Transformation is broad: it encompasses creating change in technology, processes, and people. People are arguably the hardest to transform. This is not only because humans are creatures of habit but because until now, we have not been able to capture and measure our patterns of work in a way that would help us yield better business outcomes and happier employees.  

Financial metrics, operational data, and active data such as company surveys inform one side of the transformation story: they are snapshots in time of how you are doing. Behavioral data—the passive digital footprints knowledge workers leave behind every day—inform another part of the story. People’s day-to-day actions drive business outcomes—good and bad. By analyzing real-time data about how people collaborate, connect, and get things done, organizations can map behaviors directly to outcomes and influence or change them.  

The Way People Work Drives Success 

How can companies measure how work gets done to lead to better business outcomes? Most leaders have hunches, but without data, hunches are hard to prove and even harder to fix. Combining traditional sources of data such as financial and operational metrics together with rich behavioral data sets enable leaders to see a more holistic picture of the current state of the organization. Using business data and active (survey) data sources to create change in an organization is common practice. Adding passive, behavioral data to create and measure change and help companies become more resilient as they transform, is not yet mainstream. But it holds considerable potential. Let me give you three examples:

1. Acquiring and Retaining Great Talent 

On many transformation agendas is the topic of hiring new employees with new or different skillsets. Microsoft conducted a study to understand how onboarding new hires correlates to negative attrition, meaning attrition of employees a company does not want to lose. We combined active employee survey data including onboarding and employee engagement survey results with passive behavioral data signals. These passive data signals include collaboration time and focus time. Collaboration time is time spent in emails, meetings, and chat with other coworkers and focus time is time spent alone to focus on work and projects. The passive behavioral data also includes metadata from email, calendar, and chat and HR attributes such as meeting timestamps, HR profile, organizational name, level designation and email subject lines (certainly no body of messages or attachments are captured).  

This information allows millions of passive data signals to be aggregated and displayed to show, for example, how many levels of hierarchy are attending the same meeting or are copied on the same email thread. All these data signals are de-identified to show patterns and trends at an aggregate level with the intent of surfacing pockets of bright spots and areas of improvement.  

The study revealed that conducting a one-on-one between the manager and the new hire within the first week of employment was most critical for successful onboarding. Employees who had a manager one-on-one within the first week had 12% larger networks within the first 90 days of employment than those who did not. They also reported a higher sense of belonging to the team and their ‘intend to stay’ measure was 8% higher. These new hires also collaborated three times more with their teams. Higher collaboration in the first 90 days correlated to these employees feeling that they were contributing to the team’s overall success. The combination of this early onboarding survey data combined with the passive, anonymized data signals confirmed the importance of that first manager one-on-one within the first week of employment. 

2. Improving Manager Effectiveness 

Every business wants great leaders and managers. And employees do not leave jobs, they leave managers. Half of Gallup’s poll respondents said their manager was the number one reason as to why they left their employer. Further, Microsoft customer evidence and research concluded that effective managers lead by example with regard to working hours, ensure an even allocation of work, maintain and grow their internal network across the company, value one-on-ones with their direct reports, and are engaged.  

Behavioral data not only surfaces insights about what should change, but it can also be used to provide intelligent nudges to anyone, including managers, to help drive behavioral change to support an organization’s transformation. Examples of such nudges embedded into Microsoft Outlook are reminding managers to check in with their direct reports; schedule one-on-one time; or track any unanswered tasks, emails, or questions from their team. 

In addition, managers can access insights about their team and organization to understand in an aggregated and de-identified way whether the team as a whole is getting enough time with their managers, if they are overwhelmed by email or whether they are working too much after-hours. Depending on what the insights say, the manager can then put the whole team on a plan to remind employees with AI-based nudges to increase their focus time and preserve their quiet days. The before and after can always be measured to ensure everyone is adopting new behaviors for a better employee experience. 

3. Managing Gender Diversity

Transformation often means becoming a more inclusive and diverse company, which is a very broad topic with many change levers. A large telecommunications company and Microsoft customer conducted its own study to test a hypothesis of whether high-performing women were being managed differently than non-high-performing men. Gender and performance data were applied to the rich behavioral data set and the transformation team analyzed ‘time with manager’ and ‘networks by gender and performance.’ The company discovered that women get less time with their managers, across the board. Women classified as ‘top talent’ or ‘high potential’ get the same amount of one-on-one time with their managers with men classified as neither of those things, and top talent men get roughly 20% more time with their managers than top talent women. Men’s networks skew higher in the organization hierarchy relative to their own level and women’s networks skew lower relative to their own level. 

Data, not hunches! 

These are examples of insights where managers can enact change immediately to increase employee engagement, improve employee satisfaction, and contribute positively overall to a company’s transformation. The combination of active data with passive, behavioral data can uncover bright spots of how well organizations are structured or how effective companies are at retaining and managing employees as well as surface areas of improvement. These actionable insights enable leaders to create change programs where the before, during, and after can be measured. Joining behavioral data to business data paints a richer picture for leaders to better understand their transformation efforts, if they were successful and why.  

Danielle Grossi is Worldwide Sales Director for Microsoft 365 Knowledge & Insights.