Brushworks
Brief us

Political strategy and data, at human resolution

Elections are decided by people, not demographics. We work at that scale.

Brushworks is a political intelligence firm. We model how individual voters behave, determine what would move each of them, and put persuasion into the field with a standard of proof — across the United States and in the harder theaters abroad.

  1. 1. The file
  2. 2. Signals resolve
  3. 3. Deciders surface
  4. 4. A program in field
Plate IFour thousand people as the voter file sees them: identical rows, nothing to tell one from another.
500+contests, from presidential to school board, over a decade
96%won, across every level and both parties
$5B+approved in more than a hundred bond and levy measures
270M+American adults and households, resolved and behaviorally scored
12countries of institutional and diaspora work
7offices on a single 24-hour desk
The premise

Most campaigns still buy the electorate the way television networks sold it in 1985: by age, sex, county and party file. Those labels are easy to purchase and easy to defend in a budget meeting. They are also the reason two neighbors with identical demographics receive identical mail and vote in opposite directions.

People do not decide as categories. They decide as individuals, under pressure, with whatever they happened to pay attention to that month. That attention leaves traces — what someone reads, buys, searches, cancels, renews, drives past on the way to work. Read carefully and modeled properly, those traces describe a person far better than the file does: what they are anxious about, what they take pride in, whom they trust, and what they have quietly stopped believing.

Brushworks was built to work at that resolution. We do the research, the modeling, the message architecture and the delivery ourselves, through our own media seats, and we measure every program against a held-out control — so that what we report at the end is what actually happened.

What we doThe method, in five movements

Terrain, audience, message, delivery, proof.

The order matters. Nothing is built before the race is mapped; nothing is written before the deciders are named; nothing is reported that was not measured against a control.

Capabilities in full

  1. I

    Terrain

    Where the race will actually be decided: the precincts, the margin, the money coming against you, and the clock.

  2. II

    Audience

    Deciders identified by what they do rather than by label, and grouped by what would move them.

  3. III

    Message

    A narrative track for each group, built from the anxieties and hopes it already holds and sequenced for the persuasion arc.

  4. IV

    Delivery

    Three registers: precision paid media, organic advocacy, and the ambient campaign. All of it disclosed, all of it through our own seats.

  5. V

    Proof

    Randomized holdouts, daily measurement, and a publish gate no model passes without beating the baseline.

The human layerExhibit 1

One person, seventeen thousand signals.

Our view of the American electorate is assembled from roughly sixty-two billion observed behaviors, resolved to more than 270 million adults and their households, organized into a ten-thousand-topic dictionary, and scored by models trained on real outcomes rather than proxies.

No single signal is decisive. The pattern is. A subscription cancelled, a commute lengthened, a local news habit, a sudden interest in prescription prices — each is noise on its own and evidence together. The graph is rebuilt monthly, digital activity refreshed daily, and none of it touches ethnicity, religion or ideology, which we do not model.

Inside the human layer

Exhibit 1The active signal registry, drawn to scale by domain — roughly 17,000 signals, each line a family of observed behaviors. Ochre marks signals that entered a scored model this month. Grouping illustrative.
From the behavioral lexiconExhibit 2, composites, not individuals

A campaign speaks to millions. It persuades one at a time.

Every other vendor in politics starts from the voter file, because that is what there is to buy: age band, sex, party, precinct, vote history. It is enough to send mail. It is not enough to know what to say. We describe deciders as phenotypes — composite people assembled from thousands of behavioral neighbors — so that a strategist and a copywriter can hold an entire segment in mind as a single person, and so that the person receives a line that was written for her.

Exhibit 2The same voter, two ways. Left, everything the voter file knows about the Night Nurse. Right, what the human layer knows — signals color-coded by domain, six scores, and the track she was assigned. Composite; the file's fields are typical, the signals illustrative.
  • Behavior, not labels.

    Cohorts are built from what people do — sixty-two billion observed events — never from ethnicity, religion or party as a proxy for what they will do.

