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2016
Working Paper

The Impact of Supplier Inventory Service Level on Retailer Demand

By: Nathan Craig, Nicole DeHoratius and Ananth Raman

To set inventory service levels, suppliers must understand how changes in inventory service level affect demand. We build on prior research, which uses analytical models and laboratory experiments to study the impact of a supplier's service level on demand from... View Details

Keywords: Customer Satisfaction; Forecasting and Prediction; Learning; Consumer Behavior; Service Delivery; Performance Expectations; Apparel and Accessories Industry; Service Industry

March–April 2023
Article

Market Segmentation Trees

By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis

Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details

Keywords: Decision Trees; Computational Advertising; Market Segmentation; Analytics and Data Science; E-commerce; Consumer Behavior; Marketplace Matching; Marketing Channels; Digital Marketing

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April–May 2024
Article

Gone with the Big Data: Institutional Lender Demand for Private Information

By: Jung Koo Kang

I explore whether big-data sources can crowd out the value of private information acquired through lending relationships. Institutional lenders have been shown to exploit their access to borrowers’ private information by trading on it in financial markets. As a shock... View Details

Keywords: Analytics and Data Science; Borrowing and Debt; Financial Markets; Value; Knowledge Dissemination; Financing and Loans

July – August 2011
Article

Foundations of Organizational Trust: What Matters to Different Stakeholders?

By: Michael Pirson and Deepak Malhotra

Prior research on organizational trust has not rigorously examined the context specificity of trust nor distinguished between the potentially varying dimensions along which different stakeholders base their trust. As a result, dominant conceptualizations of... View Details

Keywords: Trust; Competency and Skills; Forecasting and Prediction; Ethics; Framework; Analytics and Data Science; Surveys; Organizations; Business and Stakeholder Relations; Identity; Perspective

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January 2025
Case

AI Meets VC: The Data-Driven Revolution at Quantum Light Capital

By: Lauren Cohen, Grace Headinger and Sophia Pan

Ilya Kondrashov, CEO of Quantum Light Capital, was driven to harness AI for identifying high-potential scale-ups. Collaborating with Nik Storonsky, founder of Revolut, the duo observed that most venture capital (VC) decisions were heavily influenced by emotion, with... View Details

January 2018
Article

Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life

By: Edward L. Glaeser, Scott Duke Kominers, Michael Luca and Nikhil Naik

New, "big" data sources allow measurement of city characteristics and outcome variables at higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for... View Details

Keywords: Analytics and Data Science; Urban Scope; City

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2017
Working Paper

Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity

By: Edward L. Glaeser, Hyunjin Kim and Michael Luca

Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of economic activity at the local level. However, these statistics typically appear only... View Details

Keywords: Economy; Analytics and Data Science; Local Range; Social and Collaborative Networks

June 2023
Article

How New Ideas Diffuse in Science

By: Mengjie Cheng, Daniel Scott Smith, Xiang Ren, Hancheng Cao, Sanne Smith and Daniel A. McFarland

What conditions help new ideas spread? Can knowledge entrepreneurs’ position and develop new ideas in ways that help them take off? Most innovation research focuses on products and their reference. That focus ignores the ideas themselves and the broader ideational... View Details

Keywords: Innovation Adoption; Natural Language Processing; Knowledge; Science; Innovation and Invention; Knowledge Sharing; Analytics and Data Science

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09 Jan 2024
In Practice

Harnessing AI: What Businesses Need to Know in ChatGPT’s Second Year

crowdsource diverse viewpoints and innovative solutions to their most complex solutions. Take the Netflix challenge. In 2006, Netflix launched an open competition for the best collaborative filtering algorithm to predict user ratings for... View Details

Keywords: by Rachel Layne; Information Technology

July 2023
Article

Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations

By: Dawson Beutler, Alex Billias, Sam Holt, Josh Lerner and TzuHwan Seet

In 2001, Dean Takahashi and Seth Alexander of the Yale University Investments Office developed a deterministic model for estimating future cash flows and valuations for the Yale endowment’s private equity portfolio. Their model, which is simple and intuitive, is still... View Details

Keywords: Forecasting and Prediction; Investment Portfolio; Analytics and Data Science

2012
Working Paper

~Why Do We Redistribute so Much but Tag so Little? Normative Diversity, Equal Sacrifice and Optimal Taxation

By: Matthew Weinzierl

Tagging is a free lunch in conventional optimal tax theory because it eases the classic tradeoff between efficiency and equality. But tagging is used in only limited ways in tax policy. I propose one explanation: conventional optimal tax theory has yet to capture the... View Details

Keywords: Forecasting and Prediction; Cost; Framework; Policy; Taxation; Analytics and Data Science; Performance Efficiency; United States

Forthcoming
Article

Slowly Varying Regression Under Sparsity

By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami

We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details

Keywords: Mathematical Methods; Analytics and Data Science

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26 Oct 2010
First Look

First Look: October 26, 2010

set of analytic and practical challenges arises, which this article explores via three cases: 1) a cross-border, large-dollar complex sales effort requiring interlocking financial, political, and organizational negotiations among dozens... View Details

Keywords: Sean Silverthorne

26 May 2015
First Look

First Look: May 26

successfully unified all analytics talent and resources into one group over a three-year period. Rapid increases in computing power and decreases in data storage costs had enabled DA2's data architects to build View Details

Keywords: Sean Silverthorne

February 2021
Case

Digital Manufacturing at Amgen

By: Shane Greenstein, Kyle R. Myers and Sarah Mehta

This case discusses efforts made by biotechnology (biotech) company Amgen to introduce digital technologies into its manufacturing processes. Doing so is complicated by the fact that the process for manufacturing biologics—or therapeutics made from living cells—is... View Details

18 Apr 2017
First Look

First Look at New Ideas, April 18

interest rates than predicted by the standard expectations hypothesis. We find that, since 2000, such high-frequency "excess sensitivity" remains evident in U.S. data and has, if anything, grown stronger. By contrast, the positive... View Details

Keywords: by Sean Silverthorne

2023
Working Paper

Feature Importance Disparities for Data Bias Investigations

By: Peter W. Chang, Leor Fishman and Seth Neel

It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details

Keywords: AI and Machine Learning; Analytics and Data Science; Prejudice and Bias

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

Keywords: Customer Journey; Privacy; Consumer Behavior; Analytics and Data Science; AI and Machine Learning; Customer Focus and Relationships

2025
Working Paper

Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli

Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships between customer states, company actions, and long-term value. However, its practical implementation often faces significant challenges.... View Details

Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science

23 Oct 2018
First Look

New Research and Ideas, October 23, 2018

allow testing for and predicting firm-specific coefficients, thereby distinguishing between effects that have a significant mean versus significant variance. RCMs may also be used to explore the sources of firm heterogeneous effects. We... View Details

Keywords: Dina Gerdeman

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