{"id":178721,"date":"2012-04-24T22:44:59","date_gmt":"2012-04-25T05:44:59","guid":{"rendered":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=178721"},"modified":"2025-08-15T16:19:31","modified_gmt":"2025-08-15T23:19:31","slug":"micro-targeting-with-the-updated-tapestry-segmentation-data-on-the-community-analyst-api-part-i-of-ii","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/apps\/analytics\/micro-targeting-with-the-updated-tapestry-segmentation-data-on-the-community-analyst-api-part-i-of-ii","title":{"rendered":"Micro-Targeting with the Updated Tapestry Segmentation Data on the Community Analyst API (Part I of II)"},"author":4231,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[34731,24571,25821],"industry":[],"product":[36591,36601],"class_list":["post-178721","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-apis","tag-demographics","tag-tapestry-segmentation","product-apps","product-developers"],"acf":{"short_description":"You heard last week that you can access new data through Community Analyst.","flexible_content":[{"acf_fc_layout":"content","content":"<p><strong><a href=\"http:\/\/blogs.esri.com\/esri\/arcgis\/files\/2012\/04\/tapestries.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-12119 alignleft\" src=\"http:\/\/blogs.esri.com\/esri\/arcgis\/files\/2012\/04\/tapestries.png\" alt=\"Some Tapestry Segments\" width=\"288\" height=\"93\" \/><\/a><\/strong><\/p>\n<p><strong>Community Analyst Data Release<\/strong><\/p>\n<p>You\u00a0<a title=\"Community Analyst Data Release\" href=\"http:\/\/blogs.esri.com\/esri\/arcgis\/2012\/04\/18\/new-data-added-to-community-analyst\/#more-11292\" target=\"_blank\" rel=\"noopener\">heard\u00a0last week<\/a>\u00a0that you\u00a0can access new data through\u00a0Community Analyst.<\/p>\n<p>Well, in celebration of this Community Analyst data release, I\u2019m going to talk about accessing one of my favorite datasets through the Community Analyst APIs\u2014the\u00a0<a title=\"Esri Tapestry Segmentation\" href=\"http:\/\/www.esri.com\/data\/esri_data\/tapestry.html\" target=\"_blank\" rel=\"noopener\">Esri Tapestry Segmentation\u00a0<\/a>\u00a0dataset\u2014and discuss\u00a0<em>how<\/em>\u00a0and\u00a0<em>why<\/em>\u00a0it\u00a0should be leveraged by organizations large and small.<\/p>\n<p>Before we get into the nitty gritty, let me describe a real life use case of Tapestry Segmentation.\u00a0 I\u2019m confident that this use case will inspire your own!<\/p>\n<p><strong>Charity Outreach<\/strong><\/p>\n<p>OK. So, what is this Esri Tapestry Segmentation stuff and what makes it so special that a geek like me gets excited?<\/p>\n<p>Well, let me use the example of a charitable organization.<\/p>\n<p>Many charitable\u00a0organizations rely on individual donors to sustain them.\u00a0 Many of these organizations are constantly under pressure to identify potential donors and solicit donations from them to keep their bills paid and pass on value and consideration to\u00a0the causes they are advocating.<\/p>\n<p>Well, this can be a delicate balance.\u00a0 With increased public scrutiny, some charitable organizations are under fire for what many perceive as\u00a0spending too much time and resources on this marketing aspect and not enough on the actual charitable work and\/or direct aid\u00a0to their causes.\u00a0 How can they responsibly balance these competing priorities?