{"id":2828812,"date":"2025-07-31T08:22:12","date_gmt":"2025-07-31T15:22:12","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=2828812"},"modified":"2025-09-09T09:30:35","modified_gmt":"2025-09-09T16:30:35","slug":"tapestry-history","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/retail\/tapestry-history","title":{"rendered":"Unraveling segmentation history: how ArcGIS Tapestry stands out"},"author":367732,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[430202,430222,22871],"tags":[780052,780202],"industry":[],"product":[36571,36551,37011],"class_list":["post-2828812","blog","type-blog","status-publish","format-standard","hentry","category-real-estate","category-retail","category-state-government","tag-arcgis-data","tag-tapestry-data","product-arcgis-enterprise","product-arcgis-online","product-esri-demographics"],"acf":{"authors":[{"ID":367732,"user_firstname":"Robin","user_lastname":"Lovell","nickname":"Robin Lovell","user_nicename":"rlovell","display_name":"Robin Lovell","user_email":"rlovell@esri.com","user_url":"","user_registered":"2025-01-22 19:15:39","user_description":"Robin Lovell is a product engineering writer for ArcGIS Online and ArcGIS Web Editor. He is a mixed methods enthusiast and loves to mix qualitative and quantitative data to discover new trends. Robin helps craft reference documentation and writes blogs, tutorials, and other content to support Esri products.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Robin-pic-1-213x200.png' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"},{"ID":32571,"user_firstname":"Donna","user_lastname":"Fancher","nickname":"Donna Fancher","user_nicename":"dfancher","display_name":"Donna Fancher","user_email":"dfancher@esri.com","user_url":"","user_registered":"2020-05-06 14:17:55","user_description":"Donna Fancher is a Data Specialist (and data enthusiast) on the Esri Data Development team. As part of the team that produces unique and innovative databases such as Tapestry Segmentation, Consumer Spending and Market Potential, Donna works with the team to create supporting documentation that enhances the data user experience. A huge advocate for both Esri GIS technology and data, she helps craft data tutorials using ArcGIS StoryMaps giving data users a better understanding of updated demographics and best use case practices.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/donna-headshot_vignette-sharpen.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"short_description":"Understand what segmentation is and how demographic data can help you make crucial decisions.","flexible_content":[{"acf_fc_layout":"content","content":"<p>Segmentation is like turning on a spotlight in a dark room. It reveals the hidden patterns in how people live, work, and engage with the world around them based on where they are and who they are. Geodemographic segmentation blends geography with demographics to paint a rich portrait of communities.<\/p>\n<p><span data-contrast=\"auto\">At its core, segmentation helps us answer: <\/span><i><span data-contrast=\"auto\">Who lives here? What do they care about? And how can we connect with them more meaningfully?<\/span><\/i><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-teams=\"true\">There are two primary building blocks of segmentation data: geographic data and demographic data. <\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-teams=\"true\">Geographic data tells us where people live based on characteristics like housing density, urbanicity, and building types.<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-ccp-props=\"{}\">Demographic data provides insight into who lives there using indicators such as age, gender, family structure, education, and income.\u00a0 <\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">This approach has evolved over decades, shaped by the growing need for precision in marketing, planning, and public service.<\/span><span data-ccp-props=\"{}\"> <a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/tapestry-segmentation.htm\">ArcGIS Tapestry&#8217;s<\/a> June 2025 update is the result of these efforts. See <span class=\"TextRun SCXW122757087 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW122757087 BCX0\"><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/tapestry-methodology.htm\">ArcGIS Tapestry methodology<\/a> for more information on how Esri creates ArcGIS Tapestry. <\/span><\/span><span class=\"EOP SCXW122757087 BCX0\" data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/span><\/p>\n"},{"acf_fc_layout":"content","content":"<h2><b><span data-contrast=\"none\">Why does segmentation matter?<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:281,&quot;335559739&quot;:281}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"none\">There are many uses for segmentation. A key purpose is targeted marketing, where businesses direct their efforts toward specific customer groups that share certain traits relevant to their products or services.<\/span><span data-contrast=\"auto\"> Segmentation isn\u2019t just for marketers, it\u2019s a <\/span><span data-contrast=\"none\">tool<\/span><span data-contrast=\"auto\"> for decision-makers across industries. Whether you&#8217;re launching a product, planning a park, or designing a public health campaign, segmentation helps you:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\">Target the right audience<\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\">Understand community needs<\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\">Tailor messages and services<\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\">Visualize behavior across space<\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">From real estate to recreation, healthcare to humanitarian work, segmentation data helps you see the people behind the places.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Segmentation helps you ask and answer critical questions to <strong>get to know your audience<\/strong>:<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Who are the people in this area?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">What challenges do they face?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">How do they engage with the world around them?