bringing them back.htm

WEB日志挖掘！

                  <P>For example, new products from several large data 
                  depositories &#8212; such as Acxiom and Experian, which have 
                  consumer demographics on more than 95 percent of U.S. 
                  households &#8212; make it possible to match and retrieve consumer 
                  and household information in real time over the Web. These 
                  products include household information such as age, education, 
                  occupation, marital status, presence of children, household 
                  size, income, and net worth. 
                  <P>You can couple this household information with online 
                  transactional data, mine it, and then develop a profile of a 
                  your site&#8217;s most profitable and loyal customers. Not only can 
                  e-tailers benefit from this type of analysis, but so can 
                  content providers &#8212; by identifying their core audience and 
                  adjusting their marketing efforts accordingly. Access to these 
                  types of consumer demographics, coupled with data mining 
                  technology, can significantly boost customer relationship 
                  management in ways that directly affect online visitor 
                  acquisition and retention.
                  <P>
                  <H3>Mining Web Data</H3>
                  <P>Let&#8217;s take a look at an example. Using the Clementine data 
                  mining tool from SPSS Inc., my company performed several 
                  analyses on a data set from an e-commerce site that sells 
                  clothing for men and women. The transactional data from the 
                  site included the number of purchases and type of items bought 
                  by customers, to which we appended household demographic 
                  information such as gender, age, income, and other consumer 
                  attributes. (See Figure 1) Then, using Clementine&#8217;s 
                  visualization component, we found some interesting 
                  associations among gender, age, and number of purchases made 
                  over time. In summary, the link analysis found strong 
                  associations between older male customers: They tend to make a 
                  high number of multiple purchases. 
                  <BR><BR><!-- Caption--><B>FIGURE 1</B> Purchase and product 
                  information appended with demographics. <BR><!--image--><IMG 
                  border=0 height=175 
                  src="Bringing Them Back.files/IE17July.Menafig1.gif" 
                  width=306> <BR><BR clear=all>
                  <P>We next performed a segmentation analysis using the tool&#8217;s 
                  rule generator component. Segmentation is the process of 
                  dividing your customer base into smaller markets based on 
                  different needs, preferences, behavior, and attributes &#8212; the 
                  idea being that as you segment your customers into smaller and 
                  smaller sectors, you will be able to interact with them in 
                  different ways. For example, when you segment your customer 
                  database so that you can find out who your most profitable 
                  clients are, you may want to orchestrate a marketing campaign 
                  to reward them for retention purposes. 
                  <P>The segmentation analysis, like the prior association 
                  analysis, found age to be a key factor affecting the number of 
                  sales a customer was likely to make. One of the most important 
                  market segments discovered by this analysis is simply that 
                  when the age of a customer is equal to or greater than 45, the 
                  number of multiple purchases tends to be quite high (nine). 
                  Other key attributes discovered in this analysis were the 
                  importance of a customer&#8217;s income and projected worth. 
                  <P>One of the benefits to this type of Web mining is that it 
                  lets the e-tailer target specific offers and incentives to a 
                  smaller segment of all its customers based on historical 
                  purchase patterns. In this case, the company in question can 
                  use these type of rules to drive its email marketing campaign 
                  more intelligently: Rather than marketing to all customers in 
                  the same way, it can focus on rewarding those clients who are 
                  over 45 because they are by far its most profitable ones.
                  <P>Finally, we constructed a predictive model from our Web 
                  mining analysis using Clementine&#8217;s neural-network component. 
                  First we &#8220;trained&#8221; the neural network on the historical Web 
                  data in order to predict the number of purchases new visitors 
                  are likely to make. After we trained the model, we viewed its 
                  overall design to find out what attributes are the most 
                  important inputs for predicting the output of the total 
                  Projected Number of Purchases. A sensitivity report showed 
                  that the network achieved a predicted accuracy of 94.67 
                  percent, and that these input values were the most important:
                  <P>Customer Age .55976
                  <P>Customer Income .31237
                  <P>Marital Status .17328
                  <P>Projected Worth .03903
                  <P>Gender .03834
                  <P>Presence of Children .01982
                  <P>As you can see, the most important value for predicting the 
                  number of sales at this site is the customer&#8217;s age, with 
                  income, marital status, worth, gender, and presence of 
                  children following in priority. Furthermore, Clementine, like 
                  most other data mining tools, not only helps you construct 
                  predictive models but also generates C code that you can 
                  compile and incorporate into a production system, such as a 
                  marketing or email manager. This system can then use either 
                  the rules or formulas from a neural network for targeting 
                  potential new sales prospects. 
