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• Case | HBS Case Collection | September 2018 (Revised December 2019)

  Zebra Medical Vision

  Shane Greenstein and Sarah Gulick

  An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making diagnoses. By July 2018, Zebra had developed seven algorithms to analyze scans for emphysema, liver density, compression fractures, bone density, brain bleeds, breast cancer, and a calcium score used to detect calcified plaque in coronary arteries. For each scan analyzed, Zebra charged hospitals $1. By 2018 Zebra found itself in a race with its competitors to perfect these algorithms, create software tools, distribute the tools to physician partners, and create a market. Zebra already had several partners in the U.S. and Europe who gave feedback on its development. Management had to answer the following questions: What should they do next at Zebra? Should they work on the accuracy of the already developed algorithms or continue to develop many new tools? If they chose to develop new products, which applications should they address? And how should they go to market?

  Keywords: radiology; Machine learning; X-ray; CT scan; medical technology; probability; FDA 510(k); diagnosis; Business Startups; Health Care and Treatment; Technology; Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel;

  Citation:

  Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)  View Details

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• Article | Telecommunications Policy | September 2019

  The Persistence of Broadband User Behavior: Implications for Universal Service and Competition Policy

  Andre Boik, Shane Greenstein and Jeffrey Prince

  In several markets, firms compete not for consumer expenditure but consumer attention. We examine user priorities over the allocation of their time, and interpret that behavior in light of salient tensions in policy discussions over universal service, data caps, and related policy topics, such as merger analysis. Specifically, we use extensive microdata on user online choice to characterize the demand for the services offered online, which drives a household’s supply of attention. Our data cover a period of time that saw the introduction of many new and notable sites and new devices on which to access them. In our analysis, we assess “how” households supply their attention along various dimensions, such as their concentration of attention across the universe of sites and the amount of attention expenditure per domain visit. Remarkably, we find no change in “how” households allocated their attention despite drastically changing where they allocated it. Moreover, conditional on total attention expenditure, demographics entirely fail to predict our key measures of attention allocation decisions. We highlight several important implications, for policy and beyond, stemming from the persistence and demographic orthogonality of our novel attention measures.

  Keywords: broadband service; attention allocation; Consumer Behavior; Household; Internet; Competition; Policy;

  Citation:

  Boik, Andre, Shane Greenstein, and Jeffrey Prince. "The Persistence of Broadband User Behavior: Implications for Universal Service and Competition Policy." Telecommunications Policy 43, no. 8 (September 2019).  View Details

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• Case | HBS Case Collection | August 2019

  Twiggle: E-commerce with Semantic Search

  Shane Greenstein and Danielle Golan

  Four years after being founded, in 2014, by former Google executives Amir Konigsberg (CEO) and Adi Avidor (CTO), Twiggle had developed a search enhancement that plugged into an online merchant’s existing framework. The company utilized advanced structuring and linguistic tools to build search technology that understood shopper intent and matched it with the right products. Konigsberg and Avidor believed e-tailers were losing enormous revenue due to poor search results. The team built a set of proprietary tools that created a human-like understanding of customer queries in the e-commerce search experience, using natural language processing that translated text into meaning. The team also constructed a semantic model of the e-commerce world for three product domains: fashion, home and electronics. The resulting “ontology” was a set of concepts and categories in a subject area that showed their properties and the relations between them. Twiggle’s tools could also unlock significant online sales revenue by increasing click-through and add-to-cart rates, ultimately improving sales conversions. So far, the company had secured deals with more than half a dozen large e-commerce retailers, and could improve search results in three product categories: fashion, home, and electronics.
  The company initially targeted large direct-to-consumer e-commerce players. Yet the cofounders encountered challenges pursuing this type of customer. The customer acquisition process included lengthy, intensive face-to-face sales cycles, expensive and technologically complex proof-of-concept testing, and requests for customization. Konigsberg and Avidor wondered if they should expand their customer focus to target a wider set of smaller e-tailers that still had significant volumes of online search queries, but whose search quality tended to be less robust and might be easier to improve. They also contemplated a variety of growth opportunities.

  Keywords: e-commerce; search technology; customer acquisition; Online Technology; Technological Innovation; Commercialization; Growth and Development Strategy; Technology Industry; Israel;

  Citation:

  Greenstein, Shane, and Danielle Golan. "Twiggle: E-commerce with Semantic Search." Harvard Business School Case 620-025, August 2019.  View Details

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