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Software Functionality Revealed in Detail
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 data mining crm


Data Mining: The Brains Behind eCRM
Data mining has emerged from obscure beginnings in artificial intelligence to become a viable and increasingly popular tool for putting data to work. Data

data mining crm  of products and services. Data mining has emerged from obscure beginnings in artificial intelligence to become a viable and increasingly popular tool for putting data to work. What is eCRM? Customer Relationship Management is an information industry term for methodologies and software that help companies manage customer relationships in a structured way. For example, an enterprise might build a database about its customers that describe relationships in sufficient detail. Ideally, the information in the d

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

CRM for Financial and Insurance Markets

Customer relationship management (CRM) focuses on the retention of customers by collecting data from all customer interactions with a company from all access points (by phone, mail, or Web, or in the field). The company can then use this data for specific business purposes by taking a customer-centric rather than a product-centric approach. CRM applications are front-end tools designed to facilitate the capture, consolidation, analysis, and enterprise-wide dissemination of data from existing and potential customers. This process occurs throughout the marketing, sales, and service stages, with the objective of better understanding one’s customers and anticipating their interest in an enterprise’s products or services.  

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Documents related to » data mining crm

Four Critical Success Factors to Cleansing Data


Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology.

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Data, Data Everywhere: A Special Report on Managing Information


The quantity of information in the world is soaring. Merely keeping up with, and storing new information is difficult enough. Analyzing it, to spot patterns and extract useful information, is harder still. Even so, this data deluge has great potential for good—as long as consumers, companies, and governments make the right choices about when to restrict the flow of data, and when to encourage it. Find out more.

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Connecting Your Executives to the CRM Effort: Becoming (and Remaining) a Data-driven Organization


When businesses commit to implementing customer relationship management (CRM), they commit to realigning their entire organizations around the customer. More specifically, they commit to collecting the right data—and using it the right way at the right time. But if you’re the manager championing CRM in your organization, how do you create the CRM buzz and obtain executive buy-in?

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Ask the Experts: Approaches to Data Mining ERP


From one of our readers comes this question: I am a student of IT Management; I have an ERP course and I am supposed to write an article to review new aspects of ERP systems. I’ve decided to explore the reasons for using data mining techniques in ERP systems—and to look at different modules to which these techniques have been applied. I am going to prepare a framework to determine

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CRM for Manufacturing vs. Regular CRM


A couple of weeks ago, I published a blog post called Customer Relationship Manufacturing. In this blog post, I described the symbiosis between the sales and production departments within a manufacturing company, mentioned some customer relationship management (CRM) vendors that seem to have adapted their products for the manufacturing industry, and I also promised I would get back to you with

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Customer Relationship Management Showdown: Microsoft Dynamics CRM vs. Oncontact CRM vs. SageCRM


For this Showdown, we looked at all three of the main CRM modules: sales force automation, marketing automation, and customer service and support. To eliminate any chance of bias and to ensure a level playing field, all the criteria that make up these three modules in our CRM Evaluation Center were given equal weight and priority. In other words, no area of functionality was treated as being more important than any other.

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New Data Protection Strategies


One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased demand for data capacity and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment. Learn about data protection strategies that can help organizations meet these demands while maintaining flat budgets.

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Big Data, Little Data, and Everything in Between—IBM SPSS Solutions Help You Bring Analytics to Everyone


Regardless of the type or scale of business data your users need to harness and analyze, they need a straightforward, visual solution that is easy to use on the front end and highly scalable on the back end. Fortunately, IBM SPSS solutions provide just such an ecosystem that can make different kinds of data stores—from Hadoop to those proverbial spreadsheets—useful sources of business insight and decision support.

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Data Quality Strategy: A Step-by-Step Approach


To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality.

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A Roadmap to Data Migration Success


Many large business initiatives and information technology (IT) projects depend upon the successful migration of data—from a legacy source, or multiple sources, to a new target database. Effective planning and scoping can help you address the associated challenges and minimize risk for errors. This paper provides insights into what issues are unique to data migration projects and to offer advice on how to best approach them.

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