Jennifer Jenkins For Florida US Inactive
Jennifer Jenkins For Florida
NEW Poll 📊
Ad copy
🚨𝐇𝐔𝐆𝐄 𝐍𝐄𝐖𝐒: “Democrats flip Miami mayor’s office, winning control for first time in nearly 30 years.” Democrats are flipping seats across the country – including in Miami for the first time in DECADES! It’s clear: voters are tired of Trump’s MAGA nonsense. Momentum shows we can flip the Florida Senate seat blue and deliver a Democratic Majority to stop Trump’s radical agenda. Will you rush $5 now to help us win this race?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 33.2/100 |
| Spend / Reach / Long. / Eff. | 17.4 · 49.2 · 8.5 · 57.6 |
| Spend range | USD 200–299 |
| Impressions | 6,000 – 6,999 |
| CPM (≈ $/1k impr) | USD 38.39 |
| Est. audience size | 100 – 1,000 |
| Days live | 32 (2025-12-22 → 2026-01-23) |
| Created | 2025-12-22 |
| 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 | Bold news-headline hook citing Miami mayoral flip to manufacture momentum and urgency |
| Tactic | Leverages a real electoral win as social proof to create a bandwagon effect, positioning a small $5 ask as joining a winning wave against Trump — a classic urgency-plus-validation fundraising tactic. |
Where it ran (delivery by region)
| Florida | 84.6% |
| California | 1.6% |
| Texas | 1.4% |
| New York | 1.3% |
| Georgia | 0.8% |
| North Carolina | 0.7% |
| Michigan | 0.7% |
Who saw it (delivery)
| 18-24 · female | 0.1% |
| 45-54 · male | 4.9% |
| 65+ · male | 39.0% |
| 65+ · female | 25.9% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 13.8% |
| 55-64 · female | 7.6% |
| 45-54 · unknown | 0.0% |
| 45-54 · female | 2.3% |
| 18-24 · male | 0.2% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 2.2% |
| 35-44 · female | 1.4% |
| 25-34 · unknown | 0.0% |
| 25-34 · male | 0.9% |
| 25-34 · female | 0.7% |
| 65+ · unknown | 0.8% |
