Jennifer Jenkins For Florida US Inactive
JENNIFER JENKINS FOR U.S. CONGRESS
people-powered campaign
Join the fight.
Ad copy
My name is Jennifer Jenkins. I'm running for Congress to defeat MAGA Randy Fine. Our campaign is counting on grassroots donations to reach our goal, combat MAGA billionaire attack ads, and flip this seat blue. If you're with me, will you please chip in a donation of $25, $10, or anything you can before our critical deadline? >>
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 29.2/100 |
| Spend / Reach / Long. / Eff. | 11.9 · 43.0 · 5.2 · 56.8 |
| Spend range | USD 100–199 |
| Impressions | 3,000 – 3,999 |
| CPM (≈ $/1k impr) | USD 42.72 |
| Est. audience size | 10,001 – 50,000 |
| Days live | 20 (2026-03-28 → 2026-04-17) |
| Created | 2026-03-27 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | FL — Jennifer Jenkins |
| Surfaced by | “senate:Jennifer Jenkins” |
🧠 AI analysis
| Issue | candidate character bio |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | candidate, opponent |
| Symbols | none |
| Call to action | donate |
| Standout element | Split blue-vs-red panel with labeled contrasts — 'Working Mom / Defends working families' vs 'Former Casino Executive / Defends the rich' — visually encoding the ideological battle |
| Tactic | Classic contrast-frame fundraising tactic pairs a flattering candidate photo on blue against an unflattering opponent photo on red, using punchy label copy to define the stakes and drive an urgent $10 donation ask. |
Where it ran (delivery by region)
| Florida | 83.5% |
| New York | 1.2% |
| California | 1.2% |
| Illinois | 1.2% |
| New Jersey | 1.0% |
| Virginia | 0.9% |
| Massachusetts | 0.9% |
Who saw it (delivery)
| 18-24 · female | 0.2% |
| 18-24 · unknown | 0.1% |
| 25-34 · female | 0.9% |
| 25-34 · male | 0.5% |
| 35-44 · female | 3.8% |
| 35-44 · male | 1.2% |
| 35-44 · unknown | 0.2% |
| 45-54 · female | 4.6% |
| 45-54 · male | 3.6% |
| 45-54 · unknown | 0.1% |
| 55-64 · female | 8.0% |
| 55-64 · male | 9.2% |
| 55-64 · unknown | 0.1% |
| 65+ · female | 33.2% |
| 65+ · male | 32.9% |
| 65+ · unknown | 1.4% |
