I’m co-founder and CTO at Overleaf, a successful SaaS startup based in London. From August 2014 to December 2015, I manually tracked all of my work time, minute-by-minute, and analysed the data in R.
Like most people who track their time, my goal was to improve my productivity. It gave me data to answer questions about whether I was spending too much or too little time on particular activities, for example user support or client projects. The data showed that my intuition on these questions was often wrong.
There were also some less tangible benefits. It was reassuring on a Friday to have an answer to that usually rhetorical question, “where did this week go?” I feel like it also reduced context switching: if I stopped what I was doing to answer an chat message or email, I had to take the time to record it in my time tracker. I think this added friction was a win for overall productivity, perhaps paradoxically.
This post documents the (simple) system I built to record my time, how I analysed the data, and the results. The main things I learned were:
- I tracked a bit over 50 hours of actual work per week on average. I am more skeptical of the fabled 130 hour work week.
- My management time increased by 230% as the development team grew by 200% (2 to 6), but interestingly my time in meetings decreased by 70%.
- My development time stayed pretty much the same, but that was mainly because it shifted from the workweek to the weekend.