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Show Results For
- All HBS Web
(1,720)
- People (9)
- News (316)
- Research (1,050)
- Events (15)
- Multimedia (10)
- Faculty Publications (863)
- November 2019
- Article
How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework
By: Doug J. Chung, Byungyeon Kim and Byoung G. Park
This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details
Keywords: Nerlove-Arrow Framework; Stock-of-goodwill; Dynamic Panel Data; Serial Correlation; Instrumental Variables; Sales Effectiveness; Detailing; Analytics and Data Science; Sales; Analysis; Performance Effectiveness; Pharmaceutical Industry
Chung, Doug J., Byungyeon Kim, and Byoung G. Park. "How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework." Management Science 65, no. 11 (November 2019): 5197–5218.
- February 2011
- Supplement
Dataset for "Slots, Tables, and All That Jazz: Managing Customer Profitability at the MGM Grand Hotel" (CW)
By: Dennis Campbell and Francisco de Asis Martinez-Jerez
Datasets of gaming and hotel customers to perform analysis for the case. View Details
- 01 Mar 2013
- News
Faculty Books
Enterprise Analytics: Optimize Performance, Process, and Decisions through Big Data edited by Thomas Davenport (FT Press) This book, a collection of research papers from the International Institute for Analytics, addresses a wide variety of topics in managing business... View Details
- Career Coach
Lee Scott
Lee started her career in the nonprofit sector. She has spent the last few years transitioning into an impact investing role. She can provide advice on switching sectors and strategizing how to build out relevant skillsets. Work Experience: J.P. Morgan (MBA... View Details
- 2023
- Article
Experimental Evaluation of Individualized Treatment Rules
By: Kosuke Imai and Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
- September 2016 (Revised March 2017)
- Module Note
Strategy Execution Module 3: Using Information for Performance Measurement and Control
By: Robert Simons
This module reading explains how managers use information to control critical business processes and outcomes. The analysis begins by illustrating how managers use information to communicate goals and track performance. Then the focus turns to the choices that managers... View Details
Keywords: Management Control Systems; Implementing Strategy; Strategy Execution; Organization Process; Feedback Model; Innovation; Uses Of Information; Big Data; Benchmarking; Decision Making; Information; Performance Evaluation; Analytics and Data Science
Simons, Robert. "Strategy Execution Module 3: Using Information for Performance Measurement and Control." Harvard Business School Module Note 117-103, September 2016. (Revised March 2017.)
- October 2000 (Revised April 2003)
- Background Note
Project Finance Research, Data, and Information Sources
By: Benjamin C. Esty and Fuaad Qureshi
Documents the major sources of project finance research and data. It is to be a reference guide for MBA students writing for the elective curriculum course, Large-scale Investment, and for others interested in the field of project finance. View Details
Esty, Benjamin C., and Fuaad Qureshi. "Project Finance Research, Data, and Information Sources ." Harvard Business School Background Note 201-041, October 2000. (Revised April 2003.)
- August 2024
- Technical Note
Introduction to Data Analysis in Python
By: Michael Parzen and Jo Ellery
This note introduces Python as a tool for data science, including the Pandas library for data analysis. View Details
Keywords: Analytics and Data Science
Parzen, Michael, and Jo Ellery. "Introduction to Data Analysis in Python." Harvard Business School Technical Note 625-016, August 2024.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)
- 14 Dec 2015
- Research & Ideas
Deflategate and the Sustained Success of the New England Patriots
organizations involved. But Deflategate isn’t the only issue examined by the case. “What started essentially as an analytics exercise ended up as a much broader analysis of the data, the sport, the NFL, and how it’s organized and how it’s... View Details
- 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
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression Under Sparsity." Operations Research (forthcoming). (Pre-published online March 27, 2024.)
- 2024
- Working Paper
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python
By: Melissa Ouellet and Michael W. Toffel
This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in... View Details
Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods
Ouellet, Melissa, and Michael W. Toffel. "Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python." Harvard Business School Working Paper, No. 25-010, August 2024.
