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Publications

Publications

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  • All HBS Web  (969)
    • News  (131)
    • Research  (716)
    • Events  (8)
    • Multimedia  (4)
  • Faculty Publications  (547)

Show Results For

  • All HBS Web  (969)
    • News  (131)
    • Research  (716)
    • Events  (8)
    • Multimedia  (4)
  • Faculty Publications  (547)
← Page 8 of 969 Results →
  • 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
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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.
  • October 2018
  • Case

BreezoMeter: Making Air Pollution Data Actionable

By: Frank V. Cespedes, Allison M. Ciechanover and Margot Eiran
The case focuses on an Israeli startup that provides actionable air pollution data and forecasts. The company has over 50 enterprise customers and its tool reached a million people daily in 67 countries. The co-founders wrestle with which markets and customers to focus... View Details
Keywords: Startups; Entrepreneurship; Business Startups; Pollutants; Analytics and Data Science; Sales; Marketing; Decision Choices and Conditions; Technology Industry; Israel; United States
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Cespedes, Frank V., Allison M. Ciechanover, and Margot Eiran. "BreezoMeter: Making Air Pollution Data Actionable." Harvard Business School Case 819-058, October 2018.
  • 2010
  • Book

The New Science of Retailing: How Analytics Are Transforming the Supply Chain and Improving Performance

By: Marshall Fisher and Ananth Raman
Retailers today are drowning in data but lacking in insight: They have huge volumes of information at their disposal. But they're unsure of how to sort through it and use it to make smart decisions. The result? They're struggling with profit-sapping supply chain... View Details
Keywords: Profit; Knowledge Use and Leverage; Logistics; Supply Chain Management; Mathematical Methods; Retail Industry
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Fisher, Marshall, and Ananth Raman. The New Science of Retailing: How Analytics Are Transforming the Supply Chain and Improving Performance. Harvard Business Press, 2010.
  • Winter 2016
  • Article

Analytics for an Online Retailer: Demand Forecasting and Price Optimization

By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi
We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time... View Details
Keywords: Internet and the Web; Price; Forecasting and Prediction; Revenue; Sales; Retail Industry
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Ferreira, Kris J., Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization." Manufacturing & Service Operations Management 18, no. 1 (Winter 2016): 69–88.
  • Research Summary

Overview

By: Ayelet Israeli
Professor Israeli utilizes econometric methods and field experiments to study data driven decision making in marketing context. Her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data... View Details
Keywords: Channel Management; Pricing; Pricing Policies; Online Marketing; E-commerce; Analytics; Econometrics; Field Experiments; Data Analytics; Artificial Intelligence; Value Of Data
  • July 2023
  • Case

HealthVerity: Real World Data and Evidence

By: Satish Tadikonda
Andrew Kress (CEO and founder) and his team had built a promising marketplace business at HealthVerity serving its core market in healthcare, with a focus on pharmaceutical R&D and services. Thus far, HealthVerity’s products had been unique to the pharma and pharma... View Details
Keywords: Growth and Development Strategy; Market Entry and Exit; Product Marketing
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Tadikonda, Satish. "HealthVerity: Real World Data and Evidence." Harvard Business School Case 824-019, July 2023.
  • September 2019
  • Case

Starling Trust Sciences: Measuring Trust in Organizations

By: Aiyesha Dey, Jonas Heese and James Weber
Stephen Scott needed to decide whether to keep his behavioral analytics startup in the people analytics sector or shift his company into the RegTech sector. Starling had develop technology that enabled its customers to anticipate and shape the behavior of their... View Details
Keywords: Behavioral Analytics; Financial Institutions; Banks and Banking; Entrepreneurship; Strategy; Banking Industry; Consulting Industry; Information Technology Industry; United States; United Kingdom
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Dey, Aiyesha, Jonas Heese, and James Weber. "Starling Trust Sciences: Measuring Trust in Organizations." Harvard Business School Case 120-006, September 2019.
  • 2024
  • Case

EPCorp: What Story Does the Data Tell?

By: Jacob M. Cook
In EPCorp: What Story Does the Data Tell?, the Quick Case begins with Shivani Bahl researching problems with her company's website so that she can begin to analyze which option would help EPCorp most: selling all its products on Amazon or improving its own data... View Details
Keywords: Distribution Channels; E-commerce; Analytics and Data Science; Decision Making
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Cook, Jacob M. "EPCorp: What Story Does the Data Tell?" Harvard Business Publishing Case, 2024.
  • 01 Mar 2018
  • News

Democratizing Data to Favor Farmers

as much for the same seeds. That knowledge would help farmers haggle with dealers, but the real insight would come from analytics that go beyond consolidating seed prices to measuring a seed variety’s potential success. The most important... View Details
Keywords: Sasha Issenberg
  • July 2021
  • Article