<\/p>\n<p>One obvious solution is to\u00a0really know\u00a0<strong><em>who<\/em><\/strong>\u00a0their donors are and know\u00a0<strong><em>where<\/em><\/strong>\u00a0to find more of them.\u00a0 So what does this really mean?<\/p>\n<p><strong>Esri Tapestry Segmentation \u2013 Outstanding Enterprise-grade Data for Profiling\u00a0People, Communities, Areas, and Locations\u00a0<\/strong><\/p>\n<p>If they have a good idea of the profile of their\u00a0<em>\u201cbest\u201d<\/em>\u00a0and\u00a0<em>\u201cmost reliable\u201d<\/em>\u00a0donors\u2014their \u201cdonor base\u201d\u2014then,\u00a0they can\u00a0more effectively seek\u00a0them out\u00a0in outreach efforts while greatly minimizing the chances\u00a0of\u00a0needlessly\u00a0spending\u00a0valuable time and resources on fruitless efforts.\u00a0 I guess what I\u2019m really saying here on a meta-level\u00a0is that,\u00a0<em>the most\u00a0responsible and\u00a0effective organizations thoughtfully coordinate their\u00a0efforts, decisions, and strategies based on\u00a0reliable and\u00a0actionable intelligence rather than\u00a0on whims, feelings,\u00a0or \u201cknee jerk\u201d reactions<\/em>\u2014because, of course,\u00a0having a better picture, model, or view of reality\u00a0significantly\u00a0increases your chances of success!<\/p>\n<p>OK.\u00a0 Let me put it bluntly.\u00a0 I\u2019m not a professional marketer, statistician, demographer, or data miner\u00a0(<em>even though I work with a lot of them<\/em>)\u2014I\u2019m actually a GIS geek with enough of a technical background to make me slightly \u201cdangerous.\u201d\u00a0 With\u00a0Tapestry Segmentation, you can be all of that and more with relatively little effort.\u00a0 Just imagine\u00a0leveraging advanced market segmentation methodologies which were once only accessibly to the largest and most lucrative\u00a0organizations who are able to hire these crazy people and crunch a lot of (expensive) data\u2014and,\u00a0<em>no<\/em>, you don\u2019t need a marketing MBA to understand it either!<\/p>\n<p><strong>Esri Tapestry Segmentation \u2013 What is It?<\/strong><\/p>\n<p>The dataset behind Tapestry Segmentation \u201cclassifies\u201d every area into one of 65 household categories or segments.\u00a0 Each segment represents a set of dominant characteristics among the households within the area.\u00a0 These characteristics can represent a broad base of attributes including demographic, lifestyle,\u00a0spending, interests,\u00a0and behavioral traits.\u00a0 From my perspective, the individual attributes themselves\u00a0are not so important here\u00a0<em>(although, the API supports querying\/analyzing these individual attributes in other tasks).\u00a0\u00a0<\/em>The value and \u201cwow\u201d factor\u00a0behind Tapestry Segmentation is\u00a0the fact that, the segments\u00a0effectively\u00a0describe\u00a0\u00a0<strong>households which share a common set of characteristics<\/strong>\u00a0(almost like a \u201ccluster\u201d).<\/p>\n<p>Why is this amazingly powerful?\u00a0 Well, let\u2019s go back to the example of charitable organizations.\u00a0 What\u00a0if, based on prior knowledge or records,\u00a0they have a list of areas, locations, or addresses\u00a0which represent\u00a0the greatest donor activity?\u00a0\u00a0Using Tapestry Segmentation, the charity can take this list of their \u201cbest and most reliable\u201d donors\u00a0and classify them into one of the 65\u00a0Tapestry segments.