                  <P>In this Web mining analysis case study, it became clear 
                  that certain customer features are the driving forces in 
                  predicting online sales for the company. Characteristics such 
                  as age, household projected income, and marital status are the 
                  key factors in determining the number of purchases customers 
                  would make. For this company, it is clear that its most 
                  profitable customers are mature males over 45 years in age 
                  with a high income.
                  <P>Interestingly, through the processes of segmentation and 
                  classification, this analysis uncovered hidden patterns and 
                  structures in this data set &#8212; such as the fact that although 
                  this particular retailer is very popular with young people and 
                  kids, its most profitable online customers tend to be older 
                  consumers. (Perhaps the youngsters are doing the buying using 
                  their parents&#8217; credit cards.) Furthermore, we discovered that 
                  this e-tailer&#8217;s prime customers are smack in the middle of 
                  baby-boomer country, which according to Media Metrix, an 
                  Internet and digital- media measurement service, represents 35 
                  percent of the Internet&#8217;s 63 million users. These consumers 
                  are aged 35 to 49 and have household incomes averaging $75,000 
                  and above, compared to only $58,000 among the overall online 
                  population. Plentiful, wired, and wealthy, these consumers are 
                  an e-tailer&#8217;s marketing dream. Of further interest to this 
                  particular company is the fact that these consumers are very 
                  interested in recreational travel (given their high disposable 
                  income), which it may leverage in its marketing promotions at 
                  its site.
                  <P>
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                  <H3>Leveraging Web Data </H3>
                  <P>During the past five years, the number of U.S. households 
                  with online access has grown from 6 million to 37 million; 
                  with 10 million of those households coming online just within 
                  the last year. Projections for online sales vary; however, 
                  they all agree that this marketplace is exploding. (Forrester 
                  Research predicts online buying will hit $185 billion by 
                  2004.) Although online sales still account for much less than 
                  1 percent of total retail spending, the possibility that they 
                  might soon account for much more has produced a high number of 
                  dot-com retailing ventures.
                  <P>Furthermore, not only can most items be bought online; they 
                  can be bought online at dozens of different places. This 
                  amount of choice translates to increased competition among 
                  e-commerce sites for the attraction of new and existing 
                  customers for everything from cars, books, and insurance to 
                  pet supplies. As consumer options increase, retailers will 
                  face pressure to improve customer service, broaden product 
                  offerings, and reduce prices. As competition increases, so 
                  will the need to attract and retain online customers, which is 
                  where offline demographics and data mining come into play &#8212; 
                  the Web offers an incredible channel for understanding 
                  customer behavior and expectations. 
                  <P>Indeed, customer retention will become the metric by which 
                  e-retailers will measure themselves. (For example, the Yankee 
                  Group estimates that new customer acquisition costs for 
                  Amazon.com increased from $24.89 in 1998 to $37.37 in 1999.) 
                  Web data mining is ideally positioned to give e-tailers a 
                  methodology for acquiring and retaining customers. As the 
                  marketplace grows and consumers become more sophisticated, Web 
                  mining will be a key to attracting and retaining them. 
                  <P>One caveat about mining your Web data: Tell your visitors 
                  why you are doing it and give them a choice of opting-out from 
                  the process. Explain your practices clearly and let your 
                  customers have access to their own profiles. In the end, they 
                  will realize that your objective is to service them better 
                  than your competitors. 
                  <P><!-- END ARTICLE CONTENT --><BR><BR><!-- AUTHOR BIO use MAILTO for email addresses--><B>Jesus 
                  Mena</B><I> (<A 
                  href="mailto:[e-mail]">[e-mail]</A>) is 
                  the CEO of WebMiner, a Web-mining consultancy, and the author 
                  of </I>Data Mining Your Web Site<I> (Digital Press, 2000). 
</I><!--END AUTHOR BIO--><BR><BR>
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                        <H3>RESOURCES</H3><!--resource content here --><B>Axciom:</B> 
                        <A href="http://www.acxiom.com/" 
                        target=_new>http://www.acxiom.com/</A><BR><B>Experian:</B> 
                        <A href="http://www.experian.com/" 
                        target=_new>http://www.experian.com/</A><BR><B>SPSS:</B> 
                        <A href="http://www.spss.com/" 
                        target=_new>http://www.spss.com/</A> 
                    </FONT></TD></TR></TBODY></TABLE><!--end--><!--END AUTHOR BIO-->
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