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So Yeon Chun

About Me

So Yeon Chun

Associate Professor of Technology and Operations Management

INSEAD

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Research Interests

My research interests are data-driven revenue and management (pricing and forecasting), and operations and marketing interface utilizing applied statistics and stochastic optimization.

  • Operations with loyalty programs and point currency
  • Data-driven revenue management and dynamic pricing with applications to transportation, retail, and hospitality industries
  • Consumer choice behavior and substitution patterns
  • Behavioral experiments and field studies
  • Statistics and stochastic optimization algorithms with large-scale datasets

More details can be found on the <Research> page.


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Resume

Education

Ph.D., Operations Research

Georgia Institute of Technology, Atlanta, Georgia
School of Industrial and Systems Engineering

  • Dissertation:
    “Hybrid is Good: Stochastic Optimization and Applied Statistics for OR”

M.S., Applied Statistics

Georgia Institute of Technology, Atlanta, Georgia

B.S., Industrial Engineering

Seoul National University, Seoul, Korea
Department of Industrial Engineering

  • Admitted with highest honors
  • Graduated with highest honors (summa cum laude)

Professional Experience

Operations Research Analyst

JDA Software Group, Inc., Atlanta, Georgia

  • Analyzed hotel and cruise line booking and sales data
  • Developed price elasticity estimation and demand forecasting systems for a price optimization solution for hospitality clients

Researcher

IBM Thomas J. Watson Research Center, Yorktown, New York

  • Analyzed traffic time series data
  • Developed traffic decision support system (DSS) architecture and optimizer (control) algorithms in real-time traffic management centers

Researcher

IBM Thomas J. Watson Research Center, Hawthorne, New York

  • Analyzed retail sales transaction data for promotion planning
  • Developed spatial-temporal data mining statistics techniques for real-time anomaly detection
    for road user (congestion) charging
  • Developed optimization models for a back-office enforcement process of vehicle records

Professional Service

Secretary/Treasurer

INFORMS Revenue Management. and Pricing Section, 2015-2016

Revenue Management and Pricing Track Organizer

POMS Annual Conference, 2016 (13 sessions with 54 talks)

Revenue Management and Pricing Cluster Organizer

INFORMS Annual Conference, 2015 (48 Sessions with 185 talks)

Conference Session Chair

POMS Annual Conference, 2015
INFORMS Annual Conference, 2011-Present

Paper Competition Judge

MSOM Student Paper Competition, 2014-Present

Invited Journal Reviewer

Management Science, Operations Research, Manufacturing and Service Operations Management, Production and Operations Management, Transportation Science, IIE Transactions, Naval Research Logistics

Honors

INFORMS Future Academician Doctoral Colloquium

Austin, Texas, 2010

Thank a Teacher Recipient

Georgia Institute of Technology, 2010 (Honored at the Dean Griffin Day Luncheon)

Award for Top Performance on Ph.D. Comprehensive Exam

Georgia Institute of Technology (Perfect Score)

Presidential Honor Recipient

Seoul National University (Ranked First in the
Industrial Engineering Department)

Best Student Scholarship

Seoul National University

Math Olympiad

Seoul, Korea (Honorable Mention)

Research


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I enjoy building (stochastic) mathematical models and testing them with real data to examine the chaotic reality around us.

This endeavor is inherently interdisciplinary, requiring application-specific knowledge and simultaneous expertise in two fields: statistical analysis and optimization under uncertainty.

My research takes a multi-method approach that combines analytical and empirical methods and addresses challenging problems that lie at the intersections of Operations, Marketing, Economics, and Risk Management.

