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_walter_ 's review for:
The Cold Start Problem
by Andrew Chen
I am pretty sure this book will one day either be required reading, or an entire course itself at some of the top business schools in the world. It is that good.
If you work on "networked" products, or are trying to implement network elements into a product, then you should read this before you go any further. Even if the subject itself is of little interest to you, the case studies (of which there are many and really well presented) are illuminating, and entertaining on their own.
Early on, the author explains that the classic view of network effects, as expounded by fans of Metcalfe's Law (the value/utility of a network is proportional to the square of its members) - is simply wrong, and that you must pay attention to the composition of the network and not just the raw numbers.
The rest of the book proceeds to explain why this is the case, and to teach you how to think about building and scaling network effects following clear, and effective exposition. He explains the theory, presents case studies on both the winner and loser side, then gives some advice.
Andrew Chen knows his stuff and it shows. There were chapters where my understanding of network effects was completely turned on its head and made me realize how fascinating this area can be.
I highly recommend that you read his blog and some of the writings of his fellow partners at a16Z as well. In a lot of ways, that content amplifies each chapter in the book.
It will for sure remain a reference for many future projects. Highest possible recommendation!
If you work on "networked" products, or are trying to implement network elements into a product, then you should read this before you go any further. Even if the subject itself is of little interest to you, the case studies (of which there are many and really well presented) are illuminating, and entertaining on their own.
Early on, the author explains that the classic view of network effects, as expounded by fans of Metcalfe's Law (the value/utility of a network is proportional to the square of its members) - is simply wrong, and that you must pay attention to the composition of the network and not just the raw numbers.
The rest of the book proceeds to explain why this is the case, and to teach you how to think about building and scaling network effects following clear, and effective exposition. He explains the theory, presents case studies on both the winner and loser side, then gives some advice.
Andrew Chen knows his stuff and it shows. There were chapters where my understanding of network effects was completely turned on its head and made me realize how fascinating this area can be.
I highly recommend that you read his blog and some of the writings of his fellow partners at a16Z as well. In a lot of ways, that content amplifies each chapter in the book.
It will for sure remain a reference for many future projects. Highest possible recommendation!