Data mining as applied to e-commerce is a breakthrough technology that can gather information in an automated fashion and build models used to predict customer purchasing decisions with remarkable accuracy. Through data mining, e-tailers can customize their offerings and pricesùpersonalizing them to each online customerùand achieve much higher sales. Simply by knowing what type of customer is likely to purchase a particular item, e-tailers can accurately target their customers and obtain add-on sales that rapidly add up to big dollars. Although e-tailers used to be at a disadvantage in comparison with merchants in bricks-and-mortar stores, data mining has evened the playing field, allowing e-tailers to achieve the equivalent of a relationship with the customer without ever seeing his or her face. Since data mining largely occurs behind the scenes, unbeknownst to consumers, it has been criticized for privacy concerns, but most consumers appreciate the convenience of having items suggested to them that are exactly what they like. Within the software and databases that support data mining, a number of techniques are used to model and analyze the data, from simple association or clustering to more advanced logistic regression. Many of these are used in conjunction with one another to provide more precise profiling of customers and their purchasing preferences. Data mining has the potential to revolutionize e-commerce, reducing many of its disadvantages and increasing the profitability by magnitudes.
Literature Survey/Theoretical Analysis
Data MiningÆs Advantages in E-Commerce
Electronic commerce (ôE-commerceö) is similar in many ways to traditional commerce conducted in bricks-and-mortar stores or via paper catalogs. It is still concerned with customers, sales, revenues, and products or services. However, the marketing of products and services for e-commerce is more technologically advanced due to the availabi...