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- All HBS Web
(62)
- Faculty Publications (8)
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- 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
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Programs; Consumer Behavior; Analysis
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
- September 2019
- Case
Netflix: A Creative Approach to Culture and Agility
By: Ranjay Gulati, Allison Ciechanover and Jeff Huizinga
By 2018, Netflix had been credited for revolutionizing how viewers consumed entertainment—shifting from ad-fueled linear network programming to a highly personalized, on-demand, all-you-can-consume, ad-free model. The company was riding a long wave of revenue and... View Details
Keywords: Digital Technologies; Streaming; Video On Demand; International Expansion; Leadership; Information Technology; Entrepreneurship; Innovation and Management; Innovation Strategy; Leadership Style; Management Style; Organizational Culture; Entertainment; Media; Change Management; Expansion; Technology Industry; United States
Gulati, Ranjay, Allison Ciechanover, and Jeff Huizinga. "Netflix: A Creative Approach to Culture and Agility." Harvard Business School Case 420-055, September 2019.
- 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
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups.... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- May 2016
- Article
Matching with Slot-Specific Priorities: Theory
By: Scott Duke Kominers and Tayfun Sönmez
We introduce a two-sided, many-to-one matching with contracts model in which agents with unit demand match to branches that may have multiple slots available to accept contracts. Each slot has its own linear priority order over contracts; a branch chooses contracts by... View Details
Keywords: Matching With Contracts; Stability; Strategy-proofness; School Choice; Affirmative Action; Airline Seat Upgrades; Contracts; Market Design; Marketplace Matching; Balance and Stability
Kominers, Scott Duke, and Tayfun Sönmez. "Matching with Slot-Specific Priorities: Theory." Theoretical Economics 11, no. 2 (May 2016): 683–710.
- July 2012
- Article
Discrete Choice Cannot Generate Demand That Is Additively Separable in Own Price
By: Sonia Jaffe and Scott Duke Kominers
We show that in a unit demand discrete choice framework with at least three goods, demand cannot be additively separable in own price. This result sharpens the analogous result of Jaffe and Weyl (2010) in the case of linear demand and has implications for testing of... View Details
Keywords: Discrete Choice; Unit Demand; Separable Demand; Linear Demand; Demand and Consumers; Market Design; Mathematical Methods; Economics
Jaffe, Sonia, and Scott Duke Kominers. "Discrete Choice Cannot Generate Demand That Is Additively Separable in Own Price." Economics Letters 116, no. 1 (July 2012): 129–132.
- 2009
- Article
Modeling Expert Opinions on Food Healthfulness: A Nutrition Metric
By: Jolie M. Martin, John Beshears, Katherine L. Milkman, Max H. Bazerman and Lisa Sutherland
Research over the last several decades indicates the failure of existing nutritional labels to substantially improve the healthiness of consumers' food and beverage choices. The difficulty for policy-makers is to encapsulate a wide body of scientific knowledge in a... View Details
Keywords: Judgments; Food; Nutrition; Labels; Knowledge Use and Leverage; Demand and Consumers; Measurement and Metrics; Mathematical Methods
Martin, Jolie M., John Beshears, Katherine L. Milkman, Max H. Bazerman, and Lisa Sutherland. "Modeling Expert Opinions on Food Healthfulness: A Nutrition Metric." Journal of the American Dietetic Association 109, no. 6 (June 2009): 1088–1091.
- 1980
- Article
Consumer Impulse Purchase and Credit Card Usage: An Empirical Examination Using the Log Linear Model
By: Rohit Deshpandé and S. Krishnan
Most of the work in impulse purchase behavior has investigated the association of socioeconomic variables and unplanned purchases with equivocal results. This paper examines the interrelationship between impulse purchases, credit card usage, cost of items bought, and... View Details