Publications

Refereed Publications


Dynamic Pricing with Point Redemption

(with Hakjin Chung and HyunSoo Ahn)
Manufacturing and Service Operations Management 26(5):1878-1896.
[PDF]

Dynamic Pricing with Point Redemption Program

Many sellers allow consumers to pay with reward points instead of cash or credit card. While the revenue implications of cash and credit card purchases are transparent, the implications of reward sales are not trivial, especially when a firm that issues points is not a seller. In this case, a seller receives a monetary compensation or reimbursement from the point issuer when a consumer purchases the good by redeeming points. In this paper, we examine how reward sales influence a seller’s pricing and inventory decisions. In particular, we consider a consumer who can choose to pay with cash or points based on her attributes—reservation price, point balance, and the perceived value of a point. Then, we incorporate this consumer choice model into a dynamic pricing model where a seller earns revenues from both cash and reward sales.

How Loyalty Programs Are Saving Airlines

(with Evert Boer)
Harvard Business Review (online), (2021)
[PDF]

Refocusing Loyalty Programs in the Era of Big Data: A Societal Lens Paradigm

(with Valeria Stourm, Scott A. Neslin, Eric T. Bradlow, Els Breugelmans, Pedro Gardete, P. K. Kannan, Praveen Kopalle, Young-Hoon Park, David Restrepo Amariles, Raphael Thomadsen, Yuping Liu-Thompkins, and Rajkumar Venkatesan)
Marketing Letters, Special Issue: 11th Triennial Invitational Choice Symposium, 31 (2020): 405–418
[PDF]

Risk-based Loan Pricing: Portfolio Optimization Approach with Marginal Risk Contribution

(with Miguel Lejeune)
Management Science, 66.8 (2020): 3735–3753
[PDF]

Risk-based Loan Pricing: Portfolio Optimization Approach with Marginal Risk Contribution

We consider a lender (bank) who determines the optimal loan price (interest rates) to offer to prospective borrowers under uncertain risk and borrower response. A borrower may or may not accept the loan at the price offered, and in the presence of default risk, both the principal loaned and the interest income become uncertain. We present a risk-based loan pricing optimization framework, which explicitly takes into account marginal risk contribution, portfolio risk, and borrower’s acceptance probability. Marginal risk assesses the amount a prospective loan would contribute to the bank’s loan portfolio risk by capturing the interrelationship between a prospective loan and the existing loans in the portfolio and is evaluated with respect to the Value-at-Risk and Conditional-Value-at-Risk risk measures.

Loyalty Program Liabilities and Point Values

(with Dan Iancu and Nikolaos Trichakis)
Manufacturing and Service Operations Management, 22.2 (2019): 223–428
[PDF]

Loyalty Program Liabilities and Point Values

Loyalty programs (LP) introduce a new currency, the points, through which customers transact with firms. Such points represent a promise for future service, and their monetary value thus counts as a liability on the issuing firms’ balance sheets. Consequently, adjusting the value of points has a first order effect on profitability and performance, and emerges as a core operating decision. We study the problem of optimally setting the points’ value in view of their associated liabilities.

Strategic Consumers, Revenue Management, and the Design of Loyalty Programs

(with Anton Ovchinnikov)
Management Science, 65.9 (2019): 3949–4450
[PDF]

Strategic Consumers, Revenue Management, and the Design of Loyalty Programs

We study the interaction between the design of a premium-status loyalty program, revenue management, and strategic consumer behavior… (summary continues)

Modified Distribution-free Goodness of Fit Test Statistic

(with Alex Shapiro and Michael Browne)
Psychometrika 83.1 (2018): 48–66
[PDF]

Modified Distribution-free Goodness-of-Fit Test Statistic

Covariance structure analysis … The proposed statistic deals with the possible ill conditioning of the involved large scale covariance matrices.

When Friends Become Competitors: The Design of Resource Exchange Alliances

(with Anton Kleywegt and Alex Shapiro)
Management Science, 63.7 (2016): 2127–2145 (Previous title: “Resource Exchange Seller Alliances”)
[PDF]

Resource Exchange Seller Alliances: Two-stage stochastic model with equilibrium constraints

Many carriers … robust results can be obtained with a reasonable amount of computational effort.

Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics

(with Alex Shapiro and Stan Uryasev)
Operations Research, 60.4 (2012): 739–756
[PDF]

Conditional Value-at-Risk and Average Value-at-Risk : Estimation and Asymptotics

Value-at-Risk and Average Value-at-Risk (Conditional Value-at-Risk, Expected Shortfall) are widely used measures of financial risk…

Construction of Covariance Matrices with a Specified Discrepancy Function Minimizer, with Application to Factor Analysis

(with Alex Shapiro)
SIAM Journal on Matrix Analysis and Applications, 31.4 (2010): 1570–1583
[PDF]

Construction of Covariance Matrices …

Abstract: Covariance structure analysis … Keywords: Model misspecification …

Normal versus Noncentral Chi-square Asymptotics of Misspecified Models

(with Alex Shapiro)
Multivariate Behavioral Research, 44.6 (2009): 803–827
[PDF]

Normal versus Noncentral Chi-square Asymptotics of Misspecified Models

Abstract: The noncentral chi-square approximation …

Papers under Review/Revision/Working Papers

The Digital Hustle: Can After-Hours Work Emails Shape Career Success?

(with Priscilla Rodriguez and Jurgen Mihm)

Points Plus Money: Uncovering the Impact of Mixed Currency Redemptions

(with Freddy Lim)

[PDF]

Paying with Money or Paying with Points: How Variable Versus Fixed Exchange Rates Influence Loyalty Point Redemption

(with Rebecca Hamilton)

[PDF]

Paying with Money or Paying with Points: How Variable Versus Fixed Exchange Rates Influence Loyalty Point Redemption

Many loyalty programs award members points … we investigate how the numerosity, stability, and favorability of the exchange rate affect consumers’ payment choices.

Loyalty Currency and Mental Accounting: Do Consumers Treat Points Like Money?

(with Freddy Lim and Ville Satopaa)
[PDF]

Seasonality for New Product Diffusions

(with Evren Ozkaya and Pinar Keskinocak)
Working paper

Seasonality model for new product diffusions

In forecasting new product diffusions with short life cycles … especially when data series is short with (nonlinear) diffusion trend and high random error variance.

In Preparation

Gamification of Loyalty Program and Consumer Behavior

Intrinsic vs. Extrinsic Value of Loyalty Points, Evidence from Hotel Reward Program Data

(with Rebecca Hamilton and Chekitan Dev)

Empirical Analysis of Loyalty Point Redemption Behavior for Experience Products

(with Felipe Walker)

Internet Service Pricing and Data Throttling: Evidence from Internet Service Sales Data

Dynamic Discrete Choice Modeling for the Optimal Stopping Problem, with Application to Auto Industry Sales and Promotion Data

(with Anton Kleywegt)

Dynamic Discrete Choice Modeling for the Optimal Stopping Problem

Sales promotion plays an important role … our models appear to hold promise for building better business strategies.

Patents

Demand Analysis and Price Elasticity of Demand Estimation System for Revenue Management and Pricing

(with Ronald Menich)
Submitted for filing

Targeted Enforcement for Road User Charging

(with Todd Appel, Duncan Ashby, Milind Naphade, Richard Nash, Anshul Sheopuri, Anders Thorndivist, and Martin Vuyk)
U.S. Patent Application No. 12,752,578

Intelligent Decision Support System Optimizer for a Real-Time Command Center

(with Laura Wynter)
U.S. Patent No. 8,458,113. 4 Jun. 2013

Anomaly Detection for Road User Charging Systems

(with Todd Appel, Duncan Ashby, Milind Naphade, Richard Nash, Anshul Sheopuri, Anders Thorndivist, and Martin Vuyk)
U.S. Patent No. 9,261,375. 16 Feb. 2016


Personal

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Music

Music


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Food

Food


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Travel

Travel

Teaching

Teaching Experience

Foundations of Operations Management B (2020-Present)

