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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_121";var NextPage="Page_123";var CurPage="Page_122";var PageOrder="132";</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_121.html'>Previous</A></TD> <TD ALIGN=RIGHT><A HREF='Page_123.html'>Next</A></TD> </TR> </TABLE></TD></TR><TR><TD ALIGN=LEFT><P><A NAME='JUMPDEST_Page_122'/><A NAME='{419}'/><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 122</FONT></TD></TR></TABLE><A NAME='{41A}'/><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>percent, and your data mining goal will be to increase that rate for Product A. If you have multiple products, you need to establish baseline rates for each, or at a minimum do it for main product lines, so you can begin to gauge the impact your data mining analyses are having on your overall sales and marketing efforts. You need to establish some general baselines, whether it is in the form of average respond rates or average sales, in order to measure how much better data mining works over random chance. For starters here are some possible business goals for the mining of your web data.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{41B}'/><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><I>Are you looking to identify potential new website customers?</I> This is probably the most common data mining objective. Classification most commonly involves discovering the attributes, characteristics, or features of your website customers. This objective is best achieved by having as much information as possible about who buys and who doesn't while in your website. Classification typically involves distinguishing between revenue web visitors and nonrevenue web visitors. Classification is a clear-cut black-and-white prediction.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{41C}'/><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>In order for a data mining tool, such as a neural network, to learn the unique features of your website customers, it needs to be trained with a large number of samples. You will need examples of website customers with a large number of features, which you can begin to gather in advance of the actual data mining analysis. Start by designing your registration forms to capture as much as you can about your customers. Explore how you can link your website data with existing internal databases such as your customer information file or your data warehouse. Plan to invest in the purchasing of external demographic or other third party data, which can be overlaid with your web data to enhance its value.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{41D}'/><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><I>Are you looking to find specific website product sales trends?</I> Are you looking to find revealing online trends or relationships between certain web pages representing individual products or services? In other words, is your data mining goal that of discovering a unique online <I>association?</I> This type of discovery can assist you in the positioning of certain web pages, offers, incentives, and links. The discovery of an association between unique products and services can in fact impact your overall website design. If they click on Product001.htm, what is the probability they will click on Product002.htm, Product 003.htm, etc.? More importantly, what is the probability that if they buy Product003.htm, they have a propensity to buy Product009.htm?</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{41E}'/><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><I>Are you looking to identify specific buying patterns over time in your website?</I> This may be a sequencing issue, which is usually an association problem with an additional dimension of time. Sequencing</FONT><FONT FACE='Times New Roman, Times, Serif' SIZE=3 COLOR=#FFFF00><!-- continue --></FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{41F}'/></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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