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	<title>Comments on: For Online Recommendations, One Size Doesn&#039;t Fit All</title>
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	<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/</link>
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		<title>By: Dan</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211861</link>
		<dc:creator><![CDATA[Dan]]></dc:creator>
		<pubDate>Tue, 22 Jun 2010 14:33:48 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211861</guid>
		<description><![CDATA[&lt;p&gt;Darren - Thanks for the post.  It&#039;s a great read and highlights the importance of selecting the right logic to power recommendations.&lt;/p&gt;

&lt;p&gt;Todd - Your response to Patricia was a bit curt.  We too evaluated several recommendations providers.  After beginning with a list of 8 (ATG, Baynote, Omniture, etc), we decided we wanted a provider that could incorporate a wide variety of recommendations strategies and also wanted a best of breed solution focused on personalization.  With this criteria, we narrowed that list to RichRelevance, Certona, and iGoDigital.  All three were very good companies, but we too chose another provider in the end.  You have a good product, but you do have very capable competition, and it&#039;s hard to believe that any company would bat 1000 with proposals.&lt;/p&gt;

&lt;p&gt;Jason - Thanks again for your post.  Very informative and well written.&lt;/p&gt;]]></description>
		<content:encoded><![CDATA[<p>Darren &#8211; Thanks for the post.  It&#8217;s a great read and highlights the importance of selecting the right logic to power recommendations.</p>
<p>Todd &#8211; Your response to Patricia was a bit curt.  We too evaluated several recommendations providers.  After beginning with a list of 8 (ATG, Baynote, Omniture, etc), we decided we wanted a provider that could incorporate a wide variety of recommendations strategies and also wanted a best of breed solution focused on personalization.  With this criteria, we narrowed that list to RichRelevance, Certona, and iGoDigital.  All three were very good companies, but we too chose another provider in the end.  You have a good product, but you do have very capable competition, and it&#8217;s hard to believe that any company would bat 1000 with proposals.</p>
<p>Jason &#8211; Thanks again for your post.  Very informative and well written.</p>
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		<title>By: Kavita</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211860</link>
		<dc:creator><![CDATA[Kavita]]></dc:creator>
		<pubDate>Tue, 22 Jun 2010 08:35:09 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211860</guid>
		<description><![CDATA[&lt;p&gt;Thanks for this post. I was searching for some inspiration as I analysis the potential of recommendations for a Whatsmysize.com.&lt;/p&gt;

&lt;p&gt;Previously I worked with Figleaves to implement recommendations with mixed results. Following on from Patricia Linford comments I am interested in what
metrics are used in analysis for such assertions and how is this best measured when implemented ?&lt;/p&gt;]]></description>
		<content:encoded><![CDATA[<p>Thanks for this post. I was searching for some inspiration as I analysis the potential of recommendations for a Whatsmysize.com.</p>
<p>Previously I worked with Figleaves to implement recommendations with mixed results. Following on from Patricia Linford comments I am interested in what<br />
metrics are used in analysis for such assertions and how is this best measured when implemented ?</p>
]]></content:encoded>
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		<title>By: Todd Pearson, Chief Customer Officer, richrelevance</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211859</link>
		<dc:creator><![CDATA[Todd Pearson, Chief Customer Officer, richrelevance]]></dc:creator>
		<pubDate>Wed, 22 Jul 2009 18:06:10 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211859</guid>
		<description><![CDATA[I find Patricia’s post very interesting. As chief customer officer,  I’ve seen every deal that has passed through richrelevance, and I can say that every retailer who has tested our product suite against our competitors has gone on to select us as their partner. Also, an important point of clarification – we only offer only pay-for-performance pricing and have never offered a rate anything like that described in Patricia’s comment.]]></description>
		<content:encoded><![CDATA[<p>I find Patricia’s post very interesting. As chief customer officer,  I’ve seen every deal that has passed through richrelevance, and I can say that every retailer who has tested our product suite against our competitors has gone on to select us as their partner. Also, an important point of clarification – we only offer only pay-for-performance pricing and have never offered a rate anything like that described in Patricia’s comment.</p>
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		<title>By: Patricia Linford</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211858</link>
		<dc:creator><![CDATA[Patricia Linford]]></dc:creator>
		<pubDate>Tue, 14 Jul 2009 17:15:40 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211858</guid>
		<description><![CDATA[&quot;Sites like Sears and Burton, which use more than one recommendation technique, have been able to boost sitewide sales by up to 30 percent over the long term&quot;.

As part of the e-commerce team of a large retailer, we decided we wanted to implement automated recommendations on our site. We looked at a number of players, among which Rich Relevance. We didn&#039;t select them because we did some bucket testing to compare their solution with two other vendors and felt they were better at marketing than at ecommerce.

If Mr. Vangroff&#039;s assertion is correct, (their solution increases sales by 30%) then why did they quote us a monthly price of $1,500? For an increase in sales by 30%, we&#039;d be happy to pay 10 or 100 times that price.

