2020 Data Driven Research Challenge

The 2020 MSOM Data Driven Research Challenge

JD.com and the MSOM society are partnering to offer MSOM members access to JD.com Transaction level data to encourage them to conduct data driven research.

In this competition, researchers will compete by building econometric models or data driven models using real data either to address some of the suggested questions below, or address questions of their own interest.

Eligibility Criteria: Who can enter?
(1)  MSOM members are encouraged to participate.
(2)  At least one author of the submitted paper must be an existing MSOM member in 2019.
(3)  Each MSOM member may enter the competition by submitting at most one paper.

Judging Criteria:  What the judges will be looking for?
All entries will be judged according to the following criteria
Criteria 1: Data-Driven.
Criteria 2: Potential Impact on Practice.
Criteria 3: Contribution to the Research Literature.
Criteria 4: Generalizability / Scalability.

INFORMS 2019: Launch the competition.
October 1st, 2020: Competition submissions deadline.
October, 2020: Judges to review submissions and select finalists
November, 2020 during INFORMS conference (exact date -- TBA): Finalist presentations, judges select winners, and winner(s) announcement.

* Finalist will be invited for a Fast Track submission to M&SOMFast Track means that the paper will go through 1 round of review before making a final decision.

Data Acquisition and Submission Guidelines:
(1) You need to be an MSOM member in 2019.
(2) You need to use your INFORMS Member ID and Password to access the data.
(3) You can access the data here.
(4) Before the deadline, you can submit the paper to https://mc.manuscriptcentral.com/msom and select 2020 Data Driven Research Challenge.

*   If you have any further questions, please email one of the co-chairs.

JD.com would like to encourage researchers to explore the provided data and develop innovative solutions to address the following problems (or other research problems of their own choosing):

  1. Which product attributes and/or features have predictive power about customer’s product choice? Does customer’s product choice differ by channel (e.g., purchasing via mobile phones versus personal computers), by region, and by brand loyalty?
  2. Would more products with similar attributes and features improve or hinder sales revenues for JD.com?
  3. For a specific target customer segment (e.g., female customers in a tier 1 city), what should merchants and brands do to improve their sales performance?
  4. What is the impact of various pricing and promotion strategies on product sales? How should JD.com improve its pricing and promotion strategy? In particular, among all the promotion methods (e.g., direct discounts, bundle discounts, and volume discounts), which one is more effective?
  5. Do ordinary customers behave differently from JD.com’s PLUS members? How should JD.com improve its pricing and shipping strategy for its PLUS members?
  6. How should JD.com improve its demand forecast accuracy for different geographic regions and different customer groups?
  7. How should JD.com improve its fulfillment efficiency and customer experience with better inventory allocation strategies in a multilevel inventory network?

Committee Bio: 

JD.com Team Bio