Data mining your website by Jesus Meña

By Jesus Meña

Flip internet facts into wisdom approximately your customers.This interesting ebook can assist businesses create, catch, increase, and learn certainly one of their most respected new resources of promoting information-usage and transactional facts from an internet site. A company's web site is a chief element of touch with its clients and a medium during which visitor's activities are messages approximately who they're and what they need. facts Mining Your site will train you the instruments, thoughts, and applied sciences you will want to profile present and power clients and are expecting online pursuits and behaviour. you will how you can extract from the massive swimming pools of knowledge your web site generates, insights into online deciding to buy styles, and the way to use this data to layout an internet site that higher draws, engages, and keeps online consumers. info Mining Your site explains how facts mining is a beginning for the recent box of web-based, interactive retailing, advertising, and advertisements. This leading edge booklet may help net builders and retailers, site owners, and information administration execs harness strong new instruments and tactics. the 1st ebook to use facts mining particularly to e-commerce research powerful equipment for accumulating, dealing with, and mining net shopper informationUse information mining to profile consumers and create customized e-commerce courses

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In clustering, there is no predefined objective; it is a more exploratory type of analysis in which the data is allowed to organize itself. In segmentation, the bookseller starts the analysis by asking the questions, "who are my most profitable customers and how do I recognize them," which is in turn answered by the decision tree in Figure 1-21. The results of segmentation analysis can also be interpreted as rules. 77, which is described in the following rule: Page 30 Figure 1-21 Segmentation analysis decision tree, in which avg = average sales, std = standard deviation, and n = number of observations.

It is a massive worldwide creature that is continuously evolving, spawning, and changing; since its birth, it has already mutated into a new type of species, where millions of computers and consumers connect to every company in the world and their product inventory databases. In this organic environment of constant ferment, the consumers drive product availability, features, and pricing. Electronic retailing, which is supported and nurtured in this environment itself, mimics the mechanisms of a single-cell organismadapting to the needs, tastes, habits, and preferences of its customers.

Clearly not all of these steps are required, but you should consider them prior to starting any in-depth analysis. They certainly do not always follow this exact sequence, but personal experience bears out the fact that in most assignments these were the issues that needed to be resolved prior to completion of most data mining projects. Most of my prior data mining projects involved working with customer information files, datamarts, and data warehouses from retailers, banks, insurers, phone, and credit card companies, but they typically dealt with the same client-centered issues or questions, mainly: Who are the customers?

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