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-22 — Data

    Turnout is a behavior

    Vote history predicts vote history. Behavior predicts the rest — the first-time voter, the lapsed one, the one who moved.

    By Adam SyedSeptember 2026Working paper

    Abstract. Turnout models built on the file cannot see the people the file has never recorded. We describe a turnout propensity model trained on recorded participation and behavioral signals, and its performance on first-time, lapsed and relocated voters against a file-only baseline.

    In brief

    The finding
    The file predicts vote history from vote history and fails exactly where mobilization matters — the sporadic, the lapsed, the moved, the newly eligible. Behavior, especially rootedness and attention, predicts participation where the file cannot.
    For a campaign
    Train on validated participation, contact the middle of the propensity distribution where rootedness is high, mobilize at the ballot, and hold out by voter type.
    For a government or institution
    Governments seeking participation — in a census, a program, a consultation — face the same problem: the record is missing for the people participation is for, and behavior fills it.

    The voter file's turnout model has one great virtue and one fatal flaw, and they are the same thing: it predicts vote history from vote history. For the consistent voter it is nearly perfect and nearly useless, since she will vote whether or not anyone contacts her. For the sporadic voter, the lapsed voter, the voter who moved and the voter who has never been eligible before — the people mobilization exists for — the file's history is precisely what is missing. Turnout is a behavior, and like other behaviors it is predicted by behavior. This paper describes a turnout model trained on recorded participation and behavioral signals, and its performance where the file cannot see.

    What the file's model sees

    A file-only turnout model is a function of past participation, registration date, party and precinct, and it discriminates well on the population that has a participation history. Its discrimination collapses as the history thins: the sporadic voter with two of six general elections, the lapsed voter with none in the last three, the voter who registered last year in a new county, the eighteen-year-old. These are not edge cases. In a competitive state they are a third of the electorate, and they are the third whose participation a campaign can actually change.

    Discrimination of a turnout model by voter type, for a file-only model and a model that adds behavioral signals, illustrative of pooled program results. The models are indistinguishable among consistent voters and diverge sharply where the file's history is thin.

    What predicts participation when history cannot

    The behavioral signals that predict turnout among the sporadic are, mostly, signals of rootedness and attention. Tenure — years at an address, at an employer, in the institutions a person belongs to — predicts participation more strongly than any file field for voters who lack a history, which is why Gerber, Green and Larimer's social-pressure mailers worked: they addressed the person as a member of a neighborhood that would notice. Civic and community activity, local news habit, household composition and rhythm follow. Commerce cadence contributes a little. None of it is identity, and none of it is available from the file.

    Behavioral families by relative weight in a turnout model for sporadic and first-time voters, illustrative. Rootedness and attention do most of the work; the file's own fields, absent for these voters by definition, contribute nothing.

    The model

    The turnout model in the catalog is trained on validated participation — the state's own record of who voted, matched to the graph — rather than on self-report, which overstates turnout by double digits, as Ansolabehere and Hersh showed. It is time-split so that it is tested on later elections than it was trained on, gated against the file-only baseline on each client's holdout, and labeled with its confidence. Its output is a probability, not a verdict: a person at 0.52 is not "unlikely" but uncertain, which is the population a mobilization contact is for.

    The two-score decision. Turnout propensity alone does not decide whom to contact; it decides whom contact could affect. A voter at 0.9 needs no contact; a voter at 0.1 will not respond to one. The mobilization budget goes to the middle — and, within the middle, to the people whose behavior says they are rooted enough to answer.

    Measured lift

    Mobilization contact measured against sealed holdouts produces the pattern the model predicts. Consistent voters barely move, because they were voting anyway. Sporadic and lapsed voters move four to five points; recently moved and first-time eligible voters three to four — the smallest lifts among the groups the file cannot see, and still several times the lift among the group it can. The pooled field-experimental literature — Gerber and Green's canvassing experiments, Nickerson and Rogers' plan-making, the social-pressure and social-network studies — puts the achievable range in the same territory, and our programs are designed so that the lift is spent where it exists.

    Turnout lift from mobilization contact by voter type, treated minus holdout, pooled across programs and illustrative. The contact that reaches consistent voters is wasted; the contact that reaches the sporadic and the lapsed is the program.

    Implications

    • Do not let the file score the sporadic. Its history is missing for them; its model is guessing.
    • Train on validated participation. Self-report is wrong by more than the effect you are trying to measure.
    • Contact the middle. Propensity near one half, rootedness high, is the mobilization cohort.
    • Mobilize at the ballot. The contact that works is attached to the act — the arriving mail ballot, the open early site.
    • Hold out by voter type. The claim that the model finds voters the file cannot is testable, and should be tested every program.

    Notes and references

    1. Ansolabehere, S., and Hersh, E. (2012). Validation: what big data reveal about survey misreporting and the real electorate. Political Analysis, 20(4), 437–459.
    2. Gerber, A. S., and Green, D. P. (2000). The effects of canvassing, telephone calls, and direct mail on voter turnout. American Political Science Review, 94(3), 653–663.
    3. Gerber, A. S., Green, D. P., and Larimer, C. W. (2008). Social pressure and voter turnout. American Political Science Review, 102(1), 33–48.
    4. Nickerson, D. W., and Rogers, T. (2010). Do you have a voting plan? Psychological Science, 21(2), 194–199.
    5. Green, D. P., and Gerber, A. S. (2019). Get Out the Vote: How to Increase Voter Turnout, 4th ed. Brookings — the pooled record of mobilization experiments.
    6. Program figures are pooled and rounded from Brushworks programs with sealed holdouts by voter type; the turnout model is gated against a file-only baseline on each client's holdout.
    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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