{"id":1985,"date":"2016-12-30T07:09:32","date_gmt":"2016-12-30T07:09:32","guid":{"rendered":"http:\/\/www.capitalnumbers.com\/blog\/?p=1985"},"modified":"2025-08-11T10:45:23","modified_gmt":"2025-08-11T10:45:23","slug":"how-usage-of-data-significantly-influenced-the-presidential-elections-of-2016","status":"publish","type":"post","link":"https:\/\/www.capitalnumbers.com\/blog\/how-usage-of-data-significantly-influenced-the-presidential-elections-of-2016\/","title":{"rendered":"How Usage Of Data Significantly Influenced The Presidential Elections Of 2016"},"content":{"rendered":"<p><i><span style=\"font-weight: 400;\">&#8220;There is a level of sophistication and knowledge about the electorate in battleground states that just gets advanced every four years.&#8221;<\/span><\/i><\/p>\n<p><i><span style=\"font-weight: 400;\">&#8211; Mr. Plouffe<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">The 2016 presidential election showed that the use of data to identify, persuade and turn out voters has become increasingly nuanced and sophisticated. All roads lead to the White House, and all routes were carved by data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Quite literally, right from the primaries, most presidential candidates used data analytics to chalk out their respective maps of the race. Then, you may ask, how did data about the same electorate tell different stories to each presidential candidate? It\u2019s a valid question and, quite simply, the essence of data science. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you are a marketer who is interested in figuring out how best to use data to leverage your position and campaigns, then the presidential elections of 2016 is one of the best case studies from which you can learn the do\u2019s and don\u2019ts from.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> Let\u2019s bring into focus the lessons that are as relevant for any marketing campaign as they were for the presidential campaigns. <\/span><\/p>\n<p><b>#1 Data May Be Accurate, but It Can Still Be Subject to Human Flaws <\/b><\/p>\n<p><span style=\"font-weight: 400;\">Hillary Clinton\u2019s campaign was proudly data driven. It generated dizzying coverage from tech and political enthusiasts alike. It was sci-fi(ish), to the extent that they used terms like \u201c<\/span><a href=\"http:\/\/www.politico.com\/magazine\/story\/2016\/09\/hillary-clinton-data-campaign-elan-kriegel-214215\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">cost per flappable delegate<\/span><\/a><span style=\"font-weight: 400;\">\u201d. The Clinton Campaign took the concept of micro-targeting to a level of exquisite art.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So, when she lost, the backlash over the data was swift and ruthless. Over-dependency on data was ridiculed, data did not fail; the human beings who analyzed it failed. The colossal collapse of most polls showed us how personal biases can percolate into sample biases, bad survey designs as well as several other loopholes that can ultimately paint an untrue picture. <\/span><\/p>\n<p><b>#2 We Should Live in the Moment, Not the Past<\/b><\/p>\n<p><a href=\"https:\/\/cambridgeanalytica.org\/\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">Cambridge Analytica,<\/span><\/a><span style=\"font-weight: 400;\"> the firm which informed key decisions on Donald Trump\u2019s campaign travel, communications, and resource allocation \u2013 put out an articulate, yet the abstract explanation of how they went on to achieve the impossible. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">One of the unique things that they emphasized is a real-time collection of audience data and quick response to it. They really <\/span>believe<span style=\"font-weight: 400;\"> (and it\u2019s very plausible) that this gave them an unrivaled insight into where the race stood every day, as well as giving them fresh information to add to its commercial and demographic data.<\/span><\/p>\n<p><b>#3 Marriage Between All Kinds of Data Nets You the Winner<\/b><\/p>\n<p><span style=\"font-weight: 400;\">In an article from what feels like a prehistoric era now, May of 2013, <\/span><span style=\"font-weight: 400;\">Wired<\/span><span style=\"font-weight: 400;\"> magazine gave us a simple formula:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Big Data + Social Data = Your Next President<\/span><\/p>\n<p><span style=\"font-weight: 400;\">They hit the nail on the head. The winning campaign apparently benefited a lot from fully integrated teams carrying out research, data science, and digital marketing. In fact, as the Cambridge Analytica proclaims, <\/span><i><span style=\"font-weight: 400;\">\u201cTheir workflow created a circular learning process. Field surveys directly influenced the data modeling. In turn, it built audiences for digital marketing, TV ads, mail and other engagement. Field research then tested the effectiveness of voter targeting, which adapted and improved accordingly. This circular process meant the campaign was constantly learning and improving its outreach.\u201d<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">These are exciting times for data, but numbers are what we make them be, and the presidential elections are just one of the many testaments to that. If out of all lessons, there\u2019s one that we would strongly preach, it would be about the need for data science to be client specific. A proper understanding of history, premise and context are imperative in building a watertight data informing system. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;There is a level of sophistication and knowledge about the electorate in battleground states that just gets advanced every four years.&#8221; &#8211; Mr. Plouffe The 2016 presidential election showed that the use of data to identify, persuade and turn out voters has become increasingly nuanced and sophisticated. All roads lead to the White House, and &#8230;<\/p>\n","protected":false},"author":13,"featured_media":1986,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false},"categories":[740],"tags":[834,863,898,913,914,916,919,1005,1158,1281,1330,1355],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/posts\/1985"}],"collection":[{"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/comments?post=1985"}],"version-history":[{"count":6,"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/posts\/1985\/revisions"}],"predecessor-version":[{"id":16183,"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/posts\/1985\/revisions\/16183"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/media\/1986"}],"wp:attachment":[{"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/media?parent=1985"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/categories?post=1985"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.capitalnumbers.com\/blog\/wp-json\/wp\/v2\/tags?post=1985"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}