Meisam Soltani-koopa

Management Analytics
Cohort 2019
Queen's University

Email

m.soltanik@queensu.ca

Program of study

Management Analytics

University

Queen's University

Academic degree

Doctoral's

Academic background

Education: • PhD Candidate in Management Science, Smith School of Business, Kingston, Ontario, Canada, 2015-Present • Master of Applied Science in Management Sciences University of Waterloo, Waterloo, Ontario, Canada, 2011-2014 • Master of Science in Industrial Engineering Tarbiat Modares University, Tehran, Iran, 1997-2000 • Bachelor of Science in Electrical Engineering Conference Presentations: • Soltani-koopa, Meisam; Kamran Habibkhani, Hootan; Nediak, Mikhail; Ovchinnikov, Anton (October 2021). “Using reinforcement learning to maximize customer profitability and CLV at financial institutions”. Presentation at Informs Annual Meeting, Anaheim, CA, USA. • Soltani-koopa, Meisam; Levin, Yuri; Nediak, Mikhail; Ovchinnikov, Anton (November 2018). “A Vertical Competition in a Supply Chain with a Cash Constraint Rational Newsvendor Type Retailer and a Supplier Offering Trade Credit”. Presentation at Informs Annual Meeting, Phoenix, AZ, USA. • Soltani-koopa, Meisam; Levin, Yuri; Nediak, Mikhail; Ovchinnikov, Anton (June 2018). “A lifetime-value of a liquidity-constrained retailer”. Presentation at CORS Conference, Halifax, NS, Canada. • Soltani-koopa, Meisam; Levin, Yuri; Nediak, Mikhail; Ovchinnikov, Anton (May 2017). “Trade Credit and Lifetime Value of a Newsvendor Buyer”. Presentation at POMS Annual Conference, Seattle, Washington, USA. • Soltani-koopa, Meisam; Levin, Yuri; Nediak, Mikhail; Ovchinnikov, Anton (July 2017). “A lifetime-value of a liquidity-constrained retailer”. Presentation at IFORS Conference, Quebec City, QC, Canada. • Soltani-koopa, Meisam; Levin, Yuri; Nediak, Mikhail; Ovchinnikov, Anton (November 2016). “Trade Credit and Lifetime Value of a Newsvendor Buyer”. Presentation at Informs Annual Meeting, Nashville, TN, USA. Accepted Publication: Soltani-koopa, Meisam; Kamran Habibkhani, Hootan; Nediak, Mikhail; Ovchinnikov, Anton (December 2021). “Customer Lifetime Value (CLV) and Fund Transfer Pricing (FTP) for Precision Retail in Financial Services”. Precision Retailing book chapter, McGill University, Montreal, Canada. Working Paper: Soltani-koopa M., Abouee-Mehrizi H., and Nediak M. “A New Simple Efficient Allocation Policy for Make-to-Stock Systems with Both Backlogs and Lost Sales”. Teaching Experience: Lecturer: Queen’s University, Kingston, ON, Canada • COMM 461: Data Science for Business (32 students), Winter 2019 University of Waterloo, Waterloo, ON, Canada • MSCI 261: Engineering Economics (90 students), Spring 2015 • MSCI 271: Advance Calculus and Numerical Methods (50 students), 2013-2014

About me

Meisam, with a background in Electrical Engineering and automotive industry moved to data science and analytics world. He graduated from the University of Waterloo Management Sciences program in 2014. While pursuing his Ph.D. in Management Analytics at Queens University, he has been involved in many data science and analytics projects in Scotiabank.

Outputs

Using reinforcement learning to maximize customer profitability and CLV at financial institutions

Customer Lifetime Value, CLV, is a popular measure to understand the future profitability of customers to allocate resources in more efficient ways to keep the company alive during difficult economic situations. We use machine learning tools to predict the expected revenue from each customer during one year of his/her relationship with the institution as the CLV of the customer. The approach is implemented on two datasets from two international financial institutions. Different feature engineering techniques were applied to improve the prediction power of the model. We used two stage or three stage prediction models. In the second phase, we train a reinforcement learning algorithm based on the history of marketing activities and the CLV as the state of customers to determine the optimum marketing action for customers in each state to maximize their profitability.