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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Big data helps political campaigns decide whom to contact, what to say, and whether to spend resources on persuasion, fundraising, or getting out the vote. Campaigns combine voter and demographic information with surveys and digital interactions to estimate political behavior and likely responses. Those estimates can guide more focused outreach, but they are probabilities—not facts about an individual’s beliefs—and they raise serious questions about privacy, consent, transparency, and accountability.
How campaigns turn data into action
The basic process is a loop: gather information, estimate likely behavior or response, group people into audiences, choose an intervention, and measure what happens. The particular data and methods vary; not every campaign uses the same inputs or modeling approach.
- Collect inputs. Depending on what is available and legally permitted, inputs may include voter-registration records, demographic information, survey responses, consumer or behavioral data, web and social-media interactions, and a person’s history of engaging with the campaign.
- Estimate political behavior. Statistical or machine-learning models can estimate such things as whether someone is likely to vote, support a candidate or issue, be persuadable, or respond to a particular campaign contact. Political scientists David W. Nickerson and Todd Rogers describe these as individual-level predictions about political behavior, support, and possible changes in support in response to campaign interventions.
- Build audience segments. Campaigns use those estimates to group people and prioritize limited resources. A segment might be used to guide canvassing, fundraising, persuasion, or turnout work; a score is a prediction, not confirmation of what a voter thinks or will do.
- Choose a message and channel. An audience may receive digital advertising, email, SMS, a phone call, or a canvassing visit. Campaigns can tailor the intervention to the modeled audience, rather than sending every person the same message through the same channel.
- Measure and adjust. Campaigns can observe responses and use engagement information to inform later targeting. This creates a feedback loop, although the available evidence does not show that every campaign measures or updates its models in the same way.
This is the practical meaning of political microtargeting: applying data and predictions to audience selection, message choice, and contact strategy. The term does not describe one specific model or guarantee that a message will persuade anyone.
What data-driven targeting can—and cannot—do
Where it can help
Campaign resources are finite. Predictions can help organizers decide which voters may warrant a conversation, which supporters may need a turnout reminder, or where an outreach effort might be more useful. Segmenting audiences can also make messages more relevant to the concerns a campaign believes those audiences may have. These are ways to allocate and customize outreach; they do not establish that a particular strategy changes votes or election results.
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Why a score is not a fact
A model estimates likelihood from available data and patterns. It can be wrong, rely on incomplete or outdated information, or mistake a correlation for a person’s actual beliefs. Treating a predicted preference or trait as certain can lead to misplaced or intrusive messages, while finely divided audiences can make it harder for voters and the public to see how political communication differs from group to group.
Effectiveness requires caution
The documented use of profiling and targeted messages shows how data can be put to work in campaigns. It does not prove that psychographic targeting has a universal causal effect on voter behavior or election outcomes. Claims about a model’s predictive accuracy or a campaign’s success should be distinguished from evidence that the targeting itself caused a change.
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What the Cambridge Analytica case established
The Cambridge Analytica episode is a prominent example of the risks of political data collection and use—not proof that one company or model decided an election.
- A 2022 article in the Canadian Journal of Political Science reports that more than 87 million Facebook users’ personal data had been obtained by Cambridge Analytica and used for political profiling and targeted messages. The figure is the article’s account of the reported scale of the data, not a measure of campaign effectiveness.
- In 2019, the U.S. Federal Trade Commission found that Cambridge Analytica had used deceptive practices to harvest personal information from tens of millions of Facebook users for voter profiling and targeting.
- A U.S. congressional record describes Cambridge Analytica as a voter-profiling firm and connects its data collection to contracts involving the 2016 Ted Cruz and Donald Trump campaigns.
- The Federal Election Commission’s MUR #7351 record contains formal matters involving Cambridge Analytica and several campaign committees, with factual and legal analyses filed in 2019. The existence of those records should not be mistaken for a finding that a particular model determined the election outcome.
Taken together, these records document concerns about how personal information was obtained and used. They do not establish that psychographic profiles independently changed enough votes to determine an election.
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How to compare campaign targeting strategies
The label “data-driven” covers very different practices. To understand what a strategy involves, ask how it performs across these dimensions:
| Dimension | What to examine |
|---|---|
| Data source and consent | Where did the information come from? Was it supplied directly, collected through a platform, or obtained from another source? Was there meaningful notice and an appropriate lawful basis for its use? |
| Prediction target | Is the model estimating turnout, candidate or issue support, persuadability, or response to a specific intervention? These are different predictions and should not be treated as interchangeable. |
| Audience granularity | Does the campaign act on broad groups, smaller segments, or individual-level estimates? More detail can enable more tailored action but can also increase privacy and transparency concerns. |
| Intervention | What message or action is selected, and through which channel—digital advertising, email, SMS, phone, or canvassing? |
| Measurement | Does the campaign track responses and use them to revise later targeting? What evidence supports a claim that the intervention, rather than other factors, produced a result? |
| Transparency and accountability | Can people understand why they were targeted and who is responsible for the data and messages, including vendors and platforms? |
These questions distinguish ordinary audience prioritization from practices involving opaque sources, weak consent, or unaccountable personal-data use. Granularity alone does not determine whether a practice is lawful or responsible.
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Privacy rules and democratic concerns vary by jurisdiction
Political data practices do not have one global legal standard. The European Commission identifies microtargeting based on unlawful processing of personal data, highlighted by the Cambridge Analytica revelations, as a distinct electoral concern and points to the General Data Protection Regulation (GDPR) as a source of tools for addressing unlawful political data use. That is not a claim that every form of political targeting is unlawful under the GDPR; the legal assessment depends on the processing and circumstances.
In Canada, Elections Canada discusses political parties’ handling of electors’ personal information as a policy issue and describes how Cambridge Analytica used Facebook data to develop profiles and target messages without users’ knowledge or consent. The rules and obligations applicable to a campaign depend on the jurisdiction and election, so a practice permitted in one context should not be assumed lawful elsewhere.
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A practical governance chain
For campaigns, vendors, and platforms, responsible handling requires more than choosing a model. A useful set of safeguards is to:
- Identify the source of each dataset and the purpose for which it was collected.
- Establish an appropriate lawful basis and provide meaningful notice about political-data use.
- Limit collection, access, retention, and reuse to what is justified.
- Secure personal information and set clear responsibilities for vendors and platform partners.
- Document targeting criteria and make political-ad practices transparent where required.
- Maintain accountability so that responsibility cannot be shifted among the campaign, data provider, vendor, and platform.
These are governance principles, not a substitute for checking the specific legal requirements that apply in a country or election.
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