\u00a0 This\u00a0classified list may contain several different Tapestry Segments at which point, the charity\u00a0can take the \u201cdominant\u201d tapestry (the Tapestry Segment which describes most of the analyzed areas, locations, or addresses)\u00a0or the \u201ctop\u201d tapestries (which\u00a0often is described as the\u00a0top three) and\u00a0have a good indication of who these\u00a0households are and\u2026\u2026\u2026\u2026.<em>where they can find more.<\/em><\/p>\n<p>But, before I discuss\u00a0<em>finding more of these\u00a0households<\/em>\u00a0which represent your charity\u2019s lifeblood, I should note that you can\u00a0now look at some information\u00a0associated\u00a0with\u00a0the dominant tapestries of your donors\u00a0which may give you an general idea of who they are and what are some traits, behaviors,\u00a0and characteristics which are common between them.\u00a0 To do\u00a0this, you can\u00a0leverage these resources:<\/p>\n<ul>\n<li>You can<a title=\"Download a Free Poster Describing the 65 Esri Tapestry Segments\" href=\"http:\/\/www.esri.com\/industries\/apps\/business\/offers\/tapestry\/index.cfm\" target=\"_blank\" rel=\"noopener\">\u00a0download\u00a0a free poster\u00a0<\/a>which quickly describes each Tapestry Segment<\/li>\n<li>You can\u00a0<a title=\"Download a Reference Guide on Esri Tapestry Segmentation\" href=\"http:\/\/www.esri.com\/library\/brochures\/pdfs\/tapestry-segmentation.pdf\" target=\"_blank\" rel=\"noopener\">download a 96-page reference guide<\/a>\u00a0which describes each Tapestry Segment in a little more detail<\/li>\n<\/ul>\n<p>OK. Now, you want to be able to find more of these households\u2014the households which have, according to your experiences, been the most generous in helping to sustain your organization\u2019s\u00a0charity work.<\/p>\n<p>There are many options here and one that I commonly come across is to, once again, leverage the Tapestry Segmentation\u00a0data to identify\u00a0areas for marketing or outreach.\u00a0 This may be done on a large scale like cities,\u00a0Designated Market Areas (DMAs)\u2014which are media markets, Congressional Districts,\u00a0and Metro Areas;\u00a0or\u00a0at\u00a0a smaller\u00a0or more \u201clocal\u201d scale like ZIP Codes, Census Tracts,\u00a0and Census Block \u00a0Groups.<\/p>\n<p>Great!\u00a0 So, say you have the tiniest of outreach budgets and you want to micro target Census Block Groups\u00a0<em>(which may have as few as a few hundred households)<\/em>\u00a0which share the same\u00a0dominant Tapestry Segment as the majority of your best donors.\u00a0 This would be better than making a large investment\u00a0in\u00a0\u201dblindly blanketing\u201d a bunch of random households throughout a wider\u00a0area\u00a0with\u00a0solicitations for donations.\u00a0 With the \u00a0<a href=\"https:\/\/www.esri.com\/en-us\/location-intelligence\">location intelligence<\/a> obtained\u00a0through Tapestry Segmentation, you can significantly increase the chances of reaching your donor base\u00a0<em>(or market, constituency,\u00a0etc.)<\/em>\u00a0.<\/p>\n<p><strong>Esri Tapestry Segmentation \u2013 Through the Community Analyst API<\/strong><\/p>\n<p>Well, conceptually, the two\u00a0popular ways to leverage Esri Tapestry Segmentation through the API are through\u00a0<em>reporting\u00a0<\/em>and through\u00a0<em>\u201cdata enrichment\u201d\u00a0<\/em>of\u00a0 study areas.<\/p>\n<p>(A1). Through\u00a0<strong>Reporting<\/strong>, Tapestry Segmentation summarizes\u00a0information about study areas in reports.