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Microtargeting, often used by political parties and election campaigns, includes direct marketing datamining techniques that involve predictive market segmentation (aka cluster analysis). It is used by the United States Republican and Democratic political parties, as well as candidates to track individual voters and identify potential supporters.

They use various means of communication such as direct mail, phone calls, home visits, television, radio, web advertising, email, and text messaging, among others, to communicate with voters, crafting messages to build support for fundraising, campaign events, volunteering, and eventually to turn them out to the polls on the election day. Microtargeting's tactics rely on transmitting a tailored message to a subgroup of the electorate on the basis of unique information about that subgroup. However, the effectiveness of this method has not been proven yet.


Although some of the tactics of microtargeting had been used in California since 1992, it really started to be used nationally only in 2004.[1] In that year, Karl Rove, along with Blaise Hazelwood at the Republican National Committee, used it to reach voters in 18 states that George W. Bush's reelection campaign was not able to reach by other means. The results were greater contacts with likely Bush voters. For example, in Iowa the campaign was able to reach 92% of eventual Bush voters (compared to 50% in 2000) and in Florida it was able to reach 84% (compared to 50% in 2000).[2] Much of this pioneering work was done by Alex Gage and his firm, TargetPoint Consulting.

Democrats did limited microtargeting in 2004, with some crediting microtargeting for Kerry's win in Iowa in 2004.[3] Some news accounts credited Republican superiority in that area for victories in that election cycle.[4] Democrats later developed microtargeting capabilities for the 2006 election cycle.[1][2] "It's no secret that the other side [Republicans] figured this out a little sooner", said Josh Syrjamaki, director of the Minnesota chapter of America Votes in October 2006. "They've had four to six years' jump on us on this stuff...but we feel like we can start to catch up."[5] In India, firms like EdwardGlobal were first to combine Microtargeting with Geofencing.[6]

In the 2016 United States presidential election, Cambridge Analytica played a role in first promoting Ted Cruz and, eventually, Donald Trump.[7] However, the claims of Cambridge Analytica's influence, made by its managers, have not been proven, and Cruz's opponent Ben Carson was ultimately unsuccessful even though he, too, involved Cambridge Analytica in his campaign.[8]


Microtargeting is a form of targeting that uses recent technological developments to gather large amounts of online data. The data from people's digital footprints is analysed to create and convey messages that reflect an individual's preferences and personality.[9] Research has shown that such digital footprints can be used to accurately and unobtrusively predict psychological traits and states of large groups of people.[10]Microtargeting is a modification of a practice used by commercial direct marketers. It would not be possible on a large scale without the development of large and sophisticated databases that contain data about as many voters as possible. The database essentially tracks voter habits in the same ways that companies like Visa track consumer spending habits. The Republican National Committee's database is called Voter Vault. The Democratic National Committee effort is called VoteBuilder.[11] A parallel Democratic effort is being developed by Catalist, a $9 million initiative headed by Harold Ickes,[2] while the leading non-partisan database is offered by Aristotle.[12]

The databases contain specific information about a particular voter (party affiliation, frequency of voting, contributions, volunteerism, etc.) with other activities and habits available from commercial data brokers. For instance, the company Cambridge Analytica added the OCEAN psychological profile (openness, conscientiousness, extraversion, agreeableness, and neuroticism) analysis to other private and public data, and developed the ability to “micro-target” individual consumers or voters with messages most likely to influence their behavior.[13] Such personal information is a "product" sold to interested companies. These data are particularly illuminating when portrayed through a geographic information system (GIS), where trends based on location can be mapped alongside dozens or hundreds of other variables. This geographic depiction also makes it ideal for volunteers to visit potential voters (armed with lists in hand, laid out in the shortest route—much like how FedEx and UPS pre-determine delivery routes).

These databases are then mined to identify issues important to each voter and whether that voter is more likely to identify with one party or another. As described by Cambridge Analytica’s CEO, their key was to identify people who might be enticed to vote for their client or be discouraged to vote for their opponent.[14] Political information is obviously important here, but consumer preferences can play a role as well. Individual voters are then put into groups on the basis of sophisticated computer modeling. Such groups have names like "Downscale Union Independents", "Tax and Terrorism Moderates," and "Older Suburban Newshounds."[2][5]

Once a multitude of voting groups is established according to these criteria and their minute political differences, then the tailored messages can be sent via the appropriate means. While political parties and candidates once prepared a single television advertisement for general broadcast nationwide, it is now not at all uncommon to have several dozen variations on the one message, each with a unique and tailored message for that small demographic sliver of the voting public. This is the same for radio advertisement, direct mail, email, as well as stump speeches and fundraising events.

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  1. ^ a b Chad Vander Veen, Zeroing In, www.govtech.net, Jan 2, 2006 Archived 2006-10-14 at the Wayback Machine, accessed November 1, 2006.
  2. ^ a b c d Yochi J. Dreazen, Democrats, Playing Catch-Up, Tap Database to Woo Potential Voters, The Wall Street Journal, October 31, 2006, A1.
  3. ^ Schaller, T: New Math: How a trio of savvy Kerry campaign workers used a fresh voter equation to win Iowa., web only. American Prospect, 2004.
  4. ^ Martin Kettle, "How Democrats missed the vote", The Guardian, November 3, 2006 [1], accessed February 2, 2007
  5. ^ a b Dan Balz, Democrats Aim to Regain Edge In Getting Voters to the Polls, Washington Post, October 8, 2006, accessed November 7, 2006. [2]
  6. ^ Jadhao, Amar, Increasing Use of Microtargeting and Geofencing in Political campaigns (March 24, 2021). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3811268
  7. ^ Rhys Blakely (2016-09-22). "Data scientists target 20 million new voters for Trump". The Times.
  8. ^ Reinbold, Fabian; Schnack, Thies (6 December 2016). "Ich ganz allein habe Trump ins Amt gebracht". Spiegel Online.
  9. ^ Krotzek, Lennart J. "Inside the Voter's Mind: The Effect of Psychometric Microtargeting on Feelings Toward and Propensity to Vote for a Candidate." International journal of communication [Online], 2019, p. 3609+. Gale Literature Resource Center, https://link.gale.com/apps/doc/A610256042/LitRC?u=ucberkeley&sid=LitRC&xid=0b2d9447. Accessed 22 Sept. 2020.
  10. ^ Matz, Sandra; Appel, Ruth; Kosinski, Michal (2020-02-01). "Privacy in the age of psychological targeting". Current Opinion in Psychology. 31: 116–121. doi:10.1016/j.copsyc.2019.08.010. PMID 31563799.
  11. ^ Aaron Blake (August 15, 2007). "DNC holds national training as it rolls out new voter file". The Hill.
  12. ^ James Verini (December 3, 2007). "Big Brother Inc". Vanity Fair.
  13. ^ Isaak, Jim; Hanna, Mina J. (August 2018). "User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection". Computer. 51 (8): 56–59. doi:10.1109/MC.2018.3191268.
  14. ^ Isaak, Jim; Hanna, Mina J. (August 2018). "User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection". Computer. 51 (8): 56–59. doi:10.1109/MC.2018.3191268.

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