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    <title>topic Re: Data requirement for Propensity Modelling, Please Help Thanks in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126968#M6672</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;In general, the independence assumption is violated by having the repeated measures in the data.&amp;nbsp; Some suggestions:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;First, rebuild the 'wrong' (the one with all the repeats) model to make sure you can reproduce it.&lt;/P&gt;&lt;P&gt;Do a sensitivity analysis.&amp;nbsp; Sometimes, the "wrong" model is still useful.&lt;/P&gt;&lt;P&gt;-- do a model based on the first call.&lt;/P&gt;&lt;P&gt;-- do a model based on the last call.&lt;/P&gt;&lt;P&gt;-- compare them to the original model. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;If they all tell the same story, then use the first or last (whichever makes more sense in the analysis context).&lt;/P&gt;&lt;P&gt;If they tell different stories, then you have got more work to do to understand what is going on.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Another possibility for the sensitivity analysis is to use some sort of summary record for each customer.&amp;nbsp; It may be that things like number of calls are important markers.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Good luck with your modelling.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Tue, 29 Oct 2013 15:52:51 GMT</pubDate>
    <dc:creator>Doc_Duke</dc:creator>
    <dc:date>2013-10-29T15:52:51Z</dc:date>
    <item>
      <title>Data requirement for Propensity Modelling, Please Help Thanks</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126967#M6671</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;&lt;SPAN style="color: #000080;"&gt;Hi All,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="color: #000080;"&gt;I am planning to rebuild a Logistic Regression Model. In the past I have always built a Response model on data with no duplicates. &lt;STRONG&gt;One row per customer&lt;/STRONG&gt;.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="color: #000080;"&gt;The current data I have now is as below. Basically we have different call_date per customer. Can we really build a Logistic Regression model on data with duplicates or shall I only take the max call date?? The model I have to rebuild was run on the duplicated data like below...Your help will be much appreciated! Many Thanks&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;TABLE border="0" cellpadding="0" cellspacing="0" width="208"&gt;&lt;TBODY&gt;&lt;TR&gt;&lt;TD class="xl63" height="20" width="57"&gt;Cust_no&lt;/TD&gt;&lt;TD class="xl63" style="border-left: medium none;" width="71"&gt;call_date&lt;/TD&gt;&lt;TD class="xl63" style="border-left: medium none;" width="80"&gt;address_id&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;1&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;15-May-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;17162265&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;1&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;05-Jun-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;17162265&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;1&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;06-Jun-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;17162265&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;2&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;11-Apr-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;426777&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;2&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;12-Apr-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;426777&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;3&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;27-Jun-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;1604314&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;3&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;11-Jul-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;1604314&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;3&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;22-Jul-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;1604314&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;4&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;15-Aug-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;518475&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;5&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;16-Sep-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;1617498&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl64" height="20" style="border-top: medium none;"&gt;5&lt;/TD&gt;&lt;TD align="right" class="xl65" style="border-left: medium none; border-top: medium none;"&gt;25-Sep-13&lt;/TD&gt;&lt;TD align="right" class="xl64" style="border-left: medium none; border-top: medium none;"&gt;12854216&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;6&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;14-May-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;1329769&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;7&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;01-Aug-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;595104&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;8&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;16-Sep-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;12885455&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;9&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;10-Oct-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;5022919&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD align="right" class="xl63" height="20" style="border-top: medium none;"&gt;10&lt;/TD&gt;&lt;TD align="right" class="xl66" style="border-left: medium none; border-top: medium none;"&gt;05-Aug-13&lt;/TD&gt;&lt;TD align="right" class="xl63" style="border-left: medium none; border-top: medium none;"&gt;3942622&lt;/TD&gt;&lt;/TR&gt;&lt;/TBODY&gt;&lt;/TABLE&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 29 Oct 2013 14:10:06 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126967#M6671</guid>
      <dc:creator>Kanyange</dc:creator>
      <dc:date>2013-10-29T14:10:06Z</dc:date>
    </item>
    <item>
      <title>Re: Data requirement for Propensity Modelling, Please Help Thanks</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126968#M6672</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;In general, the independence assumption is violated by having the repeated measures in the data.&amp;nbsp; Some suggestions:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;First, rebuild the 'wrong' (the one with all the repeats) model to make sure you can reproduce it.&lt;/P&gt;&lt;P&gt;Do a sensitivity analysis.&amp;nbsp; Sometimes, the "wrong" model is still useful.&lt;/P&gt;&lt;P&gt;-- do a model based on the first call.&lt;/P&gt;&lt;P&gt;-- do a model based on the last call.&lt;/P&gt;&lt;P&gt;-- compare them to the original model. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;If they all tell the same story, then use the first or last (whichever makes more sense in the analysis context).&lt;/P&gt;&lt;P&gt;If they tell different stories, then you have got more work to do to understand what is going on.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Another possibility for the sensitivity analysis is to use some sort of summary record for each customer.&amp;nbsp; It may be that things like number of calls are important markers.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Good luck with your modelling.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 29 Oct 2013 15:52:51 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126968#M6672</guid>
      <dc:creator>Doc_Duke</dc:creator>
      <dc:date>2013-10-29T15:52:51Z</dc:date>
    </item>
    <item>
      <title>Re: Data requirement for Propensity Modelling, Please Help Thanks</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126969#M6673</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;You can also consider collapsing the data to a customer level, and then having specific metrics attached to that customer, eg. Number of calls, number of sales, number of purchases&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I don't know if you could use a repeated/random statement in GLIMMIX model instead as well, with a binary response. Random thought would need looking into. &lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 29 Oct 2013 16:14:28 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126969#M6673</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2013-10-29T16:14:28Z</dc:date>
    </item>
    <item>
      <title>Re: Data requirement for Propensity Modelling, Please Help Thanks</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126970#M6674</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Many Thanks Reeza and Doc. that's really helpful!...&lt;img id="smileyhappy" class="emoticon emoticon-smileyhappy" src="https://communities.sas.com/i/smilies/16x16_smiley-happy.png" alt="Smiley Happy" title="Smiley Happy" /&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 29 Oct 2013 16:52:04 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Data-requirement-for-Propensity-Modelling-Please-Help-Thanks/m-p/126970#M6674</guid>
      <dc:creator>Kanyange</dc:creator>
      <dc:date>2013-10-29T16:52:04Z</dc:date>
    </item>
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