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 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 | 45.1/100 |
| Spend / Reach / Long. / Eff. | 38.3 · 72.4 · 9.4 · 60.3 |
| Spend range | USD 1,500–1,999 |
| Impressions | 60,000 – 69,999 |
| CPM (≈ $/1k impr) | USD 26.92 |
| Est. audience size | — |
| Days live | 35 (2026-01-09 → 2026-02-13) |
| Created | 2026-01-09 |
| 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 | Emoji-laden green text card with countdown urgency and a direct ask from the candidate herself |
| Tactic | Classic grassroots list-building tactic using artificial scarcity (midnight deadline, specific signature goal) and a personal first-person ask to manufacture momentum and collect supporter emails. |
Where it ran (delivery by region)
| California | 9.4% |
| Florida | 8.5% |
| New York | 8.0% |
| Pennsylvania | 5.5% |
| Ohio | 4.1% |
| Michigan | 4.1% |
| Maine | 4.0% |
Who saw it (delivery)
| 18-24 · female | 0.1% |
| 45-54 · male | 1.0% |
| 65+ · male | 24.7% |
| 65+ · female | 55.3% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 4.4% |
| 55-64 · female | 9.0% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 2.1% |
| 18-24 · male | 0.1% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 0.5% |
| 35-44 · female | 0.9% |
| 25-34 · unknown | 0.0% |
| 25-34 · male | 0.3% |
| 25-34 · female | 0.7% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.7% |