\u00a0 With the latest data release, here are the newest Tapestry Segmentation \u201cReports\u201d:<\/p>\n<ul>\n<li><strong><em>Tapestry Segmentation Area Profile (Esri 2011 Data, Census 2010 Geographies)<\/em><\/strong>\u00a0\u2013 PDF, Excel, and developer-friendly XML formats<\/li>\n<li><strong><em>Dominant Tapestry Site Map (Esri 2011 Data, Census 2010 Geographies)<\/em><\/strong>\u00a0\u2013 PDF-only report describing segments on a map<\/li>\n<\/ul>\n<p>(A2). Through\u00a0<strong>\u201cData Enrichment,\u201d\u00a0<\/strong>Tapestry Segmentation allows developers to\u00a0append or associate additional attributes\u00a0with input study areas.\u00a0 With the latest data release, here are some of the analysis variables that can be\u00a0specified to\u00a0<em>enrich<\/em>\u00a0an existing\u00a0dataset:<\/p>\n<ul>\n<li><strong><em>Dominant Tapestry Segment (Esri 2011 Data, Census 2010 Geographies)\u00a0<\/em><\/strong>&#8211; A variable which contains the numeric code associated with the dominant tapestry segment represented amongst the majority of the\u00a0households within the study area.<\/li>\n<li><strong><em>Tapestry Segment Household Counts (Esri 2011 Data, Census 2010 Geographies)\u00a0<\/em><\/strong>&#8211; Variables which indicate the number of households for each Tapestry Segment\u00a0within the study area<\/li>\n<li><strong><em>Tapestry Households Total\/Base (Esri 2011 Data, Census 2010 Geographies)\u00a0<\/em><\/strong>&#8211; A variable which represents the total number of Tapestry households within the study area.\u00a0 It can be used to calculate percentage composition of the dominant Tapestry Segment or individual Tapestry Segments.<\/li>\n<\/ul>\n<p>These\u00a0<strong>study areas<\/strong>\u00a0can consist of the following:<\/p>\n<ul>\n<li>An\u00a0<strong><em>administrative area<\/em><\/strong>\u00a0such as a ZIP Code, Census Tract, county, Congressional District,\u00a0etc.<\/li>\n<li>A\u00a0<strong><em>calculated<\/em>\u00a0<em>area around a Point location (address\/coordinates)<\/em><\/strong>\u00a0including\u00a0a ring-based buffer area, drive time or drive distance\u00a0-based network service area<\/li>\n<li>A<strong><em>\u00a0custom-defined map area (polygon feature)<\/em><\/strong><\/li>\n<li>An\u00a0<strong><em>administrative area\u00a0which surrounds a Point location (address\/coordinates)<\/em><\/strong>\u00a0e.g. The Census Block Group associated with an address or Point location.<\/li>\n<\/ul>\n<p>Today, I\u2019m just going to go over the\u00a0<strong>\u201cdata enrichment\u201d\u00a0<\/strong>option in our charitable organization use case.\u00a0 Here\u2019s\u00a0a quick overview and summary of what I\u2019m going to do for them:<\/p>\n<p>(B1). The charity has provided me with a list of their \u00a0<strong><em>current<\/em><\/strong>,<em>\u00a0<\/em>most generous, and sustaining donors based on their contribution history.\u00a0 They believe in the charity\u2019s \u00a0work and they feel\u00a0\u00a0passionately about its cause.\u00a0\u00a0 This list\u00a0contains hundreds of contributors, maybe more and their addresses (<em>The more \u201cobservations\u201d you have \u2014the better the view of reality!<\/em>).\u00a0\u00a0I\u2019m going to analyze this list by leveraging Tapestry Segmentation.\u00a0\u00a0I\u2019m going to determine the top\u00a0three Tapestry Segments that\u00a0these prime donors fall into after tabulating their dominant Tapestry Segments associated with their household addresses.