Amazon Information System Case Study

Making happy customers and retaining them is the biggest challenge for any company. Enhanced technology is helping organizations in managing their customers in the long run. Retail giant Amazon is one such organization which has used Information systems extensively in making its customers happy and loyal.

To handle huge customer database, systems have to be reliable and fast. Below are some Information systems Amazon uses:

SAS (Smart Analysis Search)

 Amazon built a system which can track fraud customers by analyzing behavioral pattern. It also helps personalize customers and serve genuine customers effectively.

Service Oriented Architecture (SOA)

 This robust and scalable information system is fully distributed and centralized to enhance

The system is based on "linking" and "Data mining". This helps in enhancing the browsing experience of the customers.

 Its special feature "Customers who bought this also bought..." is a very good method of boosting sales without the customers pressurized to do so. It also helps customers who are poor decision makers or often confused.

CRM system

 Amazon's huge success by the year 2004 was mainly because of its Supply chain system which was supported by its in-house CRM software.

 This system capture customer data using search history, online behavior, wish list, data mining and so on.

 It's Order Processing System takes care of the entire purchase till the delivery of the shipment.

 With the help of CRM, Amazon offers its best deals/promotional offers to its loyal and returning customers.

 Customer feedback, queries, wish list, product reviews are also taken care of by CRM.

 The company has the best return and tracking policy. Customers need not call customer care for queries related to these. All information and tracking tools will be available on the

It is the job of the executives to know the reliability of the customers they are dealing with. Offering credit cards to new customers, extending lines of credit for existing customers and approving loans can be risky for banks if customer data is not mined thoroughly and carefully. By analyzing patterns and trends such details can be predicted with increased accuracy.

 RBI maintains a historical database of fraudulent actions which were detected and reported using data mining.

 Two different approaches have been developed by financial institutions to detect fraud patterns.

 Using first approach, the data warehouse of a third party is tapped and data mining techniques are applied to identify fraud patterns. The result is then cross-referred by banks' own database for signs of internal trouble.

 In the second approach, banks strictly use their internal database to detect fraud patterns

 Most banks use a hybrid approach.

 Nine out of top ten credit card issuing banks successfully detect fraud using famous Falcon's Fraud Assessment system or FICO method.

 Using Customer characteristics such as credit history, length of employment and length of residency, banks can calculate credit score or DBR (Debt burden ratio) to check their ability to pay credit card loans and mortgages. Knowing the chances of default can reduce a bank's risk burdens.

Customer acquisition and