Tech firms are quite peculiar; they have highly qualified employees, high attrition rate and use specific tools.As you can imagine, their workplace analytics is also rather specific. For this reason, it’s worth looking at how the biggest tech firms such as PayPal, Microsoft and Google approach workplace analytics and share a few tips on how you can do it effectively at your organization.

1,7 year is the average tenure for employees in 10 biggest tech companies in the last few years (Business Insider, 2017). €17M yearly cost at a 1000-people company is calculated based on: current employee attrition rate at an average level of 20-30% for tech, coming managers’ attrition rate at a level of 40-60%, and attrition cost up to 150% of annual salary (40K EUR).

Key hybrid reality metrics – active and passive listening data

Last Friday, we held the second of six webinars for the tech industry.Challenge#2 | Mitigating team’s collaboration overload / lack of collaboration🎯 How big is the problem of too many meetings & work interruptions?🎯 How does it impact burnout, well-being, absenteeism, and attrition?🎯 What can be done to mitigate the workload in an evidence-based way?

Pre hybrid metrics – selective passive listening data

Pre hybrid metrics – key active listening data

The first in a series of six webinars is behind us!Challenge#1 | Planning team’s work from home & office🎯 How are companies planning hybrid work?🎯 What are the best practices?🎯 What’s the most challenging? 🎯 How can data help?These and other were discussed during the webinar last Friday with our special guests: Jessica Reeves, PHR and Zuzanna Przybyla.

Employee experience 4.0

We are starting a series of 6 webinars dedicated to HR, organization development, and work tech decision makers from IT companies. ‍ Hybrid Evolution & Great Attrition addressed in a data-informed way - webinar series

Hybrid work - why measuring it?

Network Perspective | Passive listening | What is ONA?‍

Network Perspective video on how our platform enables people leaders with advanced workplace insights.

Social collaborative learning has always been an important asset for businesses – however, the way we approach peer 2 peer collaboration has changed due to COVID and the introduction of hybrid work. Despite having the right digital tools that offer great social learning possibilities, employees fail to engage due to digital overload.

We recently discussed the four pillars of ethics and trust in Workplace Analytics – privacy, security, eliminating bias, and people impact. In this article, we’d like to further discuss how – by recognizing these four elements – you can safeguard employee data and build their trust. We discuss examples of use cases and some of the best Workplace Analytics implementations below – use them as inspiration for your own project!

If you’re considering implementing Workplace Analytics software at your business (or are looking for a more ethical way to process employee data), it’s important to check whether it abides by the four pillars mentioned above. Your PA platform needs to respect privacy, secure data to prevent breaches, eliminate biases, and genuinely impact your people in a positive way.

Deep work and multiple context time are two metrics that let you assess productivity and perceived sustainability among employees. They are best analyzed jointly, as they give you an overview of the deep work/multiple context work time and point to any areas that require improvement.