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Ramasubramanian Sundararajan

Researcher at PSNA College of Engineering and Technology

Publications -  31
Citations -  298

Ramasubramanian Sundararajan is an academic researcher from PSNA College of Engineering and Technology. The author has contributed to research in topics: Economic order quantity & Inflation. The author has an hindex of 7, co-authored 31 publications receiving 259 citations. Previous affiliations of Ramasubramanian Sundararajan include General Electric & Sabre Airline Solutions.

Papers
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Patent

System and method for dynamic allocation of resources in a computing grid

TL;DR: A system for dynamically allocating resources in a computing grid is provided in this article, which comprises a resource management component, a service level agreement (SLA) management component and a job scheduling component.
Patent

System and method for optimizing cross-sell decisions for financial products

TL;DR: In this article, a method for selecting a target list of customers for making cross-sell offers to, for a financial product is provided, which includes obtaining customer-level information related to one or more members from a historical database.
Journal ArticleDOI

A fuzzy mathematical programming approach for cross-sell optimization in retail banking

TL;DR: This work considers the problem of selecting the optimal list of customers to target for a cross-sell campaign in a retail bank and proposes a fuzzy mathematical programming technique to address the issues.
Journal ArticleDOI

A metric for customer lifetime value of credit card customers

TL;DR: In this paper, a conceptual model for revenue from a credit card customer and a metric for customer lifetime value (CLV) is presented. And the model has been designed specifically for credit card customers and simulated different states of a customer to demonstrate how the proposed metric works.
Journal ArticleDOI

Marketing Optimization in Retail Banking

TL;DR: This paper addresses the problem of making optimal product offers to customers of a retail bank by using techniques including Markov chains, genetic algorithms, mathematical programming, and design of experiments, which had an estimated financial impact of around $20 million.