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    • All HBS Web  (386)
      • Faculty Publications  (96)

      Algorithmic Data Remove Algorithmic Data →

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      • May–June 2023
      • Article

      Analytics for Marketers: When to Rely on Algorithms and When to Trust Your Gut

      By: Fabrizio Fantini and Das Narayandas
      Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, and supply-chain operations, which can lead to a sustainable competitive advantage. But as more data becomes available and advanced analytics are...  View Details
      Keywords: Analytics and Data Science; Decision Making
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      Fantini, Fabrizio, and Das Narayandas. "Analytics for Marketers: When to Rely on Algorithms and When to Trust Your Gut." Harvard Business Review 101, no. 3 (May–June 2023): 82–91.
      • March 2023
      • Teaching Note

      VideaHealth: Building the AI Factory

      By: Karim R. Lakhani
      Teaching Note for HBS Case No. 621-021. The case “VideaHealth: Building the AI Factory” examines the creation of dental startup VideaHealth (Videa) and the development of its artificial intelligence (AI)-led business strategy through the eyes of founder and CEO Florian...  View Details
      Keywords: AI and Machine Learning; Applications and Software; Business Model; Marketing Strategy; Product Development; Health Industry; Technology Industry
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      Lakhani, Karim R. "VideaHealth: Building the AI Factory." Harvard Business School Teaching Note 623-073, March 2023.
      • 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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      Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
      • 2022
      • Working Paper

      Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

      By: Kirk Bansak and Elisabeth Paulson
      This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment...  View Details
      Keywords: AI and Machine Learning; Refugees; Mathematical Methods
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      Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Harvard Business School Working Paper, No. 23-048, January 2022.
      • February 2023
      • Supplement

      Shanty Real Estate: Teaching Note Supplement

      By: Michael Luca
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      Luca, Michael. "Shanty Real Estate: Teaching Note Supplement." Harvard Business School Spreadsheet Supplement 923-715, February 2023.
      • Working Paper

      Group Fairness in Dynamic Refugee Assignment

      By: Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng
      Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic location within a host country to which the refugee or asylum seeker is...  View Details
      Keywords: Refugees; Geographic Location; Mathematical Methods; Employment; Fairness
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      Freund, Daniel, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt, and Wentao Weng. "Group Fairness in Dynamic Refugee Assignment." Harvard Business School Working Paper, No. 23-047, February 2023.
      • 2022
      • Working Paper

      Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions

      By: Caleb Kwon, Ananth Raman and Jorge Tamayo
      We empirically analyze how managerial overrides to a commercial algorithm that forecasts demand and schedules labor affect store performance. We analyze administrative data from a large grocery retailer that utilizes a commercial algorithm to forecast demand and...  View Details
      Keywords: Employees; Human Capital; Performance; Applications and Software; Management Skills; Management Practices and Processes; Retail Industry
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      Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, December 2022.
      • 2022
      • Working Paper

      Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence

      By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
      Even if algorithms make better predictions than humans on average, humans may sometimes have “private” information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by...  View Details
      Keywords: Cognitive Biases; Algorithm Transparency; Forecasting and Prediction; Behavior; AI and Machine Learning; Analytics and Data Science; Cognition and Thinking
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      Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, December 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 1

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Data-driven Decision-making; Decisions; Negotiation; Bids and Bidding; Valuation; Consumer Behavior; Real Estate Industry
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 1." Harvard Business School Exercise 923-016, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 2

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 2." Harvard Business School Exercise 923-017, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 3

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 3." Harvard Business School Exercise 923-018, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 1

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 1." Harvard Business School Exercise 923-019, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 2

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 2." Harvard Business School Exercise 923-020, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 3

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 3." Harvard Business School Exercise 923-021, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Updated Confidential Information for Homebuyer

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for Homebuyer." Harvard Business School Exercise 923-022, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Updated Confidential Information for iBuyer

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Measurement and Metrics; Market Timing; Decision Making
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for iBuyer." Harvard Business School Exercise 923-023, October 2022.
      • 2022
      • Working Paper

      Price Monitoring and Response: A Supply Side Characterization

      By: Ayelet Israeli and Eric Anderson
      With the growth of e-commerce and increasing price transparency, there has been tremendous interest in the adoption and competitive consequences of algorithmic pricing. A premise of algorithmic pricing is that firms monitor competitors' prices and then their pricing...  View Details
      Keywords: Algorithmic Pricing; Ecommerce; Price Monitoring; Price; Competition; Retail Industry
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      Israeli, Ayelet, and Eric Anderson. "Price Monitoring and Response: A Supply Side Characterization." Working Paper, October 2022.
      • September 2022 (Revised November 2022)
      • Teaching Note

      PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

      By: Ayelet Israeli
      Teaching Note for HBS Case No. 522-046.  View Details
      Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
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      Israeli, Ayelet. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Teaching Note 523-020, September 2022. (Revised November 2022.)
      • August 2022
      • Supplement

      Zalora: Data-Driven Pricing Recommendations

      By: Ayelet Israeli
      This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...  View Details
      Keywords: Pricing; Pricing Algorithms; Dynamic Pricing; Ecommerce; Pricing Strategy; Pricing And Revenue Management; Apparel; Singapore; Startup; Demand Estimation; Data Analysis; Data Analytics; Exercise; Price; Internet and the Web; Apparel and Accessories Industry; Retail Industry; Fashion Industry; Singapore
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      Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
      • 2022
      • Article

      Towards Robust Off-Policy Evaluation via Human Inputs

      By: Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez and Himabindu Lakkaraju
      Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes domains such as healthcare, where direct deployment is often infeasible, unethical, or expensive. When deployment environments are expected to undergo changes (that is, dataset...  View Details
      Keywords: Analytics and Data Science; Research
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      Singh, Harvineet, Shalmali Joshi, Finale Doshi-Velez, and Himabindu Lakkaraju. "Towards Robust Off-Policy Evaluation via Human Inputs." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 686–699.
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