    The category default: age band, sex, party, precinct

  • Named, and written for.

    Each cohort is a person with a name, a narrative track and a register mix. Copy is written to her; the studio produces the variants; every one carries a disclosure.

    The category default: one message, three demographic cuts

  • Measured against a control.

    Every track is judged against a randomized holdout. A line that moves the Steward and suppresses the Pharmacist is caught in week two, not guessed at in November.

    The category default: a post-election poll

The Steward

n., first-ring suburb

Homeowner, twenty years at the same employer, runs the parish fish fry. Believes the deal has been broken by people who never had to keep one.

Reads as
Order, obligation, the fair deal
Moves on
Cost-of-living arithmetic told with respect, not alarm

"The people who wrote the last budget never had to keep one."

The Splitter

n., late-deciding

Has voted for both parties in the last four cycles and resents being told what she is. Decides in the final ten days, mostly on character.

Reads as
Independence
Moves on
The single credible detail, not the platform

"Two votes, two dates, on the record. Judge them yourself."

The Night Nurse

n., shift worker

Mid-career, watches the news on a phone between rounds, has no patience for anyone who has not done a hard job under pressure.

Reads as
Competence
Moves on
Proof that someone has carried weight and kept their head

"She ran a trauma floor for eleven years. You'll hear she has never run anything."

The Renter

n., downtown, under 35

Three employers on the résumé, a lease that resets every twelve months, and a commute that has quietly become the central fact of life.

Reads as
Momentum
Moves on
A candidate who names the rent and the commute in the first sentence

"Your lease resets in March. Your rent didn't ask permission."

The Pharmacist

n., exurban professional

Meticulous about sources, allergic to slogans, and will read the footnote if there is one. Trusts the messenger who shows the working.

Reads as
Skepticism
Moves on
The claim with its evidence attached

"Here is the ratio she wrote, the hospital that adopted it, and the audit that followed."

The Contractor

n., rural-exurban, self-employed

Runs a crew of four, bids every job against someone cheaper, and keeps his own books at the kitchen table on Sunday nights.

Reads as
Self-reliance
Moves on
The rule that cost him a week, named specifically

"Section 4 cost you a week in March. Here is who wrote it and who voted to repeal it."

Commerce Media Civic Household Health and work Bars show the signal domains that define each composite

The recordExhibits 3 to 5

Ninety-six in a hundred.

Across more than five hundred contests — presidential, gubernatorial, Senate and House, ballot measures, and over a hundred bond and levy elections — the programs we have designed have won ninety-six percent of the time, for candidates and causes across the spectrum. Client names are held under agreement. The methods and the margins are not.

Open the casebook

Exhibit 3Five hundred contests, one dot each. Ochre, won; bone, lost. Drawn from the firm's record across a decade; positions of the losses are schematic.
OfficeMethodNote
PresidentialHyper-localization of a national narrativeA 2016 national campaign; the message rewritten county by county for the places that decided it.
GubernatorialBehavioral targeting across three distinct strategiesThree competitive states, three different theories of the race, one measurement standard.
U.S. SenateThe neglect algorithmPersuadable voters both parties had stopped contacting, found and moved.
U.S. HouseGrassroots amplificationSmall-dollar and volunteer growth engineered from behavioral affinity, not lists.
Ballot measuresSimplifying complexityA 2022 California measure: 65 percent support at launch, 17.7 percent at the ballot.
Bonds and leviesConcrete, not abstractMore than a hundred measures; more than five billion dollars approved by voters raising their own taxes.
Committees and NGOsThe permanent campaignTrigger-based activation between cycles, so the audience is warm when it matters.
Exhibit 4The record, by office. Each method is a case in the casebook.
Exhibit 5Support for a 2022 California ballot measure. The launch figure is the campaign's baseline polling; the result is the official statewide vote of November 8, 2022 (17.7 percent yes). The intervening trajectory is drawn to scale between them. Program in field from the second month.
CasebookThree of nine

What the method did when it met a real race.

