amazon.com – An analytics perspective

In my today’s post i will be talking about amazon.com, a leading e-commerce company. Amazon.com has huge amounts of data about its customer base, products and customer purchase behaviour. To boost up sales, amazon.com uses heavy analytics on the so collected TBs of data. If you searched for a book lets say “harry potter”. You would end up getting the following sections:

• Frequently brought together
• Customers who bought this also bought the following books
• What do customers buy ultimately buy after viewing this product?

All the above are examples of recommendation system. A Recommender system attemps to present the item(s) of interest to a particular user, there by helping to make strategic marketing decisions. Basically the user data is profiled and grouped into clusters namely high/low revenue generating customers, users interested in music, movies, science,etc. Hence a user may be presented with context based contents. This customization approach puts the customer at ease.
               Hence you may not be surprised to see, when a 10-yr old boy logs in to amazon.com to purchase a book  he may be presented with video game ads in the side bar while if a 50 yr old man login, he may be presented with an ad for walking stick,etc. This increases the probability of customer making more purchases.
              A recommender system helps the customer in making better decisions. Also it helps the companies in optimizing the markting costs. For example: email costs for promoting products  could be reduced by mailing relevant product info to the right consumer, instead of sending the mails to all the users, there by spamming the users.