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">It\u2019s about more than data points. It\u2019s about <\/span><b><span data-contrast=\"auto\">stories<\/span><\/b><span data-contrast=\"auto\">\u2014the rhythms of daily life, what drives people, and their habits.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2828852,"id":2828852,"title":"Charles Booth","filename":"Charles-Booth.png","filesize":436493,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/retail\/tapestry-history\/charles-booth","alt":"Charles Booth Color-Coded Map of London","author":"367732","description":"The Moral Mapping of Victorian and Edwardian London, Charles Booth, 1903 was one of the first segmentation analyses published.","caption":"Map Legend from The Moral Mapping of Victorian and Edwardian London, 1903.","name":"charles-booth","status":"inherit","uploaded_to":2828812,"date":"2025-06-11 15:38:05","modified":"2025-07-08 14:34:42","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":491,"height":605,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth.png","medium-width":212,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth.png","medium_large-width":491,"medium_large-height":605,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth.png","large-width":491,"large-height":605,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth.png","1536x1536-width":491,"1536x1536-height":605,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth.png","2048x2048-width":491,"2048x2048-height":605,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth-377x465.png","card_image-width":377,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Charles-Booth.png","wide_image-width":491,"wide_image-height":605}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2><b><span data-contrast=\"none\">How did segmentation evolve?<\/span><\/b><\/h2>\n<p><span data-contrast=\"auto\">Before segmentation became a marketing mainstay, businesses relied on mass production and broad messaging, hoping one product could appeal to everyone. But as early as the 1800s, thinkers began to challenge that idea. One of the first was Charles Booth, a social reformer who mapped London\u2019s neighborhoods using color-coded classifications to reveal patterns of poverty and wealth. His work laid the foundation for understanding how geography and social conditions intersect.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">In 1956, Wendell R. Smith formally introduced the term market segmentation, arguing that businesses needed to recognize and respond to the diverse needs of consumers. His ideas were echoed and expanded by marketing legends like W. Edwards Deming, Philip Kotler, Sergio Zyman, and David Aaker, who emphasized the importance of understanding behavior in a complex marketplace.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">A major leap came from geography professor Robert Webber, who developed A Classification of Residential Neighborhoods (ACORN) system in the UK to classify neighborhoods using Census data. Alongside Ken Baker, he adapted ACORN for the U.S., layering in survey data and clustering techniques to create a more behaviorally driven system. To make it more relatable, segments were given names and visuals, turning raw data into intuitive insights.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In 2002, Esri acquired CACI Marketing Systems and rebranded the U.S. version of ACORN to Community Tapestry, eventually launching the next-generation Tapestry Segmentation in 2014. Today, ArcGIS Tapestry represents over 50 years of innovation, offering a multidimensional view of American neighborhoods. ArcGIS Tapestry reveals not just where people live, but how they live, what they value, and how they engage with the world.<\/span><span data-ccp-props=\"{}\"> The June 2025 release of <a href=\"https:\/\/docdev.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/tapestry-segmentation.htm\">ArcGIS Tapestry<\/a> marks the most recent update.\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2874322,"id":2874322,"title":"ArcGIS Tapestry Timeline","filename":"tapestry-timeline-3.png","filesize":156310,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/retail\/tapestry-history\/tapestry-timeline-3","alt":"History of tapestry from 1979 to 2025","author":"367732","description":"These crucial dates represent the timeline of how segmentation data analysis evolved. Although it began in the early 1900s, from 1979 to the present, key steps are shown that resulted in ArcGIS Tapestry.","caption":"Modern segmentation analysis evolved from the 1970s to the present.","name":"tapestry-timeline-3","status":"inherit","uploaded_to":2828812,"date":"2025-07-02 15:37:34","modified":"2025-07-08 14:36:19","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":813,"height":658,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3.png","medium-width":322,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3.png","medium_large-width":768,"medium_large-height":622,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3.png","large-width":813,"large-height":658,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3.png","1536x1536-width":813,"1536x1536-height":658,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3.png","2048x2048-width":813,"2048x2048-height":658,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3-575x465.png","card_image-width":575,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/tapestry-timeline-3.png","wide_image-width":813,"wide_image-height":658}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2>What makes ArcGIS Tapestry unique? <span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:281,&quot;335559739&quot;:281}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">ArcGIS Tapestry doesn\u2019t just categorize data, it <\/span><span data-contrast=\"none\">enriches the story you tell.<\/span><span data-contrast=\"auto\">\u00a0It weaves together dozens of demographic threads to reveal the full fabric of U.S. communities. People with similar lifestyles tend to cluster together, and Tapestry captures those patterns with remarkable clarity.