Cross Commerce Media, Inc.

Other

Heidi Messer

Stephen Dale Messer

Russell William Martin, Jr

Michael Martinov

Kristis Makris

Paolo Cappellari

Paul Ashaolu

Antonio Maccioni

Alejandro Tavella

A novel approach to facilitating access to valuable actionable content from a multi-tenant database involves system generated ranking of connection content with associated data retrieval methods and systems, utilizing 'connector' scores to rank responsive content. The system “learns” how to optimize retrieving and ranking high value actionable content with experience; and applies optimized scoring parameters to enhance future operations. The computer platform is greatly improved by delivering actionable content that is immediately translated into critical operations and tasks recommended by the system to support transactions for the User.

Systems and methods for enhancing and facilitating access to specialized data

Write back to customer relationship management systems is provided. Data logging of activities can be performed for sales professionals that use other third-party software solutions during their selling process. The logged data can be written back to the CRM system instead of needing input by the sales professionals. Certain data and generated insights can be written back to further enhance the data available to clients' sales organizations. Capability of writing back to target CRM systems can leverage existing APIs. Identification of data to be written back to the CRM systems is performed by an intelligent write back (IWB) system. The IWB system will write back to the standard objects in tenant CRMs. The IWB system can create custom fields within those objects for write back data.

Intelligent write back for customer relationship management

A novel approach to facilitating access to valuable actionable content from a multi-tenant database involves system generated ranking of connection content with associated data retrieval methods and systems, utilizing 'connector' scores to rank responsive content. The system 'learns' how to optimize retrieving and ranking high value actionable content with experience; and applies optimized scoring parameters to enhance future operations. The computer platform is greatly improved by delivering actionable content that is immediately translated into critical operations and tasks recommended by the system to support transactions for the User.

Large-scale advanced database models, systems, and methods provide a responsive, scalable data storage solution which is ripe for use in data warehousing and analytics environments. These advanced database models, systems, and methods provide for dramatically increased performance in accessing data as well as increased storage capabilities in the data set.

Advanced database systems and methods for use in a multi-tenant system

A computing system includes memory storing executable instructions and one or more processors operatively connected to the memory. The one or more processors execute the executable instructions to effectuate a method. The method may include (i) analyzing raw data obtained from a plurality of different data sources in order to identify one or more data structures of the raw data and to tag data identifying at least one of the plurality of different data sources; and (ii) generating a plurality of Universal Data Model (UDM) constructs. Each UDM construct may be based at least in part on the identified data structure(s) of the raw data. Each UDM construct may exclude the tagged data identifying at least one of the plurality of different data sources. Each UDM construct may organize the raw data into a particular arrangement of rows and columns.

Providing a multi-tenant knowledge network

Advanced database systems and methods

A non-transitory computer-readable medium includes executable instructions that when executed by a processor cause the processor to effectuate a method. The method may include obtaining raw data from a plurality of different data sources; analyzing the raw data to identify one or more data structures of the raw data and to tag data identifying at least one of the plurality of different data sources; generating a plurality of Universal Data Model (UDM) constructs, each UDM construct being based at least in part on the one or more data structures of the raw data and each UDM construct excluding the source-identifying data.

Computing system, method, and non-transitory computer-readable medium for providing a multi-tenant knowledge network

Business intelligence, decision support and knowledge management network systems and methods provide efficient, robust, and business-friendly services for the rapid analysis of massive amounts of business, electronic, and other disparate data into actionable intelligence. An advantageous element of the multi-party knowledge network allows the aggregation of common data formats in order to analyze a combined dataset consisting of information from multiple parties, thereby providing additional business intelligence than with a single set of data alone.

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New York, NY, United States of America

Heidi Messer