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Article

Consumer Neuroscience: Advances in Understanding Consumer Psychology

By: Uma R. Karmarkar and Carolyn Yoon

While the study of consumer behavior has been enriched by improved abilities to generate new insights, many of the mechanisms underlying judgments and decision making remain difficult to investigate. In this review, we highlight some of the ways in which our... View Details

Keywords: Consumer Neuroscience; Neuroscience; Neuroeconomics; Consumer Psychology; Customer Behavior; Predictive Analytics; Neural Prediction; Neuroimaging; fMRI; Eye-tracking; Consumer Behavior; Marketing

Find at Harvard

March 2017 (Revised March 2022)
Case

Flashion: Art vs. Science in Fashion Retailing

By: Kris Ferreira and Karim R. Lakhani

Kate Wilson, retail analytics manager at Flashion, a fashion flash-sale site, is tasked with developing analytics to optimize pricing for first-exposure products on the site. Many in the industry have relied on years of experience and intuition to determine pricing—can... View Details

Keywords: Analytics; Pricing; Data; Service Operations; Forecasting and Prediction; Internet and the Web; Technology Adoption; Mathematical Methods; Decision Making; E-commerce; Retail Industry; Fashion Industry; United States

September 2020 (Revised March 2022)
Case

JOANN: Joannalytics Inventory Allocation Tool

By: Kris Ferreira and Srikanth Jagabathula

Michael Joyce, Vice President of Inventory Management at JOANN, championed an effort to develop and implement an inventory allocation analytics tool that used advanced analytics to predict in-season demand of seasonal items for each of JOANN’s nearly 900 stores and... View Details

Keywords: Analytics; Machine Learning; Optimization; Inventory Management; Mathematical Methods; Decision Making; Operations; Supply Chain Management; Resource Allocation; Distribution; Technology Adoption; Applications and Software; Change Management; Fashion Industry; Consumer Products Industry; Retail Industry; United States; Ohio

09 Dec 2015
Research Event

How Do You Predict Demand and Set Prices For Products Never Sold Before?

explained that the world of business analytics includes descriptive analytics (analyzing what has happened), predictive analytics (analyzing data... View Details

Keywords: by Carmen Nobel; Retail; Apparel & Accessories

February 2024
Module Note

Data-Driven Marketing in Retail Markets

By: Ayelet Israeli

This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,... View Details

October 2017 (Revised November 2017)
Case

NYC311

By: Constantine E. Kontokosta, Mitchell Weiss, Christine Snively and Sarah Gulick

Joe Morrisroe, executive director for NYC311, had some gut instincts but no definitive answer to the question he was just asked by one of the mayor’s deputies: “Are some communities being underserved by 311? How do we know we are hearing from the right people?” Founded... View Details

February 2013
Case

Recorded Future: Analyzing Internet Ideas About What Comes Next

Recorded Future is a "big data" startup company that uses Internet data to make predictions about events, people, and entities. The company primarily serves government intelligence agencies, but has some private sector clients and is considering taking on more. The... View Details

Keywords: Big Data; Analytics; Internet; Analytics and Data Science; Internet and the Web; Entrepreneurship; Forecasting and Prediction; Business Startups; Information Technology Industry

January 2021 (Revised March 2021)
Case

THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)

By: Jill Avery, Ayelet Israeli and Emma von Maur

THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on... View Details

Keywords: Data; Data Analytics; Artificial Intelligence; AI; AI Algorithms; AI Creativity; Fashion; Retail; Retail Analytics; E-Commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Preference Prediction; Predictive Analytics; App Development; ＂Marketing Analytics＂; Advertising; Mobile App; Mobile Marketing; Apparel; Online Advertising; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Analytics and Data Science; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Digital Marketing; Internet and the Web; Mobile and Wireless Technology; AI and Machine Learning; E-commerce; Digital Platforms; Fashion Industry; Retail Industry; Apparel and Accessories Industry; Consumer Products Industry; United States

February 2017 (Revised June 2017)
Case

ExxonMobil: Business as Usual? (A)

By: George Serafeim, Shiva Rajgopal and David Freiberg

Climate change was becoming an important societal and business issue as more governments were introducing climate change related regulations and investors became increasibly worried about stranded assets within oil and gas firms. In September 2016, the U.S. Securities... View Details

