Using a data-driven approach to writing a book

 During the last winter holidays I started to write my third book. After a deep introduction to Product Management and a broad review of a digital product's lifecycle and related metrics, this next work goes around product building, behavioral psychology and experimentation. It's quite a deep topic and I was really looking forward to starting its writing.

However, compared to the previous two books, in this case I didn't have a great plan ahead. The first book was based on a Master's course I had been teaching for years. The second one, on a course I had been teaching for more than 10 years in many different ways. For the product psychology one,  I had taught a short course a few times but I was not sure at all about the table of contents and the overall structure. How should I approach writing?

After thinking about it a little bit, I realized I was going to talk about cognitive bias and incentives. So I just decided to eat my own dog food and apply this to my work. Specifically, social proof. 

After building a quick excel spreadsheet and, I recognize it, seeing if I could make a good start at writing for a month and a half, I decided to share my  advances in both twitter and linkedin.

I got a few great comments both online and offline and even though I didn't feel social proof was really key for me, pushing me to share more about my journey.

The spreadsheet shows, based on an estimate of the number of pages to be written (around 45,000, or hopefully a little less than 200 pages) and of my writing rhythm (basically around 2,000 words per weekend). This gave me the estimation of the number of months that this would take me.

May went really slow with just a couple of writing days, so for a moment I thought I was going to lose track and that I would finish the book before Christmas. But I was able to push it during early June, and I was so close that I just couldn't resis it so I finished it!

First draft ready! Around 200 pages, four main sections (neurology, psychology, experimentation and use case.)