My intention is not to criticize Rich Relevance, (their impressive client base indicates that there must be some substance behind their claims), I just want to make sure that other ecommerce managers think carefully when selecting a recommendations vendor, and do not rely on promotional postings of this nature.]]></description>
		<content:encoded><![CDATA[<p>&#8220;Sites like Sears and Burton, which use more than one recommendation technique, have been able to boost sitewide sales by up to 30 percent over the long term&#8221;.</p>
<p>As part of the e-commerce team of a large retailer, we decided we wanted to implement automated recommendations on our site. We looked at a number of players, among which Rich Relevance. We didn&#8217;t select them because we did some bucket testing to compare their solution with two other vendors and felt they were better at marketing than at ecommerce.</p>
<p>If Mr. Vangroff&#8217;s assertion is correct, (their solution increases sales by 30%) then why did they quote us a monthly price of $1,500? For an increase in sales by 30%, we&#8217;d be happy to pay 10 or 100 times that price.</p>
<p>My intention is not to criticize Rich Relevance, (their impressive client base indicates that there must be some substance behind their claims), I just want to make sure that other ecommerce managers think carefully when selecting a recommendations vendor, and do not rely on promotional postings of this nature.</p>
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		<title>By: Recomendações online: nem tudo é perfeito! &#171; Artificial Intelligence Applications</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211857</link>
		<dc:creator><![CDATA[Recomendações online: nem tudo é perfeito! &#171; Artificial Intelligence Applications]]></dc:creator>
		<pubDate>Thu, 04 Jun 2009 18:23:59 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211857</guid>
		<description><![CDATA[[...] Para mais detalhes sobre segmentação, colaboração, personalização e similaridade, leia o artigo do autor clicando aqui. [...]]]></description>
		<content:encoded><![CDATA[<p>[...] Para mais detalhes sobre segmentação, colaboração, personalização e similaridade, leia o artigo do autor clicando aqui. [...]</p>
]]></content:encoded>
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	<item>
		<title>By: Analytics Team &#187; Blog Archive &#187; Different methods for online recommendation</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211856</link>
		<dc:creator><![CDATA[Analytics Team &#187; Blog Archive &#187; Different methods for online recommendation]]></dc:creator>
		<pubDate>Mon, 01 Jun 2009 18:51:37 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211856</guid>
		<description><![CDATA[[...] Darren Vengroff, chief scientist from RichRelevance, explains some of the components of a recommendation system in a GigaOM article. [...]]]></description>
		<content:encoded><![CDATA[<p>[...] Darren Vengroff, chief scientist from RichRelevance, explains some of the components of a recommendation system in a GigaOM article. [...]</p>
]]></content:encoded>
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	<item>
		<title>By: amazingamazon</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211855</link>
		<dc:creator><![CDATA[amazingamazon]]></dc:creator>
		<pubDate>Wed, 27 May 2009 16:48:30 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211855</guid>
		<description><![CDATA[That was incredibly informative.  I finally understand what underlies these recommendation systems.  And it makes perfect sense to use the best technique in each particular situation rather than always using the same approach?  Why wouldn&#039;t all of the recommendation providers do this?]]></description>
		<content:encoded><![CDATA[<p>That was incredibly informative.  I finally understand what underlies these recommendation systems.  And it makes perfect sense to use the best technique in each particular situation rather than always using the same approach?  Why wouldn&#8217;t all of the recommendation providers do this?</p>
]]></content:encoded>
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	<item>
		<title>By: Lotame Learnings &#187; Blog Archive &#187; Data driven targeting: Here are 4 approaches</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211854</link>
		<dc:creator><![CDATA[Lotame Learnings &#187; Blog Archive &#187; Data driven targeting: Here are 4 approaches]]></dc:creator>
		<pubDate>Wed, 27 May 2009 11:04:11 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211854</guid>
		<description><![CDATA[[...] his blog post over at GigaOm entitled &#8220;One Size Doesn’t Fit All When It Comes To Online Recommendations,&#8221; Darren Vengroff writes about 4 different product recommendations approaches, all deeply [...]]]></description>
		<content:encoded><![CDATA[<p>[...] his blog post over at GigaOm entitled &#8220;One Size Doesn’t Fit All When It Comes To Online Recommendations,&#8221; Darren Vengroff writes about 4 different product recommendations approaches, all deeply [...]</p>
]]></content:encoded>
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	<item>
		<title>By: eee fff</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211853</link>
		<dc:creator><![CDATA[eee fff]]></dc:creator>
		<pubDate>Wed, 27 May 2009 10:17:20 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211853</guid>
		<description><![CDATA[This seems like an advertorial with ANY DISCLOSURE!]]></description>
		<content:encoded><![CDATA[<p>This seems like an advertorial with ANY DISCLOSURE!</p>
]]></content:encoded>
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		<title>By: Maddy</title>
		<link>http://gigaom.com/2009/05/27/one-size-doesnt-fit-all-when-it-comes-to-online-recommendations/#comment-211852</link>
		<dc:creator><![CDATA[Maddy]]></dc:creator>
		<pubDate>Wed, 27 May 2009 07:47:04 +0000</pubDate>
		<guid isPermaLink="false">http://gigaom.com/?p=51558#comment-211852</guid>
		<description><![CDATA[How to combine the techniques to create the best overall experience. After serving more than 20 billion product recommendations.]]></description>
		<content:encoded><![CDATA[<p>How to combine the techniques to create the best overall experience. After serving more than 20 billion product recommendations.</p>
]]></content:encoded>
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