<\/p>\n<p>(B2). I\u00a0have also\u00a0been provided a unreasonably large list of\u00a0<strong><em>potential\u00a0<\/em><\/strong>donors and their\u00a0addresses.\u00a0\u00a0I need to further qualify and \u201cfilter\u201d this\u00a0list\u00a0because the charity doesn\u2019t have the budget or resources\u00a0to send out brochures and solicitations\u00a0to each of them.\u00a0<em>\u00a0I need to help increase the charity\u2019s chances of successfully reaching their\u00a0donor base\u00a0while\u00a0preserving their existing donors\u2019 contributions!\u00a0<\/em><\/p>\n<p>(B3). The charity has decided to\u00a0<strong><em>only send out mailings to ZIP Codes which have a dominant Tapestry Segment that matches one of the top\u00a0three<\/em><\/strong>\u00a0Tapestry Segments of the current list of the most generous donors.<\/p>\n<p>And, here\u2019s the \u201crecipe\u201d using the REST API\u00a0<em>(Remember, there are many ways to\u00a0leverage Tapestry Segmentation data.\u00a0 This is only one of them)<\/em>:<\/p>\n<p>(C1). Geocode the addresses to get coordinates for each record in your list.<\/p>\n<p>You can use this<a title=\"AGOL NA Geocoding Service\" href=\"http:\/\/www.arcgis.com\/home\/item.html?id=8b980709e0534bb39784dc42f550d554\" target=\"_blank\" rel=\"noopener\">\u00a0Geocoding service on ArcGIS Online\u00a0<\/a>.<\/p>\n<p>Here\u2019s a sample of geocoding a single\u00a0 address:<br \/>\n<a href=\"http:\/\/tasks.arcgisonline.com\/ArcGIS\/rest\/services\/Locators\/TA_Address_NA_10\/GeocodeServer\/findAddressCandidates?SingleLine=380+New+York+Street,Redlands,CA,92373&amp;f=json\">http:\/\/tasks.arcgisonline.com\/ArcGIS\/rest\/services\/Locators\/TA_Address_NA_10\/GeocodeServer\/findAddressCandidates?SingleLine=380+New+York+Street,Redlands,CA,92373&amp;f=json<\/a><\/p>\n<p>(C2). Generate a \u201ctoken\u201d with your Community Analyst API subscription with the\u00a0<a title=\"BAO\/CA API Get Token Service\" href=\"http:\/\/help.arcgis.com\/en\/businessanalyst\/apis\/rest\/reference\/index.html?GetToken.html\" target=\"_blank\" rel=\"noopener\">Get Token service\u00a0<\/a>.<\/p>\n<p>This token authenticates and validates your subscription credentials.\u00a0 To leverage Tapestry Segmentation, you need a Community Analyst Standard or \u201cbetter\u201d subscription.<\/p>\n<p><a href=\"https:\/\/baoapi.esri.com\/rest\/Authentication?request=getToken&amp;username=%3CYOUR_USERNAME%3E&amp;password=%3CYOUR_PASSWORD%3E&amp;f=json\">https:\/\/baoapi.esri.com\/rest\/Authentication?request=getToken&amp;username=&lt;YOUR_USERNAME&gt;&amp;password=&lt;YOUR_PASSWORD&gt;&amp;f=json<\/a><\/p>\n<p><code>{\"results\": {\"token\": \"pWFsqkpedjcCOL8Pn_EjIfDv-m6kfyZPGAx_M4T2KGeO-ZSF2K4lmA2yyjVm-FwG10U=\"}}<\/code><\/p>\n<p>(C3). Determine the dominant Tapestry Segment of each location using the coordinates with the\u00a0<a title=\"BAO\/CA API Smart Map Facts Service\" href=\"http:\/\/help.arcgis.com\/en\/businessanalyst\/apis\/rest\/reference\/index.html?ThematicQuery.html\" target=\"_blank\" rel=\"noopener\">Smart Map Facts (AKA Thematic Query) service<\/a>\u00a0of the API.<\/p>\n<p>Here\u2019s a sample of determining the Tapestry Segments of\u00a018 locations (We\u2019re actually querying the Tapestry Segment IDs of the Census Block Groups that are associated with these locations.\u00a0 Census Block Groups can have as few as a couple of hundred households\u2014sometimes even lower than that so this is fairly \u201chigh resolution data.\u201d)<\/p>\n<p><em>(The token value in the sample will need to be replaced as it is expired.)