Jennifer Jenkins For Florida US Inactive
Jennifer Jenkins For Florida
MAGA is terrified!
Ad copy
To flip the Senate blue, we need to flip Florida. My name is Jennifer Jenkins, and I’m a Democrat running for U.S. Senate to defeat our unelected MAGA Senator Ashley Moody. Elections across the country are showing that Democrats have the momentum! But, we HAVE to carry that momentum into 2026 and hit every single one of our fundraising goals to win this race. Right now, we’re about $17,200 short of our end-of-year goal. Can you be one of the supporters who steps up with a $5 donation today to help me flip a critical Senate seat?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 25.6/100 |
| Spend / Reach / Long. / Eff. | 11.9 · 34.4 · 5.5 · 50.5 |
| Spend range | USD 100–199 |
| Impressions | 1,000 – 1,999 |
| CPM (≈ $/1k impr) | USD 99.7 |
| Est. audience size | 5,001 – 10,000 |
| Days live | 21 (2025-12-10 → 2025-12-31) |
| Created | 2025-12-10 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | FL — Jennifer Jenkins |
| Surfaced by | “senate:Jennifer Jenkins” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | positive candidate |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | semi pro |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | Warm yellow background framing a smiling candidate with a bold blue subtitle strip conveying momentum |
| Tactic | Uses urgency and momentum framing ('MAGA is terrified') paired with a specific dollar shortfall to drive small-dollar end-of-year fundraising from energized base donors. |
Where it ran (delivery by region)
| Florida | 82.2% |
| New York | 1.5% |
| Texas | 1.4% |
| North Carolina | 1.1% |
| California | 1.1% |
| Pennsylvania | 0.9% |
| Maryland | 0.9% |
Who saw it (delivery)
| 18-24 · female | 0.5% |
| 18-24 · male | 0.2% |
| 25-34 · female | 0.6% |
| 25-34 · male | 1.7% |
| 35-44 · female | 1.7% |
| 35-44 · male | 0.9% |
| 45-54 · female | 1.5% |
| 45-54 · male | 3.5% |
| 55-64 · female | 7.4% |
| 55-64 · male | 10.1% |
| 65+ · female | 33.8% |
| 65+ · male | 37.2% |
| 65+ · unknown | 1.2% |
