Mining the E-Commerce Target Customer Behavior

ABSTRACT:

In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers’ needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using K-Means algorithm and data from web log of various e-commerce websites consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

Collecting consumer information seemed have been available, but still how to analyze these data effectively is of interest to marketers and researchers. This analyzing Customer information is not available in existing system. All the analysis is done manually. This would take a lot of time. Thus calculations had done manually have some errors. Retrieve data’s from data base and analyzing these data’s are very complex process. So to solve these problems we are going for proposed system.  Some of the disadvantages of existing system are given below.

DISADVANTAGES OF EXISTING SYSTEM:

• Manual work
• Security of information is low
• A lot of time consumed
• Needs of lot of manpower
• Frequent occurrence of error

PROPOSED SYSTEM:

The traditional methods for predicting and analyzing customer demands have found a wide range of applications. They are mainly used for predicting the total quantity of products that belong to the same family rather than the relationship between the different customer groups and associated product groups. This paper clusters customer segments by using K-Means algorithm and data from web log of various e-commerce websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior. It is seen that this data mining model can serve as an efficient vehicle for firms not only to predict the products or services that should be provided or improved for their target customer groups, but also to identify the right customers for a specific product family or service.

ADVANTAGES OF PROPOSED SYSTEM:

• It allows better customer management,
• new strategies for marketing,
• an expanded range of products
• And more efficient operations.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System             : Pentium Dual Core.
• Hard Disk       : 120 GB.
• Monitor           : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram                  : 1 GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.
• Coding Language : NET,C#.NET
• Tool                         : Visual Studio 2008
• Database                : SQL SERVER 2005