Keywords: Oil ＆ Gas; Oil Prices; Oil Companies; Asset Impairment; Predictive Analytics; Sustainability; Environmental Impact; Innovation; Disclosure; Accounting; Valuation; Climate Change; Renewable Energy; Environmental Sustainability; Financial Reporting; Energy Industry

February 2017 (Revised June 2017)
Supplement

ExxonMobil: Business as Usual? (B)

By: George Serafeim, Shiva Rajgopal and David Freiberg

The case presents ExxonMobil's response to growing pressure to disclose how climate change will impact their business. This includes multiple asset impairments and losing a proxy vote to shareholders to increase climate change related reporting. Supplements the (B)... View Details

Keywords: Oil ＆ Gas; Oil Prices; Oil Companies; Asset Impairment; Predictive Analytics; Sustainability; Environmental Impact; Innovation; Disclosure; Accounting; Valuation; Energy Sources; Ethics; Corporate Disclosure; Governance Compliance; Climate Change; Financial Reporting; Energy Industry; United States

Teaching Interest

Overview

Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details

Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption

August 2018 (Revised September 2018)
Supplement

LendingClub (C): Gradient Boosting & Payoff Matrix

By: Srikant M. Datar and Caitlin N. Bowler

This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default... View Details

Keywords: Data Analytics; Data Science; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction

August 2015 (Revised January 2017)
Technical Note

From Correlation to Causation

By: Feng Zhu and Karim R. Lakhani

To make sound business decisions, managers must be comfortable with the concepts of correlation and causation. This background note provides an overview of correlation and causation using examples and explains why the former does not imply the latter. It also describes... View Details

Keywords: Statistics; Regression; Data Analytics; Decisions; Forecasting and Prediction; Judgments

Research Summary

Overview

By: Kris Johnson Ferreira

Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details

Keywords: E-commerce; Analytics; Revenue Management; Pricing; Assortment Planning; Field Experiments; Operations; Supply Chain; Supply Chain Management; Retail Industry

May 2021 (Revised February 2024)
Teaching Note

THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)

By: Ayelet Israeli and Jill Avery

THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on... View Details

Keywords: Data; Data Analytics; Artificial Intelligence; AI; AI Algorithms; AI Creativity; Fashion; Retail; Retail Analytics; E-Commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Predictive Analytics; App Development; ＂Marketing Analytics＂; Advertising; Mobile App; Mobile Marketing; Apparel; Online Advertising; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Analytics and Data Science; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Digital Marketing; Internet and the Web; Mobile and Wireless Technology; AI and Machine Learning; E-commerce; Digital Platforms; Fashion Industry; Retail Industry; Apparel and Accessories Industry; Consumer Products Industry; United States

August 2018 (Revised September 2018)
Supplement

LendingClub (B): Decision Trees & Random Forests

By: Srikant M. Datar and Caitlin N. Bowler

This case builds directly on the LendingClub (A) case. In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan.
... View Details

Keywords: Data Science; Data Analytics; Decision Trees; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction

22 Dec 2015
News

Algorithms Need Managers, Too

October 2022 (Revised December 2022)
Case

SMART: AI and Machine Learning for Wildlife Conservation

By: Brian Trelstad and Bonnie Yining Cao

Spatial Monitoring and Reporting Tool (SMART), a set of software and analytical tools designed for the purpose of wildlife conservation, had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching and contributing to wildlife... View Details

Keywords: Business and Government Relations; Emerging Markets; Technology Adoption; Strategy; Management; Ethics; Social Enterprise; AI and Machine Learning; Analytics and Data Science; Natural Environment; Technology Industry; Cambodia; United States; Africa

14 Mar 2023
Cold Call Podcast

Can AI and Machine Learning Help Park Rangers Prevent Poaching?

Keywords: Re: Brian L. Trelstad; Computer; Information Technology; Technology

October 2017 (Revised April 2018)
Case

Improving Worker Safety in the Era of Machine Learning (A)

By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson

Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I... View Details

Keywords: Machine Learning; Policy Implementation; Empirical Research; Inspection; Occupational Safety; Occupational Health; Regulation; Analysis; Forecasting and Prediction; Policy; Operations; Supply Chain Management; Safety; Manufacturing Industry; Construction Industry; United States

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