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-11 — Method

    The Behavioral Lexicon

    Why we describe segments as people. Composite deciders as an instrument of strategy, with six entries.

    By Brushworks research deskSeptember 2026Working paper

    Abstract. Segments described as statistics are impossible to write for. We describe the construction of composite decider phenotypes from behavioral neighbors, the rules that keep them honest — no identity attributes, no individuals — and how a lexicon of a dozen entries becomes the shared picture a strategist and a copywriter work from.

    In brief

    The finding
    A segment is rows; a composite with a name, a paragraph and derived scores is someone a strategist and a writer can both picture. The lexicon is an interpretability device, and every field on it except the name is a number.
    For a campaign
    Write the entry before the message, derive every field from a model or a holdout, test near neighbors with cross-runs, and retire entries when the behavior changes.
    For a government or institution
    Institutions speaking to many publics need the same discipline: a named composite for each, separated from its neighbors by measurement, so that the whole organization holds one picture of whom it is addressing.

    A segment is a set of rows. A person is someone you can write to. The Behavioral Lexicon is the practice of turning the first into the second: composite deciders, assembled from thousands of behavioral neighbors, named and described so that a strategist and a copywriter hold the same picture of who they are speaking to. The Steward, the Splitter, the Night Nurse, the Renter, the Pharmacist, the Contractor and the Certain are not personas in the marketing sense — invented, aspirational, unmeasured. They are the human-readable surface of a model, and everything about them except the name is a number.

    Why a segment needs a name

    Campaign teams do not disagree about strategy as often as they disagree about who the voter is. The pollster's "college-educated suburban women," the data vendor's "persuadable independents" and the copywriter's picture of her aunt are three different people, and the plan is written for a fourth. A composite with a name, a paragraph and a set of scores collapses the three into one object that everyone in the room can point at. The name is not decoration. It is the interface between a model nobody in the room can read and a message everyone in the room has to write.

    The practice has precedents. Market research has used personas for decades, mostly badly, because the persona was invented before the data and survived contact with it by being vague. Machine-learning practice has a growing literature on interpretability — the demand that a model's output be explicable to the people who act on it — and the lexicon is an interpretability device: it explains a cohort to the humans who must write to it. The difference from the persona is that the composite is derived from the cohort's measured behavior and is retired when the behavior changes.

    From behavior to a name. Signals are scored into dispositions; individuals are grouped by what would move them and separated from their near neighbors; the cohort is described as a composite person; the composite is assigned a track and the studio produces its variants.

    The anatomy of a composite

    Every entry in the lexicon carries the same fields, and every field is derived. Defined by names the behavioral signature — tenure for the Steward, sequence for the Splitter, rhythm for the Night Nurse — as a one-line rule a reader could apply. Built from lists the signal families and counts that define the cohort in the registry. Reads as and moves on summarize the dispositions the models score highest and the frame that has moved the cohort against a holdout. The attack she will hear is the forecast. Receptivity window is when, by hour and day, contact lands. Lands and misses give the line that moved the cohort and the category default that did not. And do not cross-run names the neighbor cohort's line that suppresses this one — the field the persona never had, because the persona was never measured.

    The Night Nurse, built from: sixty-one signals across five families, each versioned in the registry. Behavioral events — the overnight session rhythm, weekday-weighted, with no weekend mirror — carry most of the definition; occupational signals carry the least, because the cohort is defined by what she does, not by the job title the file might hold.

    Near neighbors and the cross-run test

    The hardest part of a lexicon is not naming cohorts. It is separating them. Two cohorts that share most of their signals are one cohort with a flattering split; two that respond to the same lines are one audience with two names. The separation test is a cross-run: each cohort's best line is delivered to its neighbors against a holdout, and the cohorts are kept apart only if the lines do not travel. Where a line suppresses the neighbor, the suppression is recorded on both entries and enforced at the seat. Where it merely fails to help, the cohorts are candidates for merging.

    Cross-run results between near-neighbor cohorts from a statewide program: the change in response probability when one cohort's best line is delivered to another, against holdouts. The Night Nurse line helps the Night Nurse and does nothing for the Pharmacist; the Steward's tenure line and the Renter's momentum line actively suppress their neighbors. Illustrative; intervals in the client report.
    Composites, not individuals. A lexicon entry describes a cohort's center; no entry describes a person, and no program contacts a cohort of one. The Night Nurse is a hundred and eighteen thousand people in one state and a different hundred thousand in another. The name travels; the people do not.

    The life of an entry

    Entries are born from a segment map and die with it. A cohort that stops separating from its neighbors is merged; one whose defining signals stop predicting response is retired; one that a new program's holdout shows responding differently is rewritten. The lexicon for a statewide race in one cycle is not the lexicon for the same state four years later, and it is not the lexicon for a diaspora program in five countries, though the method is. What persists is the discipline: a cohort earns a name by being measurably different from its neighbors, and keeps it only while that remains true.

    Implications

    • Write the entry before the message. A track written for a cohort nobody in the room can describe is written for the copywriter's aunt.
    • Derive every field. If a field on the entry did not come from a model or a holdout, it is a persona, and it will be wrong in the way personas are.
    • Test the neighbors. The cross-run matrix is the lexicon's proof of separation, and the suppression list is its most valuable output.
    • Retire on evidence. An entry that has stopped predicting is a story. Kill it and log it.

    Notes and references

    1. Doshi-Velez, F., and Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv:1702.08608 — on interpretability as a requirement of models that humans act on.
    2. Mitchell, M., et al. (2019). Model cards for model reporting. Proceedings of FAT* 2019, 220–229.
    3. Cooper, A. (1999). The Inmates Are Running the Asylum. Sams — the origin of the design persona, and, by contrast, its limits.
    4. Brushworks Review WP-05, Behavior, not identity; WP-06, A standard of proof. Cohort figures are composites from Brushworks statewide programs; cross-run results are from register-level holdouts.
    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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