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Each segment is a story: where people live, how they live, what they value, and how they spend their time and money. It\u2019s like zooming out on a quilt and suddenly seeing the full design<\/span><span data-contrast=\"none\">.\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2874362,"id":2874362,"title":"Household \"A\" and household \"B\"","filename":"Household-graphic-e1751473502573.png","filesize":324848,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-e1751473502573.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/retail\/tapestry-history\/household-graphic","alt":"Household \"A\" and Household \"B\" are similar in terms of median household income and median age.","author":"367732","description":"Without the nuance of ArcGIS Tapestry, these two households seem similar in median age and income.","caption":"Two household, both alike in median age and income.","name":"household-graphic","status":"inherit","uploaded_to":2828812,"date":"2025-07-02 15:45:31","modified":"2025-07-08 14:38:22","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":600,"height":600,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-e1751473502573.png","medium-width":261,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-e1751473502573.png","medium_large-width":600,"medium_large-height":600,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-e1751473502573.png","large-width":600,"large-height":600,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-e1751473502573.png","1536x1536-width":600,"1536x1536-height":600,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-e1751473502573.png","2048x2048-width":600,"2048x2048-height":600,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-465x465.png","card_image-width":465,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Household-graphic-e1751473502573.png","wide_image-width":600,"wide_image-height":600}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2>A tale of two households<\/h2>\n<p><span class=\"TextRun SCXW247859813 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW247859813 BCX0\">Imagine two neighborhoods: Household A and Household B. On paper, they look <\/span><span class=\"NormalTextRun SCXW247859813 BCX0\">nearly identical<\/span><span class=\"NormalTextRun SCXW247859813 BCX0\">: similar median incomes, similar average ages. <\/span><span class=\"NormalTextRun SCXW247859813 BCX0\"> If <\/span><span class=\"NormalTextRun SCXW247859813 BCX0\">you&#8217;re<\/span><span class=\"NormalTextRun SCXW247859813 BCX0\"> scouting new trade areas for expansion, both might seem like equally <\/span><span class=\"NormalTextRun SCXW247859813 BCX0\">good <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed GrammarErrorHighlight SCXW247859813 BCX0\">candidate<\/span><span class=\"NormalTextRun SCXW247859813 BCX0\"> households<\/span><span class=\"NormalTextRun SCXW247859813 BCX0\">.<\/span><\/span><span class=\"EOP SCXW247859813 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">To demonstrate, let\u2019s compare the typical household in two neighborhoods using selected demographic variables that distinguish each.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2874422,"id":2874422,"title":"Up and Coming Families Segment","filename":"Up-and-Coming-Families.png","filesize":193951,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/retail\/tapestry-history\/up-and-coming-families","alt":"Up and Coming Families segment of ArcGIS Tapestry.","author":"367732","description":"Residents in this segment tend to live in suburban neighborhoods. These are large, young families in a variety of household structures.  Key employment sectors include health care, retail, education, manufacturing, and construction. ","caption":"The Up and Coming Families segment of ArcGIS Tapestry, viewed through Business Analyst. ","name":"up-and-coming-families","status":"inherit","uploaded_to":2828812,"date":"2025-07-02 15:54:33","modified":"2025-07-08 14:45:14","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1086,"height":841,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families.png","medium-width":337,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families.png","medium_large-width":768,"medium_large-height":595,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families.png","large-width":1086,"large-height":841,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families.png","1536x1536-width":1086,"1536x1536-height":841,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families.png","2048x2048-width":1086,"2048x2048-height":841,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families-600x465.png","card_image-width":600,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Up-and-Coming-Families.png","wide_image-width":1086,"wide_image-height":841}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">Household \u201cA\u201d falls into <\/span><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/lifemode-group-g.htm#ESRI_SECTION1_33E9E0FAD83049FD9044D450F6C67219\"><span data-contrast=\"none\">Segment G2<\/span><\/a><span data-contrast=\"auto\">, the Up and Coming Families segment, which consists of young families living in newer suburban homes. These households are larger, often with kids and pets, and their spending reflects that. They\u2019re investing in their homes, buying smart tech, and filling weekends with family outings to zoos, aquariums, and movie theaters. They commute longer distances, often beyond their county lines, and prioritize comfort and connection in their daily lives.