Cory Booker US Inactive
CORY BOOKER FOR SENATE
Please add your name
Ad copy
Please add your name as a citizen cosponsor of my FDA Modernization Act to urge the House to pass my legislation banning mandatory animal testing so President Biden can sign it into law. Here’s why: It will not only help to get rid of undue, burdensome regulations for companies engaging in practices that are not necessary, it will save thousands, if not hundreds of thousands, of animals from being tortured and killed and, importantly, better affirm human health. So please, add your name and join me in speaking up on this important issue!
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 50.1/100 |
| Spend / Reach / Long. / Eff. | 27.6 · 70.7 · 35.8 · 66.4 |
| Spend range | USD 600–699 |
| Impressions | 50,000 – 59,999 |
| CPM (≈ $/1k impr) | USD 11.81 |
| Est. audience size | — |
| Days live | 131 (2022-10-27 → 2023-03-07) |
| Created | 2022-10-27 |
| Creative variants | 1 body · 1 headline |
| Platforms | |
| Languages | en |
| Candidate | NJ — Cory Booker |
| Surfaced by | “senate:Cory Booker” |
🧠 AI analysis
| Issue | other |
| Framing | issue advocacy |
| Message type | list building survey |
| Emotional appeal | urgency |
| Production tier | semi pro |
| Who pictured | candidate |
| Symbols | none |
| Call to action | sign petition |
| Standout element | bold black call-to-action text on a soft pink gradient background commanding immediate action |
| Tactic | The ad uses a citizen cosponsor framing to drive list-building sign-ups, coupling moral urgency around animal welfare with a deregulation angle to broaden ideological appeal. |
Where it ran (delivery by region)
| California | 12.2% |
| New York | 8.0% |
| Pennsylvania | 6.7% |
| Florida | 6.4% |
| Texas | 5.0% |
| New Jersey | 4.6% |
| Ohio | 4.6% |
Who saw it (delivery)
| 18-24 · unknown | 0.4% |
| 35-44 · female | 18.5% |
| 35-44 · male | 1.9% |
| 45-54 · male | 2.1% |
| 25-34 · female | 15.9% |
| 55-64 · female | 16.6% |
| 45-54 · female | 18.2% |
| 65+ · female | 13.5% |
| 18-24 · female | 4.6% |
| 25-34 · unknown | 0.6% |
| 25-34 · male | 1.8% |
| 55-64 · male | 2.3% |
| 45-54 · unknown | 0.3% |
| 65+ · male | 1.9% |
| 18-24 · male | 0.5% |
| 35-44 · unknown | 0.4% |
| 55-64 · unknown | 0.1% |
| 65+ · unknown | 0.2% |
