{"id":6566,"date":"2018-02-01T17:49:33","date_gmt":"2018-02-01T22:49:33","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/from-a-presidential-campaign-to-a-data-analytics-start-up-civis-analytics\/"},"modified":"2018-02-01T17:52:59","modified_gmt":"2018-02-01T22:52:59","slug":"from-a-presidential-campaign-to-a-data-analytics-start-up-civis-analytics","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/from-a-presidential-campaign-to-a-data-analytics-start-up-civis-analytics\/","title":{"rendered":"From a Presidential Campaign to a Data Analytics Start-Up \u2013 Civis Analytics"},"content":{"rendered":"<p>Dan Wagner, Chief Analytics Officer for President Obama\u2019s reelection team in 2012, discovered that his team could improve the effectiveness of the campaign\u2019s marketing effort through sophisticated data analytics. Dan\u2019s team combined data from their party\u2019s comprehensive voter roster along with demographic data provided by TV rating firms, and using machine learning, were able to more precisely determine which voters were more or less likely to vote for President Obama vs. which voters were more or less likely to get out and vote at all. The data revealed surprising insight as to which networks, shows, and time slots would reach the intended audiences for the marketing message. The analytics then allowed Dan and the reelection team to tailor appropriate messages to the specific voter types with greater effectiveness.<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a> The results of the effort are history and the message is clear: effectively harnessing data analytics and machine learning allow campaigns and companies to improve the ROI of marketing efforts and to gain deeper insight into consumer data to make better business decisions.<\/p>\n<p>Following the election, Dan started Civis Analytics as a data science company. The goal was to use what he learned from the reelection campaign to help organizations use data analytics to gain a deeper understanding of current and potential customers, often utilizing their in-house data, and ultimately allowing leaders make more informed decisions based on the data. The firm has positioned itself to be a trusted advisor, not necessarily to companies who do not yet have a data science department, but for firms with a data science team that want to change how data science influences data-driven strategic decisions within the firm in the future.<a href=\"#_ftn2\" name=\"_ftnref2\">[2]<\/a> The company is based in Chicago, IL and received $22 million in seed and Series A funding from early stage investors, including Google\u2019s Eric Schmidt.<a href=\"#_ftn3\" name=\"_ftnref3\">[3]<\/a> Civis Analytics considers itself a consulting firm, but has evolved to offer end to end data solutions for consumers through a software-as-a-service model, supported by Amazon Web Services, which allows the service to easily scale with the client.<\/p>\n<p>The services begin with the Civis Platform \u2013 \u201c<em>built by data scientists, for data scientists<\/em>,\u201d which includes a continuously updating database of 220+ million Americans, using sources such as the U.S. Census Bureau and over 500,000 surveys, and serves as the backbone of the data analytics\u2019 inputs. Next, Civis Analytics\u2019 Machine Learning technology allows the client to input proprietary data into the Civis cloud, while Civis provides the infrastructure and in-house algorithms for the client\u2019s use (or the client can write their own algorithms using Python) along with continuous quality control and information privacy. The client is able to easily model using the platform\u2019s machine learning capabilities and ultimately create reports and share insights with the client\u2019s management team.<a href=\"#_ftn4\" name=\"_ftnref4\">[4]<\/a><\/p>\n<p>Civis Analytics\u2019 Senior Product Manager Wade J. provides an example of Civis Analytics\u2019 services, citing a client with an enormous amount of data, including \u201cdata on how often the users interacted with the brand, time in app, satisfaction scores, and other unique survey data.\u201d The client, however, did not have the ability to analyze their customer base through demographic or behavioral metrics. The client uploaded the data to Civis Analytics\u2019 cloud and ran machine learning algorithms to gain insight that ultimately shaped the new customer acquisition campaign based on which potential customers behave similarly to the firm\u2019s best existing customers. The client tailored their marketing approach to reflect the insight, and was able to reduce customer acquisition costs by 20% through reduced marketing dollars.<a href=\"#_ftn5\" name=\"_ftnref5\">[5]<\/a><\/p>\n<p>Civis Analysis is operating in an exciting space, and has a thoughtful mission. They recognize that they are able to be a strategic partner with firms <em>who truly desire to make decisions based on data analytics.\u00a0<\/em>Similar to President Obama\u2019s reelection team, for the firms ready to pivot and make data analytics an integral part of the decision making process, Civis Analytics is the data analytics partner for you.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\">[1]<\/a> \u201cChapter 2.\u201d\u00a0<em>Machine Platform Crowd: Harnessing Our Digital Future<\/em>, by Andrew McAfee and Erik Brynjolfsson, W. W. Norton &amp; Company, 2017, pp. 50\u201351.<\/p>\n<p><a href=\"#_ftnref2\" name=\"_ftn2\">[2]<\/a> Malone, Katie. <em>Civis Analytics. Build a Disciplined Data Science Team<\/em>. 2017, <a href=\"http:\/\/solutions.civisanalytics.com\/rs\/776-TUK-645\/images\/DisciplinedDataScience.pdf\">http:\/\/solutions.civisanalytics.com\/rs\/776-TUK-645\/images\/DisciplinedDataScience.pdf<\/a>. Accessed 2\/1\/18<\/p>\n<p><a href=\"#_ftnref3\" name=\"_ftn3\">[3]<\/a> Ha, Anthony. \u201cEric Schmidt-Backed Data Science Startup Civis Analytics Raises\u00a0$22M.\u201d<em>TechCrunch<\/em>, TechCrunch, 30 Nov. 2016, techcrunch.com\/2016\/11\/30\/civis-analytics-series-a\/. Accessed 1\/31\/18.<\/p>\n<p><a href=\"#_ftnref4\" name=\"_ftn4\">[4]<\/a> Civis Analytics Website. <a href=\"https:\/\/www.civisanalytics.com\/platform\/\">https:\/\/www.civisanalytics.com\/platform\/<\/a> Accessed 2\/1\/18.<\/p>\n<p><a href=\"#_ftnref5\" name=\"_ftn5\">[5]<\/a> J, Wade. \u201cCivis Data: The Powerful Dataset Built Right into Civis Platform.\u201d 10\/31\/2017. <a href=\"https:\/\/www.civisanalytics.com\/blog\/civis-data-powerful-dataset-built-right-civis-platform\">https:\/\/www.civisanalytics.com\/blog\/civis-data-powerful-dataset-built-right-civis-platform<\/a>. Accessed 2\/1\/18.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Effectively harnessing data analytics and machine learning allow campaigns and companies to improve the ROI of marketing costs and to gain deeper insight into consumer data to make better business decisions.<\/p>\n","protected":false},"author":2198,"featured_media":6567,"comment_status":"open","ping_status":"closed","template":"","categories":[776,366,819],"class_list":["post-6566","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-data-analytics","category-machine-learning","category-marketing","hck-taxonomy-organization-civis-analytics","hck-taxonomy-industry-consulting","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/digital-winners-losers-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>From a Presidential Campaign to a Data Analytics Start-Up \u2013 Civis Analytics - 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