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    • Faculty Publications  (5)

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    • All HBS Web  (437)
      • Faculty Publications  (5)

      Deep Exponential Families Remove Deep Exponential Families →

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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
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      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 2020 (Revised June 2021)
      • Case

      Algramo

      By: Michael Chu, Monica Silva and Mariana Cal
      Founded in 2013 by José Manuel Moller in Chile, Algramo first became known for addressing the “poverty tax” (the surcharge paid by lower income families for staples sold in smaller sizes) through specially-designed dispensers in low-income neighborhood grocery stores...  View Details
      Keywords: Packaging-as-a-wallet; Plastic Waste; Business At The Base Of The Pyramid; Reusable Packaging; Alliances With FMCGs To Meet ESG Goals; Social Entrepreneurship; Environmental Sustainability; Strategy; Value Creation; Goals and Objectives; Business Model; Consumer Products Industry; Latin America; South America; Chile
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      Chu, Michael, Monica Silva, and Mariana Cal. "Algramo." Harvard Business School Case 321-079, September 2020. (Revised June 2021.)
      • 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
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      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.)
      • September 2014 (Revised February 2017)
      • Case

      Belk: Towards Exceptional Scheduling

      By: Ethan Bernstein, Saravanan Kesavan, Bradley Staats and Luke Hassall
      With 24,000 staff and over 300 stores, Belk Inc. sought to replace its entirely manual labor scheduling system with an automated software solution from Reflexis. Belk hoped the upgrade would simplify scheduling, reduce time employees spent in non-customer-facing roles,...  View Details
      Keywords: Retail; Scheduling; Local Autonomy; Automation; Metrics; Organizational Change; Human Resource Management; Process Improvement; Performance Measurement; Transparency; Southern United States; Retailing; Department Stores; System Outsourced Services; Employee Relationship Management; Selection and Staffing; Change Management; Governance Controls; Resource Allocation; Service Operations; Organizational Culture; Organizational Change and Adaptation; Performance Evaluation; Performance Improvement; Applications and Software; Family Business; Retail Industry; Technology Industry; United States
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      Bernstein, Ethan, Saravanan Kesavan, Bradley Staats, and Luke Hassall. "Belk: Towards Exceptional Scheduling." Harvard Business School Case 415-023, September 2014. (Revised February 2017.)
      • Article

      The Future of Economic, Business, and Social History

      By: G. Jones, Marco H.D. van Leeuwen and Stephen Broadberry
      Three leading scholars in the fields of business, economic, and social history review the current state of these disciplines and reflect on their future trajectory. Geoffrey Jones reviews the development of business history since its birth at the Harvard Business...  View Details
      Keywords: Economic History; Business History; History; Asia; Africa; Europe; Latin America; North and Central America
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      Jones, G., Marco H.D. van Leeuwen, and Stephen Broadberry. "The Future of Economic, Business, and Social History." Scandinavian Economic History Review 60, no. 3 (2012): 225–253.
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