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<HTML> <HEAD> <!--SCRIPT LANGUAGE="JavaScript" SRC="http://a1835.g.akamai.net/f/1835/276/3h/www.netlibrary.com/include/js/dictionary_library.js"></SCRIPT> <SCRIPT LANGUAGE="JavaScript"> if (!opener){document.onkeyup=parent.turnBookPage;} </SCRIPT!--> <META HTTP-EQUIV="Cache-Control" CONTENT="no-cache"> <META HTTP-EQUIV="Pragma" CONTENT="no-cache"> <META HTTP-EQUIV="Expires" CONTENT="-1"><META http-equiv="Content-Type" content="text/html; charset=windows-1252"><SCRIPT>var PrevPage="Page_23";var NextPage="Page_25";var CurPage="Page_24";var PageOrder="36";</SCRIPT> <TITLE>Document</TITLE> </HEAD> <BODY BGCOLOR="#FFFFFF"><CENTER><TABLE BORDER=0 WIDTH=100% CELLPADDING=0><TR><TD ALIGN=CENTER> <TABLE BORDER=0 CELLPADDING=2 CELLSPACING=0 WIDTH=100%> <TR> <TD ALIGN=LEFT><A HREF='Page_23.html'>Previous</A></TD> <TD ALIGN=RIGHT><A HREF='Page_25.html'>Next</A></TD> </TR> </TABLE></TD></TR><TR><TD ALIGN=LEFT><P><A NAME='JUMPDEST_Page_24'/><A NAME='{FF}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0 WIDTH='100%'><TR><TD ALIGN=RIGHT><FONT FACE='Times New Roman, Times, Serif' SIZE=2 COLOR=#FF0000>Page 24</FONT></TD></TR></TABLE><A NAME='{100}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5></TD> <TD COLSPAN=3 HEIGHT=12></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>take hours. The Web has precipitated the trend toward one-to-one marketing and the validation of data mining results by allowing the rapid evaluation of predictive models.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{101}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5></TD> <TD COLSPAN=3 HEIGHT=12></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>It is not difficult to assess the benefits of data mining and its return-on-investment (ROI). Simply consider the quantitative counts of clickthroughs of ads or banners prior and after your data mining analysis. Consider the percentage of sales or requests for product information, as well as the amounts of purchases made as a result of a data mining analysis. Consider the rates prior to your data mining efforts and afterwards. If you initiate a marketing e-mail campaign on the basis of your data mining analysis, consider the rate of responses by splitting your e-mails on those individuals targeted via your analysis and those excluded from the targeting. Measure the improved rate of responses and sales from those targeted via the data mining analysis and those without it.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{102}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5></TD> <TD COLSPAN=3 HEIGHT=12></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>The dynamics of your industry and marketplace will dictate how often you should mine your website data. The intervals for mining your data will depend on how often the attributes of your customers change. For example, a bank may have a cross-selling model for its call site that can be quite effective for months. The intervals in which the bank model are created may take place on a quarterly or monthly basis and still be relevant to the business questions they are trying to answer, such as cross-selling opportunities of their financial products like CDs, bankcards, loans, etc. For a portal, such as a search engine, models may need to be refreshed on a weekly basis, because the dynamics of the content, their visitors, and their features change more quickly than, say, with a bank's customers. The end products they are trying to predict are also subject to change more frequently, for a bank it is a loan, for a portal it is a banner or ad.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{103}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5></TD> <TD COLSPAN=3 HEIGHT=12></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>For an Internet company which exists completely on the Web, the data mining process represents a biofeedback system to its entire supply chain. Data mining can identify for electronic retailers key market segments, which can impact directly on its overall website design and inventory control systems. As with physical retailers, the leveraging of data mining pays off in the positioning of the right message, product, and service in front of the right customers at the right time in the right format.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{104}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5></TD> <TD COLSPAN=3 HEIGHT=12></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>Data mining is not an isolated process carried in a vacuum; it must be integrated into the entire electronic retailing and marketing processes. This is especially true with virtual storefronts because everything—selections, transactions, orders, customer communications—is accelerated to "Internet time." For a website entirely sup-</FONT><FONT FACE='Times New Roman, Times, Serif' SIZE=3 COLOR=#FFFF00><!-- soft --></FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{105}'/></FORM></P></TD></TR></TABLE><P><FONT SIZE=0 COLOR=WHITE></CENTER><A NAME="bottom"> </A><!-- netLibrary.com Copyright Notice --> </BODY></HTML>⌨️ 快捷键说明
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