Mark Warner VA US Inactive
FRIENDS OF MARK WARNER
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Chip in right now.
Ad copy
Donald Trump just called me "a bad guy" and “among the worst" Democrats. While Trump and his allies are ramping up their lies and attacks on my record, I’m facing one of the most important fundraising deadlines of the year, and we’re still falling short. Can I count on you to donate $5, $10 or even $20 dollars before our deadline?
Donald Trump just called me "a bad guy" and “among the worst" Democrats. While Trump and his allies are ramping up their lies and attacks on my record, I’m facing one of the most important fundraising deadlines of the year, and we’re still falling short. Can I count on you to donate $5, $10 or even $20 dollars before our deadline?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 43.5/100 |
| Spend / Reach / Long. / Eff. | 44.9 · 72.4 · 1.1 · 55.6 |
| Spend range | USD 3,000–3,499 |
| Impressions | 60,000 – 69,999 |
| CPM (≈ $/1k impr) | USD 49.99 |
| Est. audience size | 100,001 – 500,000 |
| Days live | 5 (2025-09-27 → 2025-10-02) |
| Created | 2025-09-27 |
| Creative variants | 2 body · 2 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | VA — Mark Warner |
| Surfaced by | “senate:Mark Warner” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | semi pro |
| Who pictured | none |
| Symbols | none |
| Call to action | donate |
| Standout element | Screenshot of a personal fundraising email using Trump's own attack words as the hook |
| Tactic | Classic judo tactic — weaponizing the opponent's attack as proof of the candidate's credibility while creating end-of-quarter urgency to drive small-dollar donations. |
Where it ran (delivery by region)
| Virginia | 100.0% |
Who saw it (delivery)
| 18-24 · female | 0.1% |
| 45-54 · male | 3.3% |
| 65+ · male | 16.7% |
| 65+ · female | 40.1% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 6.9% |
| 55-64 · female | 17.4% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 7.9% |
| 18-24 · male | 0.1% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 1.7% |
| 35-44 · female | 3.3% |
| 25-34 · unknown | 0.0% |
| 25-34 · male | 0.6% |
| 25-34 · female | 0.8% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.7% |
