American Express Travel Related Services Company, Inc.

Srinivas K Kumandan

Jordan Meyerowitz

Yogaraj Jayaprakasam

Abhishek Jain

Hector Flores

Robin Jain

Lori Jean Cales

Orville Anthony Williams

Jeffrey J Raible

Matthew J B Davis

Keith R Janian

Sivagini Ahamparam

Toni Elizabeth Scanlan

Nhan T Pham

Systems and methods for optimizing user task schedules in a customer relationship management (CRM) platform is disclosed. In one example, a system comprising a computing device and a cache. The computing device is configured to generate child user task schedules and calculate task win probabilities of tasks for the child user task schedules using a machine learning system. The machine learning system is used to determine the plurality of task win probabilities. The computing device is also configured to calculate total task win probabilities for the child user task schedules based on the task win probabilities and is configured to determine an optimized user task schedule by selecting a respective user task schedule having a greatest total task win probability from a subset of the plurality of child user task schedules stored in the distributed cache.

Optimizing user task schedules in a customer relationship management platform

Systems and methods for optimizing user task schedules in a customer relationship management (CRM) platform is disclosed. The system may optimally input tasks into time slots in a user schedule to generate the optimized user task schedule. The system may generate a plurality of user task schedules and calculate a total task win probability for each of the user task schedules. The system may comprise the total task win probabilities and select the user task schedule having the greatest total task win probability. The system may also perform a genetic processing analysis of the user task schedules to further optimize task placement in the user task schedule.

A system, computer product and method are provided for providing business travel and expense benchmarking analysis. A first data storage receives and stores travel data for peer entities. A second data storage receives and stores expense transaction data for the plurality of peer entities. A data storage server aggregates the metrics of the travel data and the expense transaction data of the plurality of peer entities, generating aggregated travel and expense data. A processor compares a travel and/or expense transaction metric of an individual entity to a corresponding metric of the aggregated travel data and expense transaction data and a communications interface electronically communicates the result of the comparison to a client host computer.

System and method for quantitative peer travel and expense benchmarking analysis

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Chandler, AZ, United States of America

Srinivas K Kumandan