811 - Chapter 14. Personalization <blockquote> <p><script> function OpenWin(url, w, h) { if(!w) w = 400; if(!h) h = 300; window. open (url, "_new", "width=" + w + ",height=" + h + ",menubar=no,toobar=no,scrollbars=yes", true); } function Print() { window.focus(); if(window.print) { window.print(); window.setTimeout('window.close();',5000); } } </script><span></span></p> <table width="100%" cellpadding="0" cellspacing="0" border="0"><tr valign="top"></tr></table> <table width="100%" height="20" border="0" cellpadding="0" cellspacing="1"><tr></tr></table> <table width="100%" border="0" cellspacing="0" cellpadding="0"> <tr valign="top"> <td align="center"><table width="95%"><tr><td align="left"> <table width="100%" border="0" cellspacing="0" cellpadding="2"> <tr><td valign="top" height="5"><img src="/books/2/551/1/html/2/images/pixel.gif" width="1" height="5" alt="" border="0"></td></tr> <tr> <td><b><font size="1" color ="#0000FF">Team-Fly<img border="0" src="/books/2/551/1/html/2/Fly-Logo.gif" width="81" height="25"></font></b></td> <td valign="top" align="right"> </td> </tr> </table> <hr size="1"> <table width="100%" border="0" cellspacing="0" cellpadding="5"> <tr> <td valign="top" width="76" rowspan="4"><img src="/books/2/551/1/html/2/images/0130409510/0130409510_xs.jpg" width="76" height="95" border="0"></td> <td valign="top">Internet-Enabled Business Intelligence<br>By William A. Giovinazzo<br> </td> </tr> <tr><td>Table of Contents</td></tr> <tr><td></td></tr> <tr><td valign="bottom">Part 4. Building Relationships Over the Internet</td></tr> </table> <hr size ="1"> <br><table width="100%" border="0" cellspacing="0" cellpadding ="0"><tr><td valign="top"> <h2>Chapter 14. Personalization</h2> <blockquote> <p><span>The power to guess the unseen from the seen, to trace the implications of things, to judge the whole piece by the pattern, the condition of feeling life in general so completely that you are well on your way to knowing any particular corner of itthis cluster of gifts may almost be said to constitute experience.</span></p> <p><span>Henry James</span><br><span>The Art of Fiction </span></p> </blockquote> <p>In Chapter 11, we created a science fictional future in which a device perfectly provided the needs and wants of every consumer. As you may recall, we discussed how this device was conveniently located in the home and office. Without being asked, the system delivered a product specifically tailored to fulfill the consumer's desire at that point in time. In addition to the convenience, the price of the products was considered a bargain. What was compelling about this device, however, wasn't the price or the convenience, but that the device could perfectly fulfill needs and wants. This idealized future is the ultimate in personalization.</p> <p>If this is our ultimate destination, our goal, how do we get there from here? In the previous chapters, we discussed how we collect and aggregate data. In Chapter 3, we discussed the data warehouse, the central repository of our strategic information. We discussed how our Web sites capture customer behavior in the clickstream. So, what do we do with all this data once we have it? How does it get us closer to our ultimate destination? This chapter answers these questions.</p> <p>The solution to personalization is data mining. In order to understand how to apply data mining to create a personalized environment, we first need to understand personalization. The discussion begins by defining personalization and contrasting it with customization. We see that we need to come to an understanding of customers. We do this through data mining. We opened this chapter with a quote from Henry James. He described experience as "the power to guess the unseen from the seen, to trace the implication of things, to judge the whole piece by the pattern." This is an apt description of data mining. Through our experience with the customer, we can <span>guess</span> the unseen from the seen, to judge the whole piece by a pattern. We shall see how this occurs.</p> <p>The chapter then examines collaborative filtering and where it falls short in handling such large volumes of information. We see that the only way to adequately analyze this data is through data mining. As we examine the different data mining techniques, we see how methodologies can work together to provide a complete understanding of our customers. Armed with this understanding, we can build a more complete and robust personalized environment.</p> <p>We conclude this chapter by looking at a real-world example of personalization. We review the Oracle Personalization application and how it can be used by a Web application to provide recommendations to visitors and customers who access our Web site.</p> <img src="/books/2/551/1/html/2/images/pixel.gif" width="1" height="1" border="0"> </td></tr></table> <hr size="1"> <table width="100%" border="0" cellspacing="0" cellpadding="2"> <tr><td valign="top" height="5"><img src="/books/2/551/1/html/2/images/pixel.gif" width="1" height="5" alt="" border="0"></td></tr> <tr> <td><b><font size="1" color="#0000FF">Team-Fly<img border="0" src="/books/2/551/1/html/2/Fly-Logo.gif" width="81" height="25"></font></b></td> <td valign="top" align="right"> </td> </tr> </table> <table width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td valign="top" align="right">Top</td></tr></table> </td></tr></table></td> <td align="center"> </td> </tr> <tr><td colspan="3" align="center" valign="bottom"> <br><table width="100%"><tr><td height="25" valign="middle" colspan="4" align="center"> </td></tr></table> </td></tr> </table> </blockquote>