- May 8, 2020
- Article
Which Covid-19 Data Can You Trust?
By: Satchit Balsari, Caroline Buckee and Tarun Khanna
The COVID-19 pandemic has produced a tidal wave of data, but how much of it is any good? And as a layperson, how can you sort the good from the bad? The authors suggest a few strategies for dividing the useful data from the misleading: Beware of data that’s too broad... View Details
Balsari, Satchit, Caroline Buckee, and Tarun Khanna. "Which Covid-19 Data Can You Trust?" Harvard Business Review (website) (May 8, 2020).
- Article
Beyond Statistics: The Economic Content of Risk Scores
By: Liran Einav, Amy Finkelstein, Raymond Kluender and Paul Schrimpf
"Big data" and statistical techniques to score potential transactions have transformed insurance and credit markets. In this paper, we observe that these widely-used statistical scores summarize a much richer heterogeneity, and may be endogenous to the context in which... View Details
Einav, Liran, Amy Finkelstein, Raymond Kluender, and Paul Schrimpf. "Beyond Statistics: The Economic Content of Risk Scores." American Economic Journal: Applied Economics 8, no. 2 (April 2016): 195–224.
- February 25, 2016
- Article
The Hodgepodge Principle in U.S. Privacy Policy
By: John A. Deighton
Data, says Professor Lawrence Summers, is the new oil, "a hugely valuable asset essential to economic life." Personal data, the kind of data that invites thoughts of privacy, is a big part of that. The European Union saw this economic fuel source coming long ago and... View Details
Keywords: Data; Privacy; Technology; Big Data; Personal Data; Marketing; Information Technology; Analytics and Data Science
Deighton, John A. "The Hodgepodge Principle in U.S. Privacy Policy." Harvard Law and Policy Review Blog (March 2, 2016). http://harvardlpr.com/2016/03/02/the-hodgepodge-principle-in-us-privacy-policy/.
- Web
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optout.networkadvertising.org We may use third party analytics such as Google Analytics or similar analytics services. For information on how Google processes and collects... View Details
- 01 Sep 2010
- News
Faculty Books
The New Science of Retailing: How Analytics Are Transforming the Supply Chain and Improving Performance by Marshall Fisher and Ananth Raman (Harvard Business Press) Professor Raman and his coauthor explain how to use View Details
- 25 Aug 2017
- Blog Post
HBS Interns: Summer Takeovers
in technology at the Sephora Innovation Lab. Jenn researched the impacts of artificial intelligence on the retail industry and participated in idea hackathons to brainstorm creative new ways for the brand to innovate. Adam Behrens, View Details
Keywords: All Industries
- November 2016 (Revised April 2017)
- Case
Basecamp: Pricing
By: Frank Cespedes and Robb Fitzsimmons
A data analyst at Basecamp is evaluating the results of pricing research and its potential implications for the venture’s latest version of its project management software product. View Details
Keywords: Pricing; Entrepreneurial Management; Data Analysis; Marketing; Customer Acquisition; Customer Retention; Value Proposition; Sales Management; Product Management; Market Research; Life Time Value; Testing; Entrepreneurship; Analytics and Data Science; Customers; Value; Sales; Product Marketing; United States
Cespedes, Frank, and Robb Fitzsimmons. "Basecamp: Pricing." Harvard Business School Case 817-067, November 2016. (Revised April 2017.)
- December 1998
- Case
Origins of National Income Accounting
By: David A. Moss and Joseph P Gownder
Set in the Great Depression, this case explores the origins of national income accounting in the United States. Highlights Senator La Follette's 1932 proposal for the federal government to begin collecting national income statistics. View Details
Keywords: Accounting; Financial Crisis; Analytics and Data Science; Mathematical Methods; United States
Moss, David A., and Joseph P Gownder. "Origins of National Income Accounting." Harvard Business School Case 799-080, December 1998.