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	<title>Comments on: Measuring Online Conversation &#8211; Social Media ROI</title>
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	<link>http://tweetandmeet.com/social-media-roi</link>
	<description>Midwest Minnesota Twitter Meetup</description>
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		<title>By: Don Bartholomew</title>
		<link>http://tweetandmeet.com/social-media-roi/comment-page-1#comment-419</link>
		<dc:creator>Don Bartholomew</dc:creator>
		<pubDate>Tue, 01 Sep 2009 15:15:40 +0000</pubDate>
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		<description>Not yet in social media specifically, but yes in general PR measurement situations I have used and seen others use models for forecasting. One reason to go to the trouble and expense of developing a model is that it may be used as a predictive tool going forward so long as fundamental assumptions within the model do not change.  -Don B</description>
		<content:encoded><![CDATA[<p>Not yet in social media specifically, but yes in general PR measurement situations I have used and seen others use models for forecasting. One reason to go to the trouble and expense of developing a model is that it may be used as a predictive tool going forward so long as fundamental assumptions within the model do not change.  -Don B</p>
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		<title>By: taulpaul</title>
		<link>http://tweetandmeet.com/social-media-roi/comment-page-1#comment-418</link>
		<dc:creator>taulpaul</dc:creator>
		<pubDate>Tue, 01 Sep 2009 13:09:06 +0000</pubDate>
		<guid isPermaLink="false">http://tweetandmeet.com/?p=184#comment-418</guid>
		<description>Thanks Don,&lt;br&gt;&lt;br&gt;I appreciate the insight and feedback.  We do take sentiment into consideration.  The brands I have worked with, to date, have seen moderate increased in overall positive sentiment (i.e. 5% increase in the span of 3 months).  It was also interesting to see a competitor of this brand drop 15% in positive sentiment, and watched sales decline sharply, a month later.  Have you seen any examples of using this as a regular forecasting tool?</description>
		<content:encoded><![CDATA[<p>Thanks Don,</p>
<p>I appreciate the insight and feedback.  We do take sentiment into consideration.  The brands I have worked with, to date, have seen moderate increased in overall positive sentiment (i.e. 5% increase in the span of 3 months).  It was also interesting to see a competitor of this brand drop 15% in positive sentiment, and watched sales decline sharply, a month later.  Have you seen any examples of using this as a regular forecasting tool?</p>
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		<title>By: Don Bartholomew</title>
		<link>http://tweetandmeet.com/social-media-roi/comment-page-1#comment-417</link>
		<dc:creator>Don Bartholomew</dc:creator>
		<pubDate>Tue, 01 Sep 2009 12:55:02 +0000</pubDate>
		<guid isPermaLink="false">http://tweetandmeet.com/?p=184#comment-417</guid>
		<description>Hi Paul,&lt;br&gt;Good post, Paul.  This kind of correlation model is challenging for the reasons you mentioned (e.g. isolating SM impact from all other ways it could happen) as well as factors like seasonality.  Previous efforts have shown that correlations will improve if you factor the online conversation data  to account for sentiment (you don&#039;t want to count negative mentions do you?) as well as competitive activity (at  minimum).  So share of positive discussion is preferable to just conversation volume for starters.  Another challenge with correlations is that you can achieve a relatively high correlation but have a relatively low confidence level due to the large volume of data required (one to two years worth is not too much).  One model you might consider is correlating online conversations with something like net promoter index or purchase consideration, and then correlating this with sales.  This two-stage model should yield tighter correlations and more diagnostic capability.  -Don B  @donbart</description>
		<content:encoded><![CDATA[<p>Hi Paul,<br />Good post, Paul.  This kind of correlation model is challenging for the reasons you mentioned (e.g. isolating SM impact from all other ways it could happen) as well as factors like seasonality.  Previous efforts have shown that correlations will improve if you factor the online conversation data  to account for sentiment (you don&#39;t want to count negative mentions do you?) as well as competitive activity (at  minimum).  So share of positive discussion is preferable to just conversation volume for starters.  Another challenge with correlations is that you can achieve a relatively high correlation but have a relatively low confidence level due to the large volume of data required (one to two years worth is not too much).  One model you might consider is correlating online conversations with something like net promoter index or purchase consideration, and then correlating this with sales.  This two-stage model should yield tighter correlations and more diagnostic capability.  -Don B  @donbart</p>
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		<title>By: taulpaul</title>
		<link>http://tweetandmeet.com/social-media-roi/comment-page-1#comment-416</link>
		<dc:creator>taulpaul</dc:creator>
		<pubDate>Fri, 28 Aug 2009 18:57:05 +0000</pubDate>
		<guid isPermaLink="false">http://tweetandmeet.com/?p=184#comment-416</guid>
		<description>I realize I didn&#039;t include cost saving measures into this post, as this is sometimes much more difficult to quantify, but I do believe it should in some way be included.</description>
		<content:encoded><![CDATA[<p>I realize I didn&#39;t include cost saving measures into this post, as this is sometimes much more difficult to quantify, but I do believe it should in some way be included.</p>
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