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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.
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 crm by data mining


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

crm by data mining  likewise, the line between CRM and eCRM will become too hazy to matter. Some objectives of eCRM are to: Enable marketing departments to identify and target their best customers, manage marketing campaigns with clear goals and objectives, and generate quality leads for the sales team Assist organizations in improving telesales, account, and sales management by optimizing information shared by multiple employees, and streamlining existing processes Tailor relationships with customers to improve customer

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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 » crm by data mining

Why CRM Is So Hard and What To Do About It: Data is key to making CRM work


Making a CRM investment work is a two-step process that begins with unifying disparate systems by creating and managing standardized, reusable business definitions mapped to the different CRM system schemas throughout the organization.

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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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Microsoft says OLE for Data Mining: Is it Bull?


Microsoft released a new version of OLE DB (Object Linking and Embedding Database, based on Microsoft’s Component Object Model or COM) which supports a proprietary data mining specification. It is purported to extend the Structured Query Language (SQL) to allow easier and faster incorporation of data mining queries into existing data warehouse solutions.

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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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Unified Data Management: A Collaboration of Data Disciplines and Business Strategies


In most organizations today, data are managed in isolated silos by independent teams using various data management tools for data quality, integration, governance, and so on. In response to this situation, some organizations are adopting unified data management (UDM), a practice that holistically coordinates teams and integrates tools. This report can help your organization plan and execute effective UDM efforts.

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Understanding the PCI Data Security Standard


The payment card industry data security standard (PCI DSS) defines a comprehensive set of requirements to enhance and enforce payment account data security in a proactive rather than passive way. These include security management, policies, procedures, network architectures, software design, and other protective measures. Get a better understanding of the PCC DSS and learn the costs and benefits of compliance.

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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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Next-generation Data Auditing for Data Breach Protection and Risk Mitigation


Data breaches and leaks are on the rise—and the consequences, from theft of identity or intellectual property, can seriously compromise a company’s reputation. Stolen laptops, hacking, exposed e-mail, insider theft, and other causes of data loss can plague your company. How can you detect (and respond!) to breaches and protect your data center? Learn about the functions and benefits of an automated data auditing system.

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Data Security Is Less Expensive than Your Next Liability Lawsuit: Best Practices in Application Data Security


Insecure data. Heavy fines due to non-compliance. Loss of customers and reputation. It adds up to a nightmare scenario that businesses want to avoid at all costs. However, this nightmare is preventable: knowledge base-driven data security solutions can be critical tools for enterprises wanting to secure not only their data—but also their status in the marketplace.

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Data Leak versus Data Flood: Problems Addressed by Data Leakage and Data Breach Solutions


Data leakage and data breach are two disparate problems requiring different solutions. Data leakage prevention (DLP) monitors and prevents content from leaving a company via e-mail or Web applications. Database activity monitoring (DAM) is a data center technology that monitors how stored data is accessed. Learn why DAM complements DPL, and how you can benefit by making it part of your overall data security strategy.

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