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2874442,"id":2874442,"title":"Metro Renters Segment","filename":"Metro-Renters.png","filesize":180501,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/retail\/tapestry-history\/metro-renters","alt":"Metro Renters segment of ArcGIS Tapestry","author":"367732","description":"Located mainly in the centers of major metropolitan areas, these neighborhoods are composed of highly educated young professionals in their 20s and 30s. They work in professional or management positions with upper-tier incomes.","caption":"The Metro Renters segment of ArcGIS Tapestry, viewed through Business Analyst. ","name":"metro-renters","status":"inherit","uploaded_to":2828812,"date":"2025-07-02 15:55:17","modified":"2025-07-08 14:45:21","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1087,"height":843,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters.png","medium-width":337,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters.png","medium_large-width":768,"medium_large-height":596,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters.png","large-width":1087,"large-height":843,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters.png","1536x1536-width":1087,"1536x1536-height":843,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters.png","2048x2048-width":1087,"2048x2048-height":843,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters-600x465.png","card_image-width":600,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/06\/Metro-Renters.png","wide_image-width":1087,"wide_image-height":843}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">Household \u201cB\u201d, on the other hand, is in <\/span><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/lifemode-group-d.htm#ESRI_SECTION1_1A5400CDFBF5473EA1B3A3B029CB7068\"><span data-contrast=\"none\">Segment D4<\/span><\/a><span data-contrast=\"auto\">, the Metro Renters segment, which is primarily highly educated, mobile young professionals living in dense urban cores. These are mostly non-family households, often singles or roommates, who rent apartments and rely on public transit, biking, or rideshares. They\u2019re digital natives who use tech for everything from banking to shopping to reading the news. Their spending leans toward health, aesthetics, and sustainability\u2014think organic groceries, sleek home decor, and eco-conscious choices.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">So, while age and income offer a surface-level snapshot, they don\u2019t tell the full story. Tapestry segmentation reveals the deeper layers, including how people live, what they value, and how they engage with the world around them. It\u2019s this multidimensional view that turns data into insight and insight into smarter decisions.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"<h2><b><span data-contrast=\"none\">The art of insight<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:281,&quot;335559739&quot;:281}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"none\">Segmentation tools like ArcGIS Tapestry enhance understanding of communities and support data-driven decision-making. In a diverse marketplace, segmentation allows businesses to focus on consumers whose needs align with their offerings.<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">ArcGIS Tapestry is more than a dataset, it\u2019s a lens. A way to see the invisible patterns that shape our world. Whether you\u2019re a business leader, urban planner, nonprofit strategist, or curious explorer, segmentation gives you the power to understand people in place.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">And like any great tapestry, the beauty lies in the details.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"<h2><b><span data-contrast=\"none\">Get started using ArcGIS Tapestry<\/span><\/b><\/h2>\n<p>Be part of history and start your ArcGIS Tapestry exploration today. Check out some of the following resources to learn more and try out some workflows on your own.<\/p>\n<ul>\n<li><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/tapestry-segmentation.htm\">ArcGIS Tapestry<\/a><\/li>\n<li><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/tapestry-migration.htm\">Migrate to ArcGIS Tapestry<\/a><\/li>\n<li><a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/announcements\/working-with-arcgis-tapestry-data-in-arcgis-business-analyst\">Working with ArcGIS Tapestry data in ArcGIS Business Analyst<\/a><\/li>\n<li><a href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/esri-demographics\/announcements\/enriching-maps-with-arcgis-tapestry-data-in-arcgis-pro-arcgis-online-and-arcgis-location-platform\">Enriching maps with ArcGIS Tapestry data in ArcGIS Pro, ArcGIS Online, and ArcGIS Location Platform<\/a><\/li>\n<\/ul>\n"}],"related_articles":[{"ID":2817342,"post_author":"321952","post_date":"2025-07-07 06:53:15","post_date_gmt":"2025-07-07 13:53:15","post_content":"","post_title":"Working with ArcGIS Tapestry data in ArcGIS Business Analyst","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"working-with-arcgis-tapestry-data-in-arcgis-business-analyst","to_ping":"","pinged":"","post_modified":"2025-09-17 10:29:18","post_modified_gmt":"2025-09-17 17:29:18","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=2817342","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":1772152,"post_author":"78361","post_date":"2022-11-15 11:40:36","post_date_gmt":"2022-11-15 19:40:36","post_content":"","post_title":"Use Tapestry segments to analyze American disadvantage","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"use-tapestry-segments-to-analyze-american-disadvantage","to_ping":"","pinged":"","post_modified":"2025-08-15 07:20:26","post_modified_gmt":"2025-08-15 14:20:26","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1772152","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":2805772,"post_author":"317582","post_date":"2025-06-25 23:35:36","post_date_gmt":"2025-06-26 06:35:36","post_content":"","post_title":"What's new in Tapestry","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"whats-new-in-tapestry","to_ping":"","pinged":"","post_modified":"2025-09-09 09:33:40","post_modified_gmt":"2025-09-09 16:33:40","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=2805772","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"show_article_image":true,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/Economic-inequality-image-small.jpg","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/Economic-inequality-image.jpg"},"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>Unraveling segmentation history: how ArcGIS Tapestry stands out<\/title>\n<meta name=\"description\" content=\"The history of segmentation, how demographic data can help you make crucial decisions, and how ArcGIS Tapestry fits into the history.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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