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• Technical Note | HBS Case Collection | August 2018 (Revised August 2019)

  A Note on Compensation

  Ethan Bernstein and Michael Norris

  This note provides an overview of the important terms, concepts, and frameworks that a manager should know about compensation—whether it be their own or that of an employee. Because compensation in practice is fraught with pitfalls, this note presents an overview of the various aspects of compensation of which any general manager should be aware to anticipate and avoid missteps. It was written for the second-year MBA course "Managing Human Capital" at HBS and would fit well in any Human Resource Management course.

  Keywords: compensation design; benefits; Perks; Variable Compensation; Compensation and Benefits; Executive Compensation; Stock Options; Profit Sharing; Job Design and Levels; Labor Unions; Wages; United States;

  Citation:

  Bernstein, Ethan, and Michael Norris. "A Note on Compensation." Harvard Business School Technical Note 419-020, August 2018. (Revised August 2019.)  View Details

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• Case | HBS Case Collection | August 2019

  Lemonade: Disrupting Insurance with Instant Everything, Killer Prices, and a Big Heart

  Elie Ofek and Danielle Golan

  Launching its first products in the fall of 2016 in New York, insurtech startup Lemonade was on a mission to disrupt the insurance market by using AI and behavioral economics principles. The company offered renters, homeowners and condo insurance and mainly targeted millennial, tech-savvy individuals with relatively few assets— customers who were less attractive to traditional insurers who had costly, labor-intensive, operations. By offering a delightful, bot-driven consumer experience, embracing a model that removed any incentive to deny claims and that let customers pick causes to donate leftover money to, building a loveable brand, and employing creative digital advertising – the company was able to grow at an impressive pace. By the end of 2018, Lemonade was active in 24 U.S. states, had insured 425,000 customers and was expecting sales (gross premiums) of over $57 million.
  In an effort to sustain the spectacular growth momentum, the co-founders sought to pursue ambitious plans in 2019, including the launch of new insurance product lines as well as international expansion. But limited resources and scarce management bandwidth suggested that taking on both moves simultaneously could be highly risky. In deciding which path to take, the leadership team had to consider whether it was more important at this stage to scale globally with the current product lineup or to cover a breadth of insurance categories and stay in the U.S. for now. Assessing the relevance of the business model and the role of AI for each of these moves, understanding the nature of consumers in different markets, and foreseeing competitive pressures from both incumbents and startups were key in making the right call.

  Keywords: Business Startups; Insurance; Technological Innovation; Business Model; Disruption; Brands and Branding; Growth and Development Strategy; Global Strategy; Decision Making; Insurance Industry; Technology Industry;

  Citation:

  Ofek, Elie, and Danielle Golan. "Lemonade: Disrupting Insurance with Instant Everything, Killer Prices, and a Big Heart." Harvard Business School Case 520-020, August 2019.  View Details

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• Article | Management Science | August 2019

  When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation

  Yicheng Song, Nachiketa Sahoo and Elie Ofek

  Sometimes we desire change, a break from the same, or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However, current diversification strategies operate under a one-shot paradigm without considering the evolution of preferences resulting from recent consumption. Therefore, such methods often sacrifice accuracy. In the context of online media, we show that by recognizing that consumption in a session is the result of a sequence of utility-maximizing selections from various categories, one can increase recommendation accuracy by dynamically tailoring the diversity of suggested items to the diversity sought by the consumer. Our approach is based on a multicategory utility model that captures a consumer’s preference for different categories of content, how quickly the consumer satiates with one category and wishes to substitute it with another, and how the consumer trades off costly search efforts with selecting from a recommended list to discover new content. Taken together, these three elements allow us to characterize how an individual selects a diverse set of items to consume over the course of a session and how likely the individual is to click on recommended content. We estimate the model using a clickstream data set from a large media outlet and apply it to determine the most relevant content to recommend at different stages of an online session. We find that our approach generates recommendations that are on average about 10% more accurate than optimized alternatives and about 25% more accurate than those diversified using existing diversification strategies. Moreover, the proposed method recommends content with diversity that more closely matches the diversity sought by readers, exhibiting lower concentration–diversification bias than other personalized recommender systems. Using a policy simulation, we estimate that recommending content using the proposed approach would result in visitors reading 23% additional articles at the studied website and deriving 35% higher utility. This could lead to immediate gains in revenue for the publisher and longer-term improvements in customer satisfaction and retention at the site.

  Citation:

  Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019).  View Details

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