<\/em><\/p>\n<p>In the partial example response below, the IDs represent the Census Block Group\u00a0<a href=\"http:\/\/quickfacts.census.gov\/qfd\/meta\/long_fips.htm\">FIPS code<\/a>\u00a0while the DOMTAP represents the dominant Tapestry Segment code.<\/p>\n<p><a title=\"REST request to profile locations\" href=\"http:\/\/baoapi.esri.com\/rest\/maps\/ThematicQuery\/execute?geoLevelID=US.BlockGroups&amp;f=json&amp;outFields=NAME%2CID%2CDOMTAP&amp;geometry=%7B%22points%22:%5B%5B-97.0990969%2C32.766428%5D%2C%5B-96.8154520%2C32.8203959%5D%2C%5B-96.7720944%2C32.8460005%5D%2C%5B-96.7687214%2C32.950617%5D%2C%5B-96.8034808%2C32.908863%5D%2C%5B-96.7520755%2C32.8156843%5D%2C%5B-96.7951148%2C33.0292412%5D%2C%5B-96.6584883%2C33.130008%5D%2C%5B-96.616677%2C33.2159190%5D%2C%5B-97.7431824%2C30.3885401%5D%2C%5B-98.4797976%2C29.4931635%5D%2C%5B-95.6224025%2C29.5992683%5D%2C%5B-95.5421063%2C29.6419595%5D%2C%5B-95.5726391%2C29.7365877%5D%2C%5B-95.4986994%2C29.754823%5D%2C%5B-95.4416734%2C29.7068180%5D%2C%5B-95.4181112%2C29.7391306%5D%2C%5B-97.7527695%2C30.2707094%5D%5D%2C%22spatialReference%22:%7B%22wkid%22:4326%7D%7D&amp;geometryType=esriGeometryMultipoint&amp;Token=ZxxJnedxCb49t7QZBW7cOBvt6w6gATimMDhcbd4JwGkc5SrVrG3L682hx6Y2&amp;ActiveDatasetID=USACensus2010\" target=\"_blank\" rel=\"noopener\">baoapi.esri.com\/rest\/maps\/ThematicQuery\/execute?geoLevelID=US.BlockGroups&amp;f=json&amp;outFields=NAME,ID,DOMTAP&amp;geometry={\u201cpoints\u201d:[[-97.0990969,32.766428],[-96.8154520,32.8203959],[-96.7720944,32.8460005],[-96.7687214,32.950617],[-96.8034808,32.908863],[-96.7520755,32.8156843],[-96.7951148,33.0292412],[-96.6584883,33.130008],[-96.616677,33.2159190],[-97.7431824,30.3885401],[-98.4797976,29.4931635],[-95.6224025,29.5992683],[-95.5421063,29.6419595],[-95.5726391,29.7365877],[-95.4986994,29.754823],[-95.4416734,29.7068180],[-95.4181112,29.7391306],[-97.7527695,30.2707094]],\u201dspatialReference\u201d:{\u201cwkid\u201d:4326}}&amp;geometryType=esriGeometryMultipoint&amp;Token=ZxxJnedxCb49t7QZBW7cOBvt6w6gATimMDhcbd4JwGkc5SrVrG3L682hx6Y2&amp;ActiveDatasetID=USACensus2010<\/a><\/p>\n<p>(C4). Tally and sort the Tapestry Segments to determine the top\u00a0three segments for all of your households.<\/p>\n<p>Here are actual ranked results from the previous step :<\/p>\n<p><strong>#1.<\/strong><br \/>\n5 households \u2013\u00a0<a title=\"Tapestry Segment 27 \" href=\"http:\/\/www.esri.com\/data\/esri_data\/pdfs\/tapestry\/segment27.pdf\" target=\"_blank\" rel=\"noopener\">Segment 27:\u201cMetro Renters\u201d<\/a>\u00a0 (Click to view PDF Description)<\/p>\n<p><strong>#2.<\/strong><br \/>\n3 households \u2013\u00a0<a title=\"Tapestry Segment 8\" href=\"http:\/\/www.esri.com\/data\/esri_data\/pdfs\/tapestry\/segment8.pdf\" target=\"_blank\" rel=\"noopener\">Segment 8:\u201cLaptops &amp; Lattes\u201d\u00a0\u00a0<\/a>(Click to view PDF Description)<\/p>\n<p><strong>#3.<\/strong><br \/>\n2 households \u2013\u00a0<a title=\"Tapestry Segment 1\" href=\"http:\/\/www.esri.com\/data\/esri_data\/pdfs\/tapestry\/segment1.pdf\" target=\"_blank\" rel=\"noopener\">Segment 1:\u201cTop Rung\u201d\u00a0<\/a>(Click to view PDF Description)<\/p>\n<p>#4.<br \/>\n2 households \u2013 Segment 39:\u201cYoung &amp; Restless\u201d<br \/>\n<em>(This is actually \u201ctied\u201d with Segment 1.\u00a0 In case of tie, take the lower segment number when ranking.)<\/em><\/p>\n<p>#5.