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Organizational Spectroscope - questions and email data for team satisfaction

Posted on by Brandon Klein

The Organizational Spectroscope combines digital communication data, such as email metadata (e.g., time stamps and headers), with more traditional data sources, such as job titles, office locations, and employee satisfaction surveys. These data sources are combined only in ways that respect privacy and ethical considerations. We then use a variety of statistical modeling techniques to predict and explain outcomes of interest to employees, HR, and management.

Predicting team satisfaction

To illustrate the potential of these new data and methods, we analyzed the aggregate email activity patterns of teams of US-based Microsoft employees to predict their responses to an annual employee satisfaction survey. To protect individual employee privacy only email metadata was used (i.e., no content) and all identifiers were encrypted. Email activity and survey responses were aggregated to the manager level, where only managers with at least five direct reports were included, and only these aggregated results were analyzed. Our predictions therefore apply only to teams of employees who share the same manager, not to individuals.

We focused on three survey questions: did teams have confidence in the overall effectiveness of their managers, did they think that different groups across the company collaborated effectively, and were they satisfied with their own work — life balance?

We started by examining the data and found that that the vast majority of teams were pretty happy. Although this result is encouraging, as a practical matter HR managers are less interested in the large majority of happy teams than in identifying the small minority of unhappy teams. After all, it is the latter group on which HR needs to focus its resources. Rather than trying to predict the satisfaction level of every team, therefore, we focused on predicting just the teams in the bottom 15% — i.e., the least satisfied teams.

We considered two statistical models: first...