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Product Modules:

DataGYM is a set of data management software tools, which are usually deployed in processes designed to meet specific business objectives. For example, one-time clean up of data files, maintaining a database just right for your business, and enhancing your marketing activities over the Internet and other conventional channels.

The Software has various modules, which can customized according to business needs.

DataGYM Consolidation

A customer information database is not always formatted or organized for analysis. In the pool of information that is provided by the customers, there may be:

Junk data in the name block: Mr. John Smi#$th (should be rid of the junk characters)

Unparsed data in the name block: Mr John Smith (should be broken into Title, First Name, Last Name)

DG-Conversion is the process by which data is cleaned, parsed, separated into respective fields, and broken into different components. These different components are then validated and standardized as per set parameters and organized according to the needs of the business.


DataGYM Householding

This tool helps in the building of relationship between records. A company's customer information very often consists of people from the same household/address, and the problem gets compounded when the companies buy outside (financial or demographic) data for marketing.

Householding lets us know how many clients are there in one 'household' (Householding parameters can be user-defined) so that the companies can utilize this information to optimize their marketing and communications strategies.

DataGYM allows complex "householding" with match probability settings less than 100 percent. For example, it can "household" together the following 4 versions of names and addresses:

  • Bob Smith, 123 Main Street, Milwaukee, WI 54321
  • Robert Smith, 123 Main Street, Milwaukee, WI 54321
  • R. Terrence Smith, 123 Main Street, Milwaukee, WI 54321
  • Robert T. Smith, 123 Main Street, Milwaukee, WI 54333


DataGYM Segmentation and Extraction

The master database of a company contains a lot of general information, all of which may or may not be relevant or necessary for specific management decisions at a particular point of time.

A database is processed differently from time to time to achieve specific ends.

Segmentation and Extraction are processes through which need-specific information is gleaned out of the plethora of information available from the database.

For example, if a client wants that a database be generated for the purpose of a mailing list, then the master database is 'segmented' into its components and only relevant information - Name, Address, City, State and Zip are 'extracted' from it. Or similarly, if a business is provided for a "sales hot list," it can scan all names and extract only those that satisfy the criteria.


DataGYM Instant Matching (Real time customer profiling)

There is a fine balance between how much data can be requested of an individual on-line and the point at which the prospect or the customer leaves the web site.

The Instant Matching engine allows you to take this "minimalist" information and leverage it against existing database for potential identification of customer and prospect, and provide "tailored offerings" for maximum marketing effect.

New data and old information are triggers for effective 1-to-1 communication in any business, and the DataGYM approach maximizes its use "real time" in the web site, or in "batch processes" in data warehouse operations.


DataGYM Fraud Detection

In today's business, clients and customers want to complete their transactions as fast and as efficiently as possible. But, companies have to establish the authenticity of the client before approving a sale or a service. Fraud perpetrators try to find weak links in the decision-architecture of this verification process, especially given the anonymity of the web.

Such premeditated and calculated attacks can be stopped by a Fraud Appliance based on CIANT's DataGYM Instant Matching engine. The caller-identity is matched against a stored database, and a probability is assigned to the match. Such matches can be based on very complex parameters looking for transposed last names and first names, with a few vowels or consonants changed. . . to basic matching of telephone or social security numbers or addresses.


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