<br \/>\n1 household \u2013 Segment 4:\u201dBoomburbs\u201d<\/p>\n<p>#6.<br \/>\n1 household \u2013 Segment 13:\u201dIn Style\u201d<\/p>\n<p>#7.<br \/>\n1 household \u2013 Segment 16:\u201dEnterprising Professionals\u201d<\/p>\n<p>#8.<br \/>\n1 household \u2013 Segment 19:\u201dMilk &amp; Cookies\u201d<\/p>\n<p>#9.<br \/>\n1 household \u00a0&#8211; Segment 22:\u201dMetropolitans\u201d<\/p>\n<p>#10.<br \/>\n1 household \u00a0-Segment 38:\u201dIndustrious Urban Fringe\u201d<\/p>\n<p>In general, we can conclude from the top\u00a0three Tapestry Segment descriptions that the areas contain upscale singles who are fairly affluent, educated, and stylish mostly in their 30s and 40s without the financial burden of a mortgage or family.\u00a0 There are also some very wealthy married couple households\u00a0in some of the areas.<\/p>\n<p>I now have the Tapestry Segments of the charity\u2019s \u00a0most generous and reliable donors.\u00a0 Let\u2019s go find more of them!\u00a0<em>(By the way, I will let you in on\u00a0 a\u00a0secret\u00a0about the locations I used in this sample in the next post.)<\/em><\/p>\n<p>Today, I determined the top\u00a0three Tapestry Segments of a charity\u2019s most generous donors in order to understand\u00a0<em>who<\/em>\u00a0they are.\u00a0 Next time, armed with this intelligence, I\u2019m going \u00a0to show\u00a0 you how to find\u00a0<em>where<\/em>\u00a0they are so the charity can coordinate and optimize their outreach strategy to find more of them.\u00a0\u00a0<a href=\"http:\/\/esriurl.com\/4123\">Continue to Part II<\/a><\/p>\n<p>Thanks!<br \/>\nTony<br \/>\n\u2026<br \/>\n\u201cfeatures\u201d : [<br \/>\n{<br \/>\n\u201cattributes\u201d : {<br \/>\n\u201cID\u201d : \u201c484391131111\u2033,<br \/>\n\u201cNAME\u201d : \u201c484391131.111\u2033,<br \/>\n\u201cDOMTAP\u201d : 39<br \/>\n}<br \/>\n},<br \/>\n{<br \/>\n\u201cattributes\u201d : {<br \/>\n\u201cID\u201d : \u201c481130006033\u2033,<br \/>\n\u201cNAME\u201d : \u201c481130006.033\u2033,<br \/>\n\u201cDOMTAP\u201d : 27<br \/>\n}<br \/>\n},<br \/>\n{<br \/>\n\u201cattributes\u201d : {<br \/>\n\u201cID\u201d : \u201c481130133001\u2033,<br \/>\n\u201cNAME\u201d : \u201c481130133.001\u2033,<br \/>\n\u201cDOMTAP\u201d : 1<br \/>\n}<br \/>\n},<br \/>\n{<br \/>\n\u201cattributes\u201d : {<br \/>\n\u201cID\u201d : \u201c481130001002\u2033,<br \/>\n\u201cNAME\u201d : \u201c481130001.002\u2033,<br \/>\n\u201cDOMTAP\u201d : 22<br \/>\n}<br \/>\n\u2026<\/p>\n"}],"authors":[{"ID":4231,"user_firstname":"Tony","user_lastname":"Howser","nickname":"Tony Howser","user_nicename":"ahowser","display_name":"Tony Howser","user_email":"THowser@esri.com","user_url":"","user_registered":"2018-03-02 00:15:49","user_description":"Tony is a product manager on the Data &amp; Location Services team and is focused on Esri's GeoEnrichment and Places offerings. His primary goal is to empower users and developers with valuable location-based context to support their mapping, analysis, and decision support needs.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/07\/tony_howser_headshot_480X480_96dpi.png' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"related_articles":"","card_image":false,"wide_image":false,"show_article_image":false},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Micro-Targeting with the Updated Tapestry Segmentation Data on the Community Analyst API (Part I of II)<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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