  • Simplifying complexity

    A California measure whose text ran to dozens of pages, at 65 percent support, defeated with 17.7 percent of the vote — one true sentence per voter.

  • The neglect algorithm

    The persuadable, likely voters both parties had stopped calling, found by cross-referencing contact history with receptivity.

  • Ten million, five countries

    A diaspora treated as the constituency it is, across the United States, the United Kingdom, Canada, the UAE and Australia.

All nine cases

AbroadExhibit 6

The harder theaters.

The same discipline travels. Our people have run engagement, security and narrative programs for governments and institutions in the Gulf, South Asia, Southeast Asia and West Africa, and diaspora programs in five countries at once. Seven offices sit on a single desk, so a mandate in Riyadh and a race in Michigan are watched by someone who is awake.

Where we have worked

Diaspora as an electorate. A ten-million-person community spread across five countries is a constituency, whether or not anyone treats it as one. We do.

Featured readFrom the Brushworks Review
Also in the ReviewAll twenty-four papers
    The rules we work byFive things we will not do

    Five things we will not do.

    Written into every engagement letter, and the first page a donor's counsel or a ministry's compliance office is shown.

    I

    Target on identity

    We model behavior only. Ethnicity, religion and ideology are never modeled, never bought, and never accepted as a filter.

    So the Steward can be any color and any faith.

    II

    Hide the messenger

    Paid advocacy is labeled. We do not operate undisclosed personas or accounts, on any platform, in any language, for any client.

    So the second voice is a real one.

    III

    Report without a control

    Every program carries a randomized holdout sealed on day five, and every model beats a demographic and a look-alike baseline before it ships.

    So the number in the report is the number that happened.

    IV

    Cross a wall

    Independent-expenditure work is separated from candidate work by structure — teams, systems and seats. Foreign work is registered before it begins.

    So a donor's counsel can certify the posture, not take our word for it.

    V

    Run what we could not describe

    If a program could not be explained on the record, in public, in the client's name and ours, we do not run it. Mandates that fail the test are declined in writing.

    So there is nothing to find.

    WP-14 — Data

    What a signal is (and isn’t)

    Sixty-two billion observed behaviors, ten thousand topics, one rule: an event, not an opinion.

    By Brushworks research deskSeptember 2026Working paper

    Abstract. We define the signal as the unit of behavioral intelligence — an observed event resolved to a person or household, with a topic, a date and a source — and distinguish it from survey answers, inferred attributes and modeled labels. The paper sets out the registry, the dictionary, the refresh cadence, and the curation rule that keeps the registry small.

    In brief

    The finding
    A signal is a recorded act, with a time, shown to predict a later act. Modeled attributes, survey answers and file categories are data of other kinds, and a registry that admits them as signals is a registry that has stopped testing.
    For a campaign
    Ask any vendor whether a field was observed, when, and what it predicted; ask what has been pruned. A registry that has never removed a signal has never tested one.
    For a government or institution
    Institutions buying data should demand the same labels — observed, inferred, modeled — on every field, and treat a source that cannot supply them as an estimate.

    The word "data" does most of its work by hiding distinctions. A search, a modeled income, a poll answer and a party code are all data, and only one of them is a signal in the sense that matters to a program: a recorded act, with a time, that has been shown to predict another act. This paper is a definition, and a defense of why the definition is narrow. Sixty-two billion observed behaviors, ten thousand topics, seventeen thousand active signals: the numbers are large because the acts are many, and the registry is small because the definition is strict.

    Four things that get called data

    A recorded act is something a person did — subscribed, searched, renewed, visited, cancelled, donated — with a date and a source. It is observed, not inferred; it is timed; it repeats or does not; and whether it predicts a later act is a testable question. A modeled attribute is an estimate — income, education, household composition — produced by a vendor's model from other data; it is neither observed nor timed, and it is precisely as good as the model that made it, which the buyer usually cannot see. A survey answer is a self-report at one moment from a small, self-selected sample; it is valuable for what it is and useless as a signal about the millions who were not asked. A category from the file — party, age band, precinct — is a public record that describes the average of everyone who shares it.

