37 Signals‘ blog has a post about the secrets to Amazon’s success which they got from the website highscalability.com.
My favourite seven points are:
- End up with a design that is as minimal as possible. Simplicity is the key if you really want to build large distributed systems.
- Work from the customer backward. Focus on value you want to deliver for the customer.
- Teams are small. They are assigned authority and empowered to solve a problem as a service in anyway they see fit.
- Start with a press release of what features the user will see and work backwards to check that you are building something valuable.
- Use measurement and objective debate to separate the good from the bad. I’ve been to several presentations by ex-Amazoners and this is the aspect of Amazon that strikes me as uniquely different and interesting from other companies. Their deep seated ethic is to expose real customers to a choice and see which one works best and to make decisions based on those tests.
- If you have a question about what you should do code it up, let people use it, and see which alternative gives you the results you want.
- Create a frugal culture. Amazon used doors for desks, for example.
September 18, 2007 at 6:02 pm |
#5). Use measurement and objective debate to separate the good from the bad. I’ve been to several presentations by ex-Amazoners and this is the aspect of Amazon that strikes me as uniquely different and interesting from other companies. Their deep seated ethic is to expose real customers to a choice and see which one works best and to make decisions based on those tests.
Yes, that is exactly true. Here are some papers by Dr. Ron Kohavi (ex-Amazon head of data-mining now work for Microsoft) that describe the use of such measurement and experimentation.
#1) Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO (accepted for publication in the ACM KDD 2007 journal)
#2) Mining Beacon: Lessons and Challenges from the World of E-Commerce (published in 2005 in the Journal of Machine Learning)
#3) Amazon’s Data Mining and Personalization (A Talk given at Emetrics 2004).