Big Data for Social Benefits: Innovation as a Mediator of the Relationship between Big Data and Corporate Social Performance

By: Goran Calic and Maryam Ghasemaghaei
Over the last decade, the use big data in firms has seen a rapid increase. Whilst scholars have begun to unpack the relationship between big data utilisation and financial performance, significant uncertainty exists about the ethical uses of this new asset. Whether... View Details
Keywords: Analytics and Data Science; AI and Machine Learning; Performance Improvement; Organizational Change and Adaptation
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Calic, Goran, and Maryam Ghasemaghaei. "Big Data for Social Benefits: Innovation as a Mediator of the Relationship between Big Data and Corporate Social Performance." Journal of Business Research 131 (July 2021): 391–401.
  • October 2017 (Revised November 2017)
  • Case

NYC311

By: Constantine E. Kontokosta, Mitchell Weiss, Christine Snively and Sarah Gulick
Joe Morrisroe, executive director for NYC311, had some gut instincts but no definitive answer to the question he was just asked by one of the mayor’s deputies: “Are some communities being underserved by 311? How do we know we are hearing from the right people?” Founded... View Details
Keywords: New York City; NYC; 311; NYC311; Big Data; Equal Access; Bias; Data Analysis; Public Entrepreneurship; Urban Informatics; Predictive Analytics; Chief Data Officer; Data Analytics; Cities; City Leadership; Analytics and Data Science; Analysis; Prejudice and Bias; Entrepreneurship; Public Sector; City; Public Administration Industry; New York (city, NY)
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Kontokosta, Constantine E., Mitchell Weiss, Christine Snively, and Sarah Gulick. "NYC311." Harvard Business School Case 818-056, October 2017. (Revised November 2017.)
  • Article

Algorithms Need Managers, Too

By: Michael Luca, Jon Kleinberg and Sendhil Mullainathan
Algorithms are powerful predictive tools, but they can run amok when not applied properly. Consider what often happens with social media sites. Today many use algorithms to decide which ads and links to show users. But when these algorithms focus too narrowly on... View Details
Keywords: Machine Learning; Algorithms; Predictive Analytics; Management; Big Data; Analytics and Data Science
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Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101.
  • 08 Jan 2018
  • News

HBS-Born Startup Arms Doctors with Analytics to Provide Better Care

Keywords: Ambulatory Health Care Services; Health, Social Assistance
  • 2021
  • Working Paper

The Value of Descriptive Analytics: Evidence from Online Retailers

By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Retail Industry
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Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Harvard Business School Working Paper, No. 21-067, November 2020. (Revised December 2021. Accepted at Marketing Science.)
  • August 2020 (Revised September 2020)
  • Technical Note

Assessing Prediction Accuracy of Machine Learning Models

By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
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Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
  • Teaching Interest

Overview

By: John A. Deighton
I teach about the ecosystem of big data, the role of data in advertising and creative industries, and customer management and personal privacy in an era of individual addressability. View Details
Keywords: Digital Marketing; Database Marketing; Social Media; Data Analytics; Information; Advertising; Marketing; Media; Technology; Consumer Products Industry; Entertainment and Recreation Industry; Information Technology Industry; Publishing Industry; Media and Broadcasting Industry
  • November–December 2022
  • Article

The Value of Descriptive Analytics: Evidence from Online Retailers

By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Marketing; Retail Industry
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Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Marketing Science 41, no. 6 (November–December 2022): 1074–1096.
  • July 2013
  • Case

Sample6: Innovating to Make Food Safer

By: Robert F. Higgins and Kirsten Kester
Tim Curran, CEO of Sample6, a start-up biotechnology company developing a novel food safety diagnostics platform, must decide how to partner with food industry players. How can he best convince leaders in this mature industry to adopt a new technology and improve food... View Details
Keywords: Data Analytics; Food Safety; Biotechnology; Nutrition; Entrepreneurship; Product; Partners and Partnerships; Food; Technological Innovation; Business Startups; Governing Rules, Regulations, and Reforms; Product Development; Agribusiness; Information Technology; Globalization; Performance Improvement; Safety; Technology Adoption; Agriculture and Agribusiness Industry; Food and Beverage Industry; Biotechnology Industry; Information Industry; United States; Boston; Massachusetts
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Higgins, Robert F., and Kirsten Kester. "Sample6: Innovating to Make Food Safer." Harvard Business School Case 814-014, July 2013.
  • 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
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Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.
  • 15 May 2017
  • Sharpening Your Skills

The Promises and Limitations of Big Data

Source: peterhowell Although many people claim we have entered the era of big data, research firms tell us that most collected information is never used. It sits uncleaned, unanalyzed, unused in databases.  But when data View Details
Keywords: by Sean Silverthorne; Financial Services; Utilities; Public Administration; Health
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