Jordan Wood US Inactive
JORDAN FOR MAINE
CHIP IN TO DEFEAT REPUBLICAN SUSAN COLLINS
If we win this race, we’ll win the ENTIRE Senate >>
Ad copy
Jordan needs you to chip in to help him defeat Susan Collins. Here’s why: – Trump LOST Maine by 7 points – If we win Maine, we win the ENTIRE Senate – But Collins has MILLIONS in the bank to sabotage Jordan’s chances We CAN win this, but our campaign is starting with $0 in the bank, and he can’t do it without you. Please, will you chip in any amount?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 51.9/100 |
| Spend / Reach / Long. / Eff. | 47.8 · 75.1 · 29.2 · 55.6 |
| Spend range | USD 4,000–4,499 |
| Impressions | 80,000 – 89,999 |
| CPM (≈ $/1k impr) | USD 49.99 |
| Est. audience size | — |
| Days live | 107 (2025-05-27 → 2025-09-11) |
| Created | 2025-05-27 |
| 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 | urgency |
| Production tier | semi pro |
| Who pictured | candidate, opponent |
| Symbols | none |
| Call to action | donate |
| Standout element | Opponent Susan Collins rendered in black-and-white while candidate Jordan Wood appears in full color, visually othering her |
| Tactic | The contrast between a desaturated Collins and a vibrant Wood reinforces an us-vs-them frame, while the high-stakes 'flip the entire Senate' message amplifies urgency to drive small-dollar donations. |
Where it ran (delivery by region)
| California | 13.8% |
| New York | 9.2% |
| Massachusetts | 8.5% |
| Florida | 6.5% |
| Pennsylvania | 4.3% |
| Texas | 3.6% |
| Washington | 3.5% |
Who saw it (delivery)
| 25-34 · female | 0.5% |
| 25-34 · male | 0.5% |
| 25-34 · unknown | 0.0% |
| 35-44 · female | 1.1% |
| 35-44 · male | 1.2% |
| 35-44 · unknown | 0.0% |
| 45-54 · female | 2.0% |
| 45-54 · male | 2.2% |
| 45-54 · unknown | 0.0% |
| 55-64 · female | 8.9% |
| 55-64 · male | 8.1% |
| 55-64 · unknown | 0.2% |
| 65+ · female | 44.4% |
| 65+ · male | 29.8% |
| 65+ · unknown | 1.0% |
