organization, management and other related people can access the information easily. The company will understand more about customer’s

Easily customisable, CRISP-DM process model was used to implement this data mining project. With respects to this methodology, this study suggests to identify high value customers and to profile the customers using customer segmentation techniques and by effectively utilising data mining methods. There are mainly four phases for this analysis. Figure 3.1 illustrates the research methodology of this study.

In order to establish a suitable CRM system and increase the success rate, understanding CRM processes is especially important. Building CRM system, there are many works need to do(). Firstly, the target customer market should be identified. Different customer strategies are focused on different target customer markets based on their profitability. Then, firms set customer objectives, for example, acquire customer satisfaction and loyalty. After that, the leaders and managers should support and commit the implementation of a CRM system. At the same time, when companies change their targets, a plan about changing

Due to these new challenges, Waitrose is interested in analysing their customer data to gain an overall understanding about their online customers, to group them into categories and to identify the attributes of the high value online customers for their business. This helps them to be able to better optimize marketing programs, satisfy customers and to increase profits. The main objective of this analysis is to use the data mining techniques to analyse the customer purchasing data and to discover patterns, solutions and to find implicit but potentially useful information to answer business questions of Waitrose. In this analysis, these data mining techniques will be used to define the “High value customer profile” for Waitrose.

The key aim of this project is to develop an information system based on data mining techniques to build upon existing customer relationships and increase profit. Part & Parcel Computers has been at the forefront of the computer parts industry for the past fifteen years. They have developed a reputation for the cheapest computer parts by focussing on a cost-leadership strategy. P&P computers have a loyalty card programme that provides discounts and benefits to its customers but has not used this collected data to specifically identify and target its loyal customers. Unless P&P computers build sales volume with the data, it is merely an overhead without any tangible benefit (Cox, 2012).

Data Mining technique is the result of a long process of studies and research in the area of databases and product development. This evolution began when business data and companies was stored for the first time on computer device, with continuous

It’s very important to a business to have good customer relationships; CRM Software’s main objectives are to attract new customers while maintaining and satisfying their current customers and trying to win back the trust of former customers. Customers are one of the most important variables for a business owner to pay attention to, when trying to create a profit. Customer Relationship Management software keeps track of customer information to help the company maintain a strong relation with the customers. Good CRM Software brings together information from all databases within a company and gives

In this paper we are trying to understand how specific company software can provide better information to users, improve the business process (sales), etc. by incorporating data mining and data warehouse concepts to their existing

A detailed analysis of customer data is needed to understand the value of the customers, their buying behavior, motivators and patterns. Business intelligence can be gained by analyzing the relationships between various data sets and by performing a regression analysis on certain data sets to view changes over time. It is said that the 80% of profit to any organization is generated by 20% of their loyal customers. So its really important to indentify an organizations loyal customers and serving to their needs.

In order to begin the managing and fostering relationships, organisation needs to possess information about customers. The main source of customer data is from internal sources (e.g., billing records, customer surveys, web logs). An enterprise data is a crucial component of a CRM strategy in any organisation that wishes to apply data mining techniques. Most companies have very big databases that contain Human Resources (HR), marketing and financial resources; however, CRM requirements can be limited to a marketing data mart with partial supplies from other corporate systems (Freeman, 1999). Alternatively the information can be acquired from external sources, which might be a key source for gaining customer knowledge advantage (Hill, 1999).

on a reasonable overview of the problem. This helped in scoping the project and submitting