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- October 2024
- Article
Canary Categories
By: Eric Anderson, Chaoqun Chen, Ayelet Israeli and Duncan Simester
Past customer spending in a category is generally a positive signal of future customer spending. We show that there exist “canary categories” for which the reverse is true. Purchases in these categories are a signal that customers are less likely to return to that... View Details
- 2023
- Working Paper
'De Gustibus' and Disputes about Reference Dependence
By: Thomas Graeber, Pol Campos-Mercade, Lorenz Goette, Alexandre Kellogg and Charles Sprenger
Existing tests of reference-dependent preferences assume universal loss aversion. This paper examines the implications of heterogeneity in gain-loss attitudes for such tests. In experiments on labor supply and exchange behavior we measure gain-loss attitudes and then... View Details
- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD... View Details
- July 2023
- Case
Crocs: Using Community-Centric Marketing to Make Ugly Iconic
By: Ayelet Israeli and Anne V. Wilson
In 2022, the Crocs Classic Clog was the best-selling item of clothing on Amazon, the brand was one of the fastest growing brands in the U.S., and global net revenue had increased to approximately $3.6 billion. By most accounts, Crocs had become the “it” shoe. Crocs... View Details
- April 2023
- Article
The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences
By: Armin Falk, Anke Becker, Thomas Dohmen, David B. Huffman and Uwe Sunde
Incentivized choice experiments are a key approach to measuring preferences in economics but are also costly. Survey measures are a low-cost alternative but can suffer from additional forms of measurement error due to their hypothetical nature. This paper seeks to... View Details
- July 2022
- Article
The Developmental Origins and Behavioral Consequences of Attributions for Inequality
By: Antonya Marie Gonzalez, Lucia Macchia and Ashley V. Whillans
Attributions, or lay explanations for inequality, have been linked to inequality-relevant behavior. In adults and children, attributing inequality to an individual rather than contextual or structural causes is linked to greater support for economic inequality and less... View Details
- Article
Extension Request Avoidance Predicts Greater Time Stress Among Women
By: Ashley V. Whillans, Jaewon Yoon, Aurora Turek and Grant E. Donnelly
In nine studies using archival data, surveys, and experiments, we identify a factor that predicts gender differences in time stress and burnout. Across academic and professional settings, women are less likely to ask for more time when working under adjustable... View Details
- October 2021
- Article
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
- Article
Birds of a Feather...Enforce Social Norms? Interactions Among Culture, Norms, and Strategy
By: Hongyi Li and Eric J. Van den Steen
Does culture eat strategy for breakfast? This paper investigates the interactions among corporate culture, norms, and strategy, in order to better understand this issue and related questions. It first shows, through microfoundations, how the forces that drive toward... View Details
- 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
- 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
- 2020
- Working Paper
Topic Preference Detection: A Novel Approach to Understand Perspective Taking in Conversation
By: Michael Yeomans and Alison Wood Brooks
Although most humans engage in conversations constantly throughout their lives, conversational mistakes are commonplace— interacting with others is difficult, and conversation re-quires quick, relentless perspective-taking and decision making. For example: during every... View Details
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
- Article
Maimonides' Ladder: States of Mutual Knowledge and the Perception of Charitability
By: Julian De Freitas, Peter DiScioli, Kyle A. Thomas and Steven Pinker
Why do people esteem anonymous charitable giving? We connect normative theories of charitability
(captured in Maimonides’ Ladder of Charity) with evolutionary theories of partner choice to test predictions on how attributions of charitability are affected by states of... View Details
- January 2019
- Article
Making Moves Matter: Experimental Evidence on Incentivizing Bureaucrats Through Performance-Based Postings
By: Adnan Q. Khan, Asim Ijaz Khwaja and Benjamin A. Olken
Bureaucracies often post staff to better or worse locations, ostensibly to provide incentives. Yet we know little about whether this works, with heterogeneity in preferences over postings impacting effectiveness. We propose a performance-ranked serial dictatorship... View Details
- 2018
- Working Paper
Channeled Attention and Stable Errors -- Previous Working Version
By: Tristan Gagnon-Bartsch, Matthew Rabin and Joshua Schwartzstein
A common critique of models of mistaken beliefs is that people should recognize their error after observations they thought were unlikely. This paper develops a framework for assessing when a given error is likely to be discovered, in the sense that the error-maker... View Details
- November 2017
- Teaching Note
Predicting Consumer Tastes with Big Data at Gap
By: Ayelet Israeli and Jill Avery
CEO Art Peck was eliminating his creative directors for The Gap, Old Navy, and Banana Republic brands and promoting a collective creative ecosystem fueled by the input of big data. Rather than relying on artistic vision, Peck wanted the company to use the mining of big... View Details
Keywords: Brands; Brand ＆ Product Management; Big Data; ＂Marketing Analytics＂; Consumer Behavior; Predictive Analytics; Forecasting; Preferences; Operation Management; Distribution Channels; Marketing; Marketing Channels; Marketing Strategy; Brands and Branding; Forecasting and Prediction; Data and Data Sets; Retail Industry; Fashion Industry; Apparel and Accessories Industry; United States; North America
- September 2017
- Article
The Belief in a Favorable Future
By: Todd Rogers, Don A. Moore and Michael I. Norton
People believe that future others’ preferences and beliefs will change to align with their own. People holding a particular view (e.g., support of President Trump) are more likely to believe that future others will share their view than to believe that future others... View Details
- May 2017 (Revised March 2018)
- Case
Predicting Consumer Tastes with Big Data at Gap
By: Ayelet Israeli and Jill Avery
CEO Art Peck was eliminating his creative directors for The Gap, Old Navy, and Banana Republic brands and promoting a collective creative ecosystem fueled by the input of big data. Rather than relying on artistic vision, Peck wanted the company to use the mining of big... View Details
Keywords: Retailing; Preference Elicitation; Big Data; Predictive Analytics; Artificial Intelligence; Fashion; Marketing; Marketing Strategy; Marketing Channels; Brands and Branding; Consumer Behavior; Demand and Consumers; Analytics and Data Science; Forecasting and Prediction; E-commerce; Apparel and Accessories Industry; Consumer Products Industry; Fashion Industry; Retail Industry; United States; Canada; North America
- August 2016
- Article
The Role of (Dis)similarity in (Mis)predicting Others' Preferences
By: Kate Barasz, Tami Kim and Leslie K. John
Consumers readily indicate liking options that appear dissimilar—for example, enjoying both rustic lake vacations and chic city vacations or liking both scholarly documentary films and action-packed thrillers. However, when predicting other consumers’ tastes for the... View Details