PhD, INSEAD

ART: Analytics for Retail and Travel (2019-Present)

MBA Elective, INSEAD, France/Singapore

Instructor, OPIM 173 Business Statistics (2013-2019)

Georgetown University, Washington D.C.
McDonough School of Business

Instructor, ISYE 2028 Basic Statistical Methods (Spring 2010)

Georgia Institute of Technology, Atlanta, Georgia
School of Industrial and Systems Engineering

Teaching Assistant, ISYE 6402 Time Series Analysis (Spring 2007)

Georgia Institute of Technology, Atlanta, GA
School of Industrial and Systems Engineering

Teaching Assistant, ISYE 3770 Probability and Statistics (Fall 2006, Spring 2008, Fall 2009)

Georgia Institute of Technology, Atlanta, GA
School of Industrial and Systems Engineering

Highlights from Course Survey

  • “Professor Chun is one of the best and most inspirational professors I have had yet at Georgetown, and she will make you fall in love with statistics. Everything is clear cut and explained and is taught in an amazing fashion.”
  • “Professor Chun was probably the ideal professor to have – helpful, extremely organized, and very kind. She seemed to be genuinely interested in teaching students and if every professor was like her, Georgetown would be #1 ranking.”
  • “Professor Chun is an excellent teacher and is always willing to help and explain things that I have trouble with. Additionally, she is always very happy and brings a lot of energy to the class! “
  • “I believe Professor Chun really made the difference in making statistics an interesting and understandable subject. Her dedication and resourcefulness are unparalleled to that of any other professors I have had. She is extremely helpful and offered guidance every step along the way.”
  • “Prof. Chun was a really great professor! Her enthusiasm about business stats was evident in each class, which made her lectures interesting and engaging. She is sweet and incredibly approachable, and cares so much about each student’s success that she really goes out of her way to make sure people receive help when they need it. The exams in this course were challenging, which really required me to study hard, but I now have a solid foundation in stats that has already helped me in my other classes. I am so glad I took this course with Prof. Chun and will be sure to recommend her to other students!”
  • “Thanks for an enjoyable semester. I really liked the way the course was taught because you focused more on practical topics that we will one day use in our careers, rather than just dwelling on proofs and derivations.”
  • “I really really admire the professor! I know she was genuinely interested in the betterment of all of her
    students. Since the first day of classes, we were challenged and nicely encouraged to do our best. Right from the start, I knew that the professor had best intentions at heart. Honestly, couldn’t have asked for a better professor!”
  • “I had a lot of fun in this class. You had a very organized set up and your notes were very easy to follow. The tests were just right, challenging enough so students can differentiate themselves but not so difficult that the course becomes discouraging. You were always very helpful and straightforward. I really enjoyed this class and am thankful to have had you as my professor.”
  • “Great and effective instructor! I learned a lot from this course. The lectures were extremely well organized and the tests were fair. Hope to take another class with you in the future!”
  • “Professor Chun is the best teacher I’ve had in the MSB. She genuinely cares about her students, challenges us, and goes above and beyond in and out of the classroom for us.”
  • “Professor Chun was amazing! She is by far my favorite professor that I’ve had at Georgetown. She is
    helpful and engaging during class and during office hours. She makes the material fun to learn and she is
    very helpful during office hours!”
  • “Out of my two semesters at Georgetown, Professor Chun was by far the most enthusiastic about her course material and teaching her students.”
  • “Excellent instructor, she is really willing to help the student progress throughout the semester.”
  • “Thanks for being a great teacher. I am hoping that we will stay in touch for the future. You definitely know what you are talking about and I know that I could use your statistical guidance and advice. Thanks for a great semester!”
  • “Excellent job connecting with students. Status check helped focus attention on most important topics. Great job!”

Contact

+33 1 60 72 40 00


Fontainebleau, France


soyeon.chun@insead.edu


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