Jordan Wood US Inactive
JORDAN FOR MAINE
Will you chip in to help me defeat Susan Collins?
Can I count on you? >>
Ad copy
I'm officially running to replace Susan Collins in the Senate. I’ve spent my career battling corruption, standing up to election deniers, and holding powerful Wall Street CEOs accountable. I promise you this: In the Senate, I’ll stand up to Donald Trump’s lawlessness, and fight for Maine. I’ll have your back every single day – unlike Susan Collins. I really hate asking for money, but our campaign is just getting started. I’m not taking any money from corporate PACs or lobbyists – so I need your help. I would be so grateful for any amount you can spare to help get my campaign off the ground and give our movement the chance it deserves to succeed.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 35.3/100 |
| Spend / Reach / Long. / Eff. | 11.9 · 47.5 · 21.5 · 60.2 |
| Spend range | USD 100–199 |
| Impressions | 5,000 – 5,999 |
| CPM (≈ $/1k impr) | USD 27.18 |
| Est. audience size | 100,001 – 500,000 |
| Days live | 79 (2025-06-11 → 2025-08-29) |
| Created | 2025-06-11 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | ME — Jordan Wood |
| Surfaced by | “senate:Jordan Wood” |
🧠 AI analysis
| Issue | democracy elections |
| 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 | Desaturated grayscale photo of Susan Collins contrasted against a vivid full-color photo of Jordan Wood, visually subordinating the opponent |
| Tactic | Classic contrast fundraising appeal that pairs a desaturated opponent with a confident candidate image and urgent 'Chip In Now' CTA to drive founding-donor urgency among small-dollar progressives. |
Where it ran (delivery by region)
| Maine | 100.0% |
Who saw it (delivery)
| 25-34 · female | 1.5% |
| 25-34 · male | 1.3% |
| 25-34 · unknown | 0.2% |
| 35-44 · female | 4.0% |
| 35-44 · male | 2.5% |
| 35-44 · unknown | 0.3% |
| 45-54 · female | 3.9% |
| 45-54 · male | 3.7% |
| 45-54 · unknown | 0.1% |
| 55-64 · female | 13.3% |
| 55-64 · male | 8.5% |
| 55-64 · unknown | 0.4% |
| 65+ · female | 35.4% |
| 65+ · male | 24.2% |
| 65+ · unknown | 0.8% |
