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-24 — Data

    Household or person

    When attribution matters and when it doesn’t: the honest answer to the question every diligence call asks.

    By Brushworks research deskSeptember 2026Working paper

    Abstract. Some behavior belongs to a household and some to a person, and a data firm that claims otherwise is selling. We set out how the graph labels each, where household attribution is enough — mail, connected television — and where it is not, and what the labels cost in accuracy when they are ignored.

    In brief

    The finding
    Much of the world’s recorded behavior is bound to an address, not a person; the graph labels every event and every score with its level, uses household attribution where the channel reaches a household, and person-level data where the line and the hour are the program.
    For a campaign
    Match the attribution level to the channel — household for mail and television, person for mobile, search and relational — and ask any vendor which scores carry which label.
    For a government or institution
    A ministry buying data should ask what level each source resolves to and treat a vendor that says “the individual” for everything as one that has not looked.

    Every diligence call arrives, eventually, at the same question: is this data about a person or about a household? The honest answer is that some of it is one and some of it is the other, that the graph knows which, and that a data firm claiming otherwise is selling. The behavioral feed is largely household-attributed, because much of the world's recorded behavior arrives bound to an address rather than to an individual. Some sources resolve to a person; many do not; and the models are built to know the difference. This paper sets out how the graph labels each, where household attribution is enough, where it is not, and what the labels cost in accuracy when they are ignored.

    What resolves to what

    Identity resolution matches a record to the graph on the keys it carries — hashed email, phone, postal address, device — and the keys determine the level. An authenticated, device-bound session resolves to a person. A purchase shipped to an address, a subscription billed to a household, a mail response, a utility account resolve to a household, and a rule that assigned them to an individual would be inventing a fact. The graph labels every event with its level and every derived score with the level of its weakest input, so that a strategist reading a cohort dossier knows whether "watches late-night news on mobile" is something the graph saw a person do or something it inferred for a household.

    When household is enough

    For a large share of a political program the household is the right unit. Mail reaches an address. Connected television reaches a living room. Out-of-home reaches a neighborhood. The dispositions that these channels carry — cost-of-living anxiety, tenure, local news habit — are household properties as much as personal ones, and a program that scores the household for a mailer is scoring the thing the mailer will reach. Turnout propensity is only weakly degraded by household attribution, because the signals that predict it — tenure, rootedness, civic activity — are largely shared across a household.

    Which channels reach a household and which reach a person, whether household attribution is enough for each, and what a person-level label is needed for. The channels that most need person-level data are the ones where the line and the hour are the program.

    When it is not

    Mobile video, search and relational contact reach a person, and the things that make them work — the line written for her, the hour she is awake, the intent she just expressed — are personal. The Night Nurse's receptivity window, midnight to two-thirty on shift nights, is meaningless as a household property; her husband is asleep. Persuadability is more personal than turnout, because it depends on what the person, not the address, attends to. A model that treats household-level signals as personal for these outcomes will be confident and wrong, and the cost is measurable.

    The cost in model discrimination of ignoring attribution labels, by outcome, illustrative of the catalog's own tests. Turnout and the household channels barely notice; the receptivity window and mobile video lose most of their edge. The labels are cheap and their absence is not.
    Six of seven. Six of the seven demographic facts in the graph are vendor-modeled and are treated as modeled, at fill rates a buyer can ask for. The behavioral feed is observed and, where the source is household-bound, household-attributed. We say so on the dossier and in the diligence call, because the alternative is to be caught saying otherwise.

    How the models use the labels

    Attribution level is a feature, not a footnote. Models for household channels are trained on household-level signals and score the household; models for person channels are trained on person-level signals with household signals admitted only as context, at lower weight; and every score carries the label of its weakest input so that the seat matching it knows what it is matching. The publish gate tests each model on its own channel's outcome, so a persuadability model that leans on household signals for a mobile channel will fail against the baseline and be logged for it.

    Implications for a buyer

    • Ask what level each source resolves to. A vendor that answers "the individual" for everything has not looked.
    • Match the level to the channel. Household for mail and television; person for mobile, search and relational.
    • Ask which scores carry which label. A receptivity window scored from household data is a guess.
    • Prefer the firm that tells you. The honest answer is more useful than the flattering one, and it survives diligence.

    Notes and references

    1. Gebru, T., et al. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
    2. Hersh, E. D. (2015). Hacking the Electorate. Cambridge University Press — on the limits of commercial data as campaigns receive it.
    3. Brushworks Review WP-14, What a signal is (and isn't); data use policy, §1 (provenance) and §5 (the provenance standard).
    4. Source shares and discrimination costs are illustrative of the catalog's own attribution tests; program-level figures are reported to clients.
    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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