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Top : Data Mining: Data mining is the process of collecting information from your prospective customers, making sense of it to make decisions about how to make your organization more successful.
Articles:
Customer Data Mining for Gold - by Darrell ZahorskyYour small business has a potential gold mine of customer data. Do you know how to collect and use this vital information? Expand your understanding of customer data mining and experience insight to improve your small business profits.""Each interaction your company has with a customer represents an opportunity to learn more to best serve their needs. Take advantage of this opportunity or a competitor will.""The collection and analysis of customer data can help you outpace your competitors but there are other benefits associated with customer data mining (Added: 3-Aug-2010 Hits: 290 )
Predictive analytics can transform customer service - by na
Every organization wants to know what the customer is thinking about its services or products. However, in this age of instant gratification, customers are impatient and want answers to their queries immediately.Can information technology help? (Added: 18-Nov-2011 Hits: 63 )
Data Mining For Customer Service Loyalty (pdf) - by na
What truly constitutes customer loyalty has been an ongoing debate among"members of the marketing community. This is not so much about the semantic"definition of customer loyalty - most marketers would agree that this behaviour is"represented by a strong affinity or attachment to a given company’s products or"services. The differences arise when marketers attempt to measure or evaluate"customer loyalty."The implications here are that marketers need first to define metrics and"measures of customer loyalty. Once established, we must then determine if the"data is readily available in the current information environment for the creation of"these measures. (Added: 3-Aug-2010 Hits: 263 )
Data Mining and Privacy: A conflict in the making? - by Kurt Thearling
Privacy. It’s a loaded issue. In recent years privacy concerns have taken on a more significant role in American society as merchants, insurance companies, and government agencies amass warehouses containing personal data. The concerns that people have over the collection of this data will naturally extend to any analytic capabilities applied to the data. Users of data mining should start thinking about how their use of this technology will be impacted by legal issues related to privacy. (Added: 1-Nov-2010 Hits: 212 )
Data Mining and Customer Relationships - by Kurt Thearling
Most marketers understand the value of collecting customer data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs. In this accessible introduction, Kurt Thearling provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine customer relationships. (Added: 1-Nov-2010 Hits: 288 )
Turning Data into Profit: Using the Customer Experience to Drive Improvement and Growth at Oracle by Jeremy Whyte - by Jeremy Whyte
is well documented that optimizing customer loyalty has a direct and positive impact on a company’s financial performance and strategy. Increasing customer loyalty through improved customer experiences can be driven by the insight gained from a comprehensive customer feedback program. As companies have better visibility into the attitudes, expectations and preferences of their customers, they can cost-effectively tailor their sales, marketing and services programs to better align solutions with the business objectives of their customers. Yet many companies continue to struggle with strategy, namely how to translate customer feedback into action and improve the bottom line. (Added: 2-Aug-2010 Hits: 277 )
Scoring a Data Mining Model - by Kurt Thearling
Once a model has been created by a data mining application, the model can then be used to make predictions for new data. The process of using the model is distinct from the process that creates the model. Typically, a model is used multiple times after it is created to score different databases. (Added: 1-Nov-2010 Hits: 228 )
Increasing Customer Value by Integrating Data Mining and Campaign Management Software - by Kurt Thearling
As a database marketer, you understand that some customers present much greater profit potential than others. But, how will you find those high-potential customers in a database that contains hundreds of data items for each of millions of customers?""Data Mining software can help find the "high-profit" gems buried in mountains of information. However, merely identifying your best prospects is not enough to improve customer value. You must somehow fit your Data Mining results into the execution of marketing campaigns that enhance the profitability of customer relationships.""Unfortunately, Data Mining and Campaign Management technologies have followed separate paths %u2013 until now. Your organization stands to gain a competitive edge by understanding and utilizing this new union.""This white paper describes how you can profit from the integration of Data Mining and Campaign Management technologie (Added: 1-Nov-2010 Hits: 263 )
Increasing Customer Value by Integrating Data Mining and Campaign Management Software - by Kurt Thurling
As a database marketer, you understand that some customers present much greater profit potential than others. But, how will you find those high-potential customers in a database that contains hundreds of data items for each of millions of customers?""Data Mining software can help find the "high-profit" gems buried in mountains of information. However, merely identifying your best prospects is not enough to improve customer value. You must somehow fit your Data Mining results into the execution of marketing campaigns that enhance the profitability of customer relationships.""Unfortunately, Data Mining and Campaign Management technologies have followed separate paths %u2013 until now. Your organization stands to gain a competitive edge by understanding and utilizing this new union.""This white paper describes how you can profit from the integration of Data Mining and Campaign Management technologies (Added: 3-Aug-2010 Hits: 245 )
An Overview of Data Mining Techniques - by Alex Berson, Stephen Smith, and Kurt Thearling
his overview provides a description of some of the most common data mining algorithms in use today. We have broken the discussion into two sections, each with a specific theme: 1) Classical Techniques such as statistics, neighborhoods and clustering, and 2) Next Generation Techniques such as trees, networks and rules. Each section will describe a number of data mining algorithms at a high level, focusing on the "big picture" so that the reader will be able to understand how each algorithm fits into the landscape of data mining techniques. (Added: 1-Nov-2010 Hits: 255 )
Data Mining and Customer Relationships - by Kurt Thurling
Most marketers understand the value of collecting customer data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs. In this accessible introduction, Kurt Thearling provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine customer relationships. (Added: 3-Aug-2010 Hits: 256 )
Understanding Data Mining: It's All in the Interaction - by Kurt Thearling
Data mining is a relatively unique process. In most standard database operations, nearly all of the results presented to the user are something that they knew existed in the database already. Data mining, on the other hand, extracts information from a database that the user did not know existed. Relationships between variables and customer behaviors that are non-intuitive are the jewels that data mining hopes to figure out. This is where visualization comes in. (Added: 1-Nov-2010 Hits: 241 )
An Overview of Data Mining Techniques - by Kurt Thurling
This overview provides a description of some of the most common data mining algorithms in use today. We have broken the discussion into two sections, each with a specific theme: 1) Classical Techniques such as statistics, neighborhoods and clustering, and 2) Next Generation Techniques such as trees, networks and rules. Each section will describe a number of data mining algorithms at a high level, focusing on the "big picture" so that the reader will be able to understand how each algorithm fits into the landscape of data mining techniques (Added: 3-Aug-2010 Hits: 262 )
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