Janet Mills US Inactive
Mills for Maine
🚨need your endorsement
Maine Gov. Janet Mills just announced her Democratic campaign for U.S. Senate to defeat Susan Collins. Janet needs 10,000 signatures endorsing her campaign to defeat Susan Collins and flip the Senate majority. Will you add your name now? >>
Ad copy
Please, don’t scroll away. Gov. Janet Mills here. I just jumped into Maine’s U.S. Senate race. I’m hoping to collect 50,000 signatures endorsing my campaign to defeat Susan Collins and flip the Senate majority. That’s why I’m personally asking: Will you endorse my grassroots campaign for U.S. Senate?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 46.1/100 |
| Spend / Reach / Long. / Eff. | 41.0 · 75.1 · 8.0 · 60.4 |
| Spend range | USD 2,000–2,499 |
| Impressions | 80,000 – 89,999 |
| CPM (≈ $/1k impr) | USD 26.46 |
| Est. audience size | — |
| Days live | 30 (2025-11-21 → 2025-12-21) |
| Created | 2025-11-21 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | ME — Janet Mills |
| Surfaced by | “senate:Janet Mills” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | positive candidate |
| Message type | list building survey |
| Emotional appeal | urgency |
| Production tier | semi pro |
| Who pictured | none |
| Symbols | us flag |
| Call to action | sign petition |
| Standout element | Urgent midnight deadline with clock emoji on bold green background drives immediate action |
| Tactic | Classic list-building urgency tactic using a fake-personal ask, a hard deadline, and a signature-collection framing to harvest email leads and build grassroots credibility simultaneously. |
Where it ran (delivery by region)
| Florida | 9.0% |
| California | 7.6% |
| Maine | 5.1% |
| New York | 5.0% |
| Ohio | 4.7% |
| Texas | 4.7% |
| Pennsylvania | 4.4% |
Who saw it (delivery)
| 18-24 · female | 0.5% |
| 45-54 · male | 1.9% |
| 65+ · male | 31.9% |
| 65+ · female | 41.3% |
| 55-64 · unknown | 0.1% |
| 55-64 · male | 7.7% |
| 55-64 · female | 9.1% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 2.1% |
| 18-24 · male | 0.5% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 0.9% |
| 35-44 · female | 1.1% |
| 25-34 · unknown | 0.1% |
| 25-34 · male | 0.9% |
| 25-34 · female | 1.1% |
| 18-24 · unknown | 0.1% |
| 65+ · unknown | 0.5% |
