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• Case | HBS Case Collection | April 2020

  Ment.io: Knowledge Analytics for Team Decision Making

  Yael Grushka-Cockayne, Jeffrey T. Polzer, Susie L. Ma and Shlomi Pasternak

  Ment.io was a software platform that used proprietary data analytics technology to help organizations make informed and transparent decisions based on team input. Ment was born out of founder Joab Rosenberg’s frustration that, while organizations collected ever increasing amounts of data, the information gathering process did not capture human insights, which he viewed as critical to the use of the data. In early 2020, Rosenberg felt that the company he had started five years ago was taking off, but he wondered how to manage the team’s limited engineering resources to help Ment stand out in a crowded software market. Should the company improve Ment’s user interface, develop new features, or focus on its innovative algorithm?

  Keywords: Decision Making; Technology; Knowledge; Knowledge Acquisition; Knowledge Management; Operations; Information Management; Product; Product Development; Entrepreneurship; Business Startups; Communications Industry; Information Industry; Information Technology Industry; Web Services Industry; Middle East; Israel;

  Citation:

  Grushka-Cockayne, Yael, Jeffrey T. Polzer, Susie L. Ma, and Shlomi Pasternak. "Ment.io: Knowledge Analytics for Team Decision Making." Harvard Business School Case 420-078, April 2020.  View Details

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• Supplement | HBS Case Collection | March 2020

  People Analytics at Teach For America (B)

  Jeffrey T. Polzer and Julia Kelley

  This is a supplement to the “People Analytics at Teach For America (A)” case. In this supplement, Managing Director Michael Metzger must decide how to extend his team’s predictive analytics work.

  Keywords: Data and Data Sets; Forecasting and Prediction; Recruitment;

  Citation:

  Polzer, Jeffrey T., and Julia Kelley. "People Analytics at Teach For America (B)." Harvard Business School Supplement 420-086, March 2020.  View Details

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• Case | HBS Case Collection | February 2018 (Revised March 2020)

  People Analytics at Teach For America (A)

  Jeffrey T. Polzer and Julia Kelley

  As of mid-2016, national nonprofit Teach For America (TFA) had struggled with three consecutive years of declining application totals, and senior management was re-examining the organization’s strategy, including recruitment and selection. A few months earlier, former TFA corps member Michael Metzger had spearheaded the launch of a predictive analytics team responsible for integrating strategy across TFA’s recruitment and admissions continuum. Metzger believed that predictive analytics could help address TFA’s recruitment and admissions challenges. Now, TFA’s senior management wanted Metzger and his team to put their data models to the test. From a data modeling standpoint, what variables and models should TFA consider as it began a new recruitment season, and, more generally, how heavily should TFA rely on predictive models to reverse the negative trends?

  Keywords: Recruitment; Selection and Staffing; Analysis; Forecasting and Prediction;

  Citation:

  Polzer, Jeffrey T., and Julia Kelley. "People Analytics at Teach For America (A)." Harvard Business School Case 418-013, February 2018. (Revised March 2020.)  View Details

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