AI and Analytics for Business

Research Opportunities

(Closed for Submission)

Consumer Purchase Histories and Product Supply Data for a Quick Service Restaurant Chain

This AI and Analytics for Business’ dataset is from an independent purchasing cooperative that serves as a supplier to a major quick service restaurant chain.  This unique dataset, which will allow researchers to explore research questions at the intersection of customer purchase behavior and supply chain management, consists of data collected from approximately 10,000 restaurant locations across 4 geographic regions, and contains all purchases made by customers over the course of two years.

In addition to typical transaction data, awarded teams will also have access to detailed information about what products each customer purchased, the ingredients used in that product, information about quality complaints, customer survey results, allowing a comprehensive view of the product and service quality for each customer purchase. Since some customers shop at the establishment multiple times per week, the dataset provides an opportunity for a deep dive into how perceived quality affects repeat purchasing (and customer lifetime value).

The dataset includes:

    • transaction information such as which menu item(s) were purchased, quantities of each item, and any discounts/promotions applied to the order
    • ingredient lists for individual menu items
    • records of complaints submitted by customers linked to individual restaurants, including the date on which the complaint occurred, a description of the issue, and the product(s) affected
    • metadata on specific restaurant, including location, open/close date, and store type (such as street store vs. food court storefront)

While the data sponsor is open to all kinds of proposals, possible avenues of investigation might include exploring:

    • drivers of customer lifetime value
    • effects of restaurant employees on customers satisfaction, complaints, and sales
    • the impact of product quality on sales and future customer behavior
    • if and how certain products and/or limited-time offers drive profitability, attracting new customers, and driving customer visits
    • predicting customer reactions to limited-time offers

Research Teams

Incorporating Voluntary Customer Satisfaction Surveys Into Customer Base Analysis: A Simple Empirical Model

Rutger van Oest, BI Norwegian Business School
George Knox, Tilburg University

Impact of Reward Program on Customer Behavior over Time: A Big Data Approach

Frederico Bumbaca, University of California – Irvine
Peter Rossi, University of California – Los Angeles

A Data-Driven Approach for Limited-Time Offers and Rewards Programs

Yanchong Zheng, Massachusetts Institute of Technology
Georgina Perakis, Massachusetts Institute of Technology

Improving Store Profitability by Better Supply Chain Quality Management and Inventory Control

Ming Hu, University of Toronto
Yang Li, University of Toronto
Jiahua Wu, Imperial College London

Promoting Healthy Diet in Fast Food Restaurants

Ginger Jin, University of Maryland
Ben Zou, Michigan State University

Ensuring success of franchise locations and streamlining supporting supply chains using transactional and survey data

Qiuping Yu, Indiana University
Shawn Mankad, Cornell University
Masha Shunko, University of Washington

Customer Satisfaction Spillover across Inter-Related Stores

Jae Young Lee, Yonsei University
Keunwoo Kim, University of California – Los Angeles
Woochoel Shin, University of Florida

Invisible no more? Linking Credit and Cash Transactions at the Customer-level using Bayesian Imputation

Yi Zhao, Georgia State University
Sarang Sunder, Texas Christian University

Cold Context: Modeling Unfamiliar Environments

Jonathan Gemmell, DePaul University
John Attisha, DePaul University
Pui Ling Ching, DePaul University
Lintsen Han, DePaul University
Patrick Carey, DePaul University

Loyalty program, Limited Time Offer (LTO), or both? An empirical investigation into the effect of loyalty programs and LTOs on customer behavior and firm performance

Omid Kamran-Disfani, University of Missouri-Columbia
Murali Mantrala, University of Missouri-Columbia
Vamsi Kanuri, University of Miami

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