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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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Visit the TEC store to compare leading software solutions by funtionality, so that you can make accurate and informed software purchasing decisions.
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 ez data crm


Ebix CRM
Ebix CRM began developing solutions to support agents and advisors more than 20 years ago. Our goal: to help industry professionals better leverage vital

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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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CRM for the Finance and Banking Industry - Part 1


How many different systems does a bank employee use when changing a customer’s contact information? The last time I moved and had to change my address, the bank employee used two systems, asked three or four of her co-workers for help, and took about 15 minutes to do it because the information from one system did not transfer right away to the other system. It goes without saying that this was not

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Understanding New CRM Functionality: Approaches to Ensure a Well-planned Successful CRM Implementation


Why do some customer relationship management (CRM) implementations fail? The answer: companies’ lack of understanding of their current CRM environments, and of what areas need modification or improvement. Companies with a clear understanding of what they need from a CRM solution—as well as of what CRM means to their business—are more likely to succeed. To clinch that success, some key elements should be assessed first.

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CRM Selections: When An Ounce Of Prevention Is Worth A Pound Of Cure Part One: The CRM Selection Challenge


Two of the greatest challenges IT decision makers face when selecting a CRM package is first, having a comprehensive understanding of their functional and technical requirements and second, identifying the vendors that best match their requirements. This article will focus on determining the functionality and technology required to enable business processes, and how to compare vendor offerings once those requirements have been documented.

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Your CRM Selection Challenge: CDC Pivotal CRM vs. NetSuite CRM+


Every now and then, I’ll examine the data from TEC’s past software selection projects, and explore the choices users have made. The information below is based on a real-life CRM selection project, but I have modified some of the data in order to respect confidentiality agreements still in place. To put myself in the user’s shoes, here’s how I answered the CRM Evaluation Center

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Welcome to the CRM Showdown: Microsoft Dynamics CRM vs. NetSuite CRM+


I’m Larry Blitz, editor of TEC’s Vendor Showdown series. Today’s Showdown compares two popular mid-market CRM solutions, Microsoft Dynamics CRM and NetSuite CRM+, head-to-head. I hope you find this showdown helpful and informative. I invite your comments and questions at showdown@technologyevaluation.com.

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Revamping Data Management: Big Data Proves Catalyst to Updating Data Management Strategies


Data management plays a key role in helping organizations make strategic sense of their data and how to best use it. Organizations with data management maturity have ushered in clear data goals, but many obstacles persist. This white paper reports survey results that help to establish a clear picture of how organizations are capitalizing on data management today, as well as what challenges and opportunities remain.

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Data Loss Prevention Best Practices: Managing Sensitive Data in the Enterprise


While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to an equally dangerous situation: data loss from the inside. Given today’s strict regulatory standards, data loss prevention (DLP) has become one of the most critical issues facing executives. Fortunately, effective technical solutions are now available that can help.

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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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Data Mining with MicroStrategy: Using the BI Platform to Distribute Data Mining and Predictive Analytics to the Masses


Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has been slow due to their lack of business intelligence (BI) functionality, proactive information distribution, robust security, and other necessities. Now there’s an integrated enterprise BI system that can deliver data mining and predictive analysis. Learn more.

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A Definition of Data Warehousing


There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.

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