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- January 1986 (Revised April 1987)
- Background Note
Models for Updating Demand Forecasts
Keywords:
Forecasting and Prediction
Schleifer, Arthur, Jr. "Models for Updating Demand Forecasts." Harvard Business School Background Note 186-180, January 1986. (Revised April 1987.)
- January 1983 (Revised September 1983)
- Case
E.T. Phone Home, Inc.: Forecasting Business Demand
By: John F. Cady and Frank V. Cespedes
Describes a process for forecasting market demand for an emerging technology--cellular radio. The student must critically evaluate the demand model and the market estimates, and modify them as appropriate in order to develop a marketing plan and budget.
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Keywords:
Budgets and Budgeting;
Forecasting and Prediction;
Marketing Strategy;
Demand and Consumers;
Business Processes;
Technology
Cady, John F., and Frank V. Cespedes. "E.T. Phone Home, Inc.: Forecasting Business Demand." Harvard Business School Case 583-121, January 1983. (Revised September 1983.)
- Forthcoming
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),...
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Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology (forthcoming). (Pre-published online November 9, 2023.)
- Forthcoming
- 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...
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Keywords:
Churn;
Churn Management;
Churn/retention;
Assortment Planning;
Retail;
Retailing;
Retailing Industry;
Preference Heterogeneity;
Assortment Optimization;
Customers;
Retention;
Consumer Behavior;
Forecasting and Prediction;
Retail Industry
Anderson, Eric, Chaoqun Chen, Ayelet Israeli, and Duncan Simester. "Canary Categories." Journal of Marketing Research (JMR) (forthcoming). (Pre-published online November 29, 2023.)
- Research Summary
Customer-Centricity as a Vehicle for Organic Growth
By: Ranjay Gulati
This body of work examines the mechanics of how firms grow profitably in commoditizing markets. Underlying the "customer-centricity" that many firms embrace today is a factor that will determine their success with this effort: enabling collaboration across...
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- Forthcoming
- Article
FinTech Lending and Cashless Payments
By: Pulak Ghosh, Boris Vallée and Yao Zeng
Borrower's use of cashless payments both improves their access to capital from FinTech lenders and predicts a lower probability of default. These relationships are stronger for cashless technologies providing more precise information, and for outflows. Cashless payment...
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- Forthcoming
- Article
Imagining the Future: Memory, Simulation and Beliefs
By: Pedro Bordalo, Giovanni Burro, Katherine B. Coffman, Nicola Gennaioli and Andrei Shleifer
How do people form beliefs about novel risks, with which they have little or no experience? Motivated by survey data on beliefs about Covid we collected in 2020, we build a model based on the psychology of selective memory. When a person thinks about an event,...
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Bordalo, Pedro, Giovanni Burro, Katherine B. Coffman, Nicola Gennaioli, and Andrei Shleifer. "Imagining the Future: Memory, Simulation and Beliefs." Review of Economic Studies (forthcoming).
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models.
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- Research Summary
Overview
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...
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- Research Summary
Selection, Reallocation, and Spillover: Identifying the Sources of Gains from Multinational Production (with Maggie Chen)
By: Laura Alfaro
Quantifying the gains from multinational production has been a vital topic of economic research. Positive productivity gains are often attributed to knowledge spillover from multinational to domestic firms. An alternative, less stressed explanation is firm selection... View Details
- Research Summary
Selective Attention and Learning
What do we notice, and how does this affect what we learn? Standard economic models of learning ignore memory by assuming that we remember everything. But there is growing recognition that memory is imperfect. Further, memory imperfections do not stem from limited... View Details
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