{"id":19583,"date":"2016-11-18T16:52:36","date_gmt":"2016-11-18T21:52:36","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/misunderstanding-the-american-electorate\/"},"modified":"2016-11-18T16:59:13","modified_gmt":"2016-11-18T21:59:13","slug":"misunderstanding-the-american-electorate","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/misunderstanding-the-american-electorate\/","title":{"rendered":"Misunderstanding the American Electorate"},"content":{"rendered":"

\u201cTHE POLLS ARE ALL WRONG. A STARTUP CALLED CIVIS IS OUR BEST HOPE TO FIX THEM\u201d, screamed a Wired<\/em> headline in June 2016. [1] Indeed, five months before the U.S. presidential election, the cracks in the polling system had already begun to appear. Primary elections that were supposed to be a landslide for Hillary Clinton were going the way of Bernie Sanders. In surprise after surprise, Donald Trump was pulling ahead of Ted Cruz. Of course, the biggest polling debacle was still a few months away.<\/p>\n

\"FiveThirtyEight\u2019s
FiveThirtyEight\u2019s Presidential election forecast \u2013 one of the most bullish on a Trump victory<\/figcaption><\/figure>\n

But is Civis Analytics the answer?<\/p>\n

Civis is a data science startup that aims to empower organizations to make better decisions using data and data analytics tools. Operationally, the company delivers on this promise by collecting and analyzing voting histories, consumer behavior, and other data on the American voting public. Mainly focused on political campaign targeting, the company has helped organizations answer questions like, what slogans best elicit Americans\u2019 support for aiding Syrian refugees? And, how can the government identify and better target Americans in need of health insurance? [2]<\/p>\n

The revenue opportunity for Civis is born out of the digital transformation forces that are creating a crisis for traditional polls. Mainly, the American public has become much harder to contact. In 1980, pollsters achieved a 70% response rate to (landline) phone calls, giving these firms at least a fighting chance to capture public sentiment. In 2016, the phone response rate has dropped to less than 1%. [3]<\/p>\n

\"\"<\/p>\n

One major reason is the rapid adoption of cell phones, along with caller identification and voicemail technologies, which allow individuals to screen unknown callers. The National Health Interview Survey, a government survey conducted through at-home interviews, estimated that in 2014, 43 percent of the American public used only cell phones, and another 17 percent \u201cmostly\u201d used cellphones. [4]<\/p>\n

Cell phone users are not only harder, but also more expensive, to reach. The 1991 Telephone Consumer Protection Act prohibits autodialing cell phones, a practice in which the call is passed to a live interviewer only when a consumer picks up. Thus, completing a 1,000-person cell phone poll, which may require dialing up to 20,000 random numbers, takes a great deal of paid interviewer time. [5] A landline-only polling campaign is cheaper but reaches, at best, half of the American public and generally the older, more conservative half. [6]<\/p>\n

Polling firms have thus turned to the internet to achieve greater sample sizes at lower cost. The fundamental problem is that there is no way to draw a randomized sample of the American population from the internet. Not everyone has an online presence and of those who do, the ones most likely to complete online surveys are generally younger and more liberal. [7]<\/p>\n

Thus, the good polling firms try to achieve a balance between more conservative, landline phone users and more liberal, online responders. It seems no one has achieved a good mix yet.<\/p>\n

Enter Civis, the new great hope for campaign managers. Rather than relying solely on poll responses, the company is aggregating voting histories, employment, residence, consumer behavior, and other data on America\u2019s 200 million voters. To answer the question of which slogans best elicit support for Syrian refugees, for example, the company still needs an initial round of telephone surveys. But, it can elicit opinions from a significantly smaller sample of individuals, identify which attributes (like geography and income) are likely predictors of their opinion, and extrapolate these findings to the rest of the voting public.<\/p>\n

On November 9, 2016, amid the liberal hand wringing and finger pointing, polling firms were quickly dismissed\u00a0and Civis and other \u201cbig data\u201d competitors named their successors. But does innovation in the operating model \u2013 namely, using big data instead of traditional polling \u2013 help Civis \u201cempower organizations to make better decisions\u201d? Perhaps not. For one, Civis does not appear to have solved the fundamental problem of drawing a randomized sample, since it is still reliant on an initial set of telephone surveys. Indeed, the risk of bias may be exacerbated due to the significantly smaller sample size. Furthermore, the company is relying on past behavior to predict voting outcomes \u2013 a practice that the recent\u00a0election should caution against.<\/p>\n

The answer to better polling may not lie in greater digitalization after all. Instead, companies like Civis should consider going back to basics: having more conversations with the American public to understand priorities, emotions, and nuances behind voting decisions. Conducting the extensive on-the-ground or phone-based surveys necessary to obtain a representative\u00a0sample may be costly, but understanding political preferences may be one space where greater reliance on technology destroys value.<\/p>\n

Word Count: 761<\/p>\n

[1] Graff, Garrett. \u201cThe Polls are all Wrong. A Startup called Civis is our Best Hope to Fix Them.\u201d Wired. <\/em>6 June 2016. Available at: https:\/\/www.wired.com\/2016\/06\/civis-election-polling-clinton-sanders-trump\/<\/a> [Accessed 18 November 2016].<\/p>\n

[2] Civis Analytics. \u201cAbout Civis\u201d. Available at: https:\/\/civisanalytics.com\/<\/a>. [Accessed 18 November 2016]<\/p>\n

[3] Graff, Ibid.<\/p>\n

[4] National Health Interview Survey. \u201cWireless Substitution: Early Release of Estimates From the National Health Interview Survey, January-June 2015.\u201d Center for Disease Control. <\/em>Available at www.cdc.gov\/nchs\/data\/nhis\/earlyrelease\/wireless201512.pdf<\/a>. [Accessed 17 November 2016].<\/p>\n

[5] Zukin, Cliff. \u201cWhat\u2019s the Matter with Polling?\u201d The New York Times. <\/em>20 June 2015. Available at: http:\/\/www.nytimes.com\/2015\/06\/21\/opinion\/sunday\/whats-the-matter-with-polling.html<\/a>. [Accessed 17 November 2016].<\/p>\n

[6] Lepore, Jill. \u201cPolitics and the New Machine\u201d. The New Yorker<\/em>. 16 November 2015. Available at: http:\/\/www.newyorker.com\/magazine\/2015\/11\/16\/politics-and-the-new-machine<\/a>. [Accessed 17 November, 2016].<\/p>\n

[7] Kennedy, Courtney, et al. \u201cEvaluating Online Nonprobability Surveys\u201d. Pew Research Center. 2 May 2016. Available at: http:\/\/www.pewresearch.org\/2016\/05\/02\/evaluating-online-nonprobability-surveys\/<\/a>. [Accessed 18 November 2016].<\/p>\n","protected":false},"excerpt":{"rendered":"

How digitization is destroying our ability to predict election outcomes<\/p>\n","protected":false},"author":2362,"featured_media":19616,"comment_status":"open","ping_status":"closed","template":"","categories":[],"class_list":["post-19583","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/digitization-challenge-2016\/","yoast_head":"\nMisunderstanding the American Electorate - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/misunderstanding-the-american-electorate\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Misunderstanding the American Electorate - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"How digitization is destroying our ability to predict election outcomes\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/misunderstanding-the-american-electorate\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"article:modified_time\" content=\"2016-11-18T21:59:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/image2.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"572\" \/>\n\t<meta property=\"og:image:height\" content=\"306\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/misunderstanding-the-american-electorate\\\/\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/misunderstanding-the-american-electorate\\\/\",\"name\":\"Misunderstanding the American Electorate - 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