    Four things called data, and what each is. Only the recorded act passes every test. The modeled attribute enters the graph labeled as what it is — six of seven demographic facts in the graph are vendor-modeled and are treated as such — and never as an observed fact.

    From event to signal

    Events enter the graph by the billion and are organized by a dictionary of roughly ten thousand topics in twenty-five domains, and every signal belongs to one of five families. A topic is not yet a signal. A signal is a defined, versioned pattern over events — "overnight sessions three weeks in four," "returned a mail ballot in the first week of the window in each of the last three cycles" — that has been promoted into the registry because it predicted a recorded political act in a prior program, against a holdout, better than the file did. Promotion is earned; retention is earned again every month, and a signal that stops predicting is pruned and logged. The registry is curated toward prediction, and volume is not the point; a registry that stays small is a registry that stays honest.

    The life of a signal: from a recorded act, through the dictionary, to a defined and versioned candidate, to promotion on predictive record, to pruning when the record fails. The loop matters: a pruned signal can be re-tested, but it cannot be quietly kept.

    Time is half the signal

    A signal without a time is an attribute. Intent signals — a search, a page read to the end — decay within days; their value is in their recency, and a program that acts on last quarter's searches is acting on noise. Cadence signals — a renewal pattern, a subscription rhythm — hold their value for months and describe a disposition rather than a moment. Tenure signals — years at an address, at an employer — grow in value with age; they describe stability, and stability predicts participation. Feature engineering in the catalog treats the three differently, with recency and decay windows fitted per family, and the models are retrained on the daily refresh so that intent is used while it is warm.

    How three kinds of signal hold their predictive value with age, illustrative. Intent decays in days; cadence holds for months; tenure appreciates. A registry that treated them alike would waste the first and misread the third.
    What is not in the registry, by construction. Ethnicity, religion and ideology are not events; they are categories; and they are absent from the dictionary, the registry and every model. The exclusion is enforced where the signals are defined, not where they are used, so that no downstream policy has to remember it.

    Labeling what we do not observe

    The graph carries facts of three kinds and labels each: observed, from a recorded act; inferred, from a rule over observed acts; and modeled, from a vendor's or our own model. Every score a program uses carries the label of its weakest input, so that a strategist reading a cohort's dossier knows whether the household-composition line is something the graph saw or something it estimated. This is unglamorous and it is the difference between a data firm and a story firm. Six of seven demographic facts in the graph are modeled, at fill rates a buyer can ask for; the behavioral feed is observed and household-attributed where the source is; and we say so.

    Implications

    • Ask whether it was observed. If a vendor cannot say which of its fields are recorded acts and which are estimates, treat all of them as estimates.
    • Ask when. A signal without a timestamp cannot be decayed, and a signal that is not decayed is being used at the wrong weight.
    • Ask what it predicted. A signal earns its place by predicting an act against a holdout; the registry should be able to show the record.
    • Ask what was pruned. A registry that has never removed a signal has never tested one.

    Notes and references

    1. Gebru, T., et al. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92 — on documenting what a dataset observed and how.
    2. Goodhart, C. (1975), as formulated by Strathern, M. (1997), "When a measure becomes a target, it ceases to be a good measure" — the reason a registry is pruned on prediction rather than grown on volume.
    3. Ansolabehere, S., and Hersh, E. (2012). Validation: what big data reveal about survey misreporting and the real electorate. Political Analysis, 20(4), 437–459 — on the gap between self-report and the record.
    4. Brushworks data use policy, §1 (provenance, minimization) and §5 (the provenance standard); Review WP-24, Household or person.
    Next paperThe Review

    Brief us.

    Tell us the race, the calendar and the margin. Within a week we will tell you whether we can move it and what it would take. Candidate work and independent-expenditure work are taken by separate teams; say which you are. Governments and institutions: tell us the mandate and the jurisdiction, and we will tell you what registration it requires before we say anything else.

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