Peggy Flanagan US Inactive
Peggy Flanagan for Minnesota
Donate to Peggy Flanagan for U.S. Senate
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Ad copy
Chip in $5 to help keep Minnesota’s Senate seat blue. Michele Tafoya is backed by national Republicans and MAGA donors who are ready to spend big to flip this seat. I’m not intimidated. But I don’t take corporate PAC money, so the only way we win is with grassroots support from people like you. This is one of the most closely watched Senate races in the country. Republicans know it. Special interests know it. And they’re preparing to spend millions to stop us. If you can, pitch in $5, $10, or whatever you’re able to help us show real strength and keep Minnesota blue.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 34.3/100 |
| Spend / Reach / Long. / Eff. | 21.0 · 55.8 · 0.6 · 60.0 |
| Spend range | USD 300–399 |
| Impressions | 10,000 – 14,999 |
| CPM (≈ $/1k impr) | USD 27.96 |
| Est. audience size | — |
| Days live | 3 (2026-06-01 → 2026-06-04) |
| Created | 2026-06-01 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MN — Peggy Flanagan |
| Surfaced by | “senate:Peggy Flanagan” |
🧠 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 | Side-by-side polling percentages with candidate photos — color vs. black-and-white contrast visually favoring Flanagan |
| Tactic | Classic small-dollar fundraising urgency tactic: a close poll stat plus a 'David vs. Goliath' grassroots-vs-big-money frame pressures donors to act immediately. |
Where it ran (delivery by region)
| Minnesota | 85.2% |
| California | 1.7% |
| Wisconsin | 1.4% |
| North Dakota | 0.8% |
| New York | 0.7% |
| Florida | 0.6% |
| Illinois | 0.6% |
Who saw it (delivery)
| 18-24 · female | 0.3% |
| 45-54 · male | 6.2% |
| 65+ · male | 16.9% |
| 65+ · female | 23.4% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 7.7% |
| 55-64 · female | 11.8% |
| 45-54 · unknown | 0.3% |
| 45-54 · female | 10.9% |
| 18-24 · male | 0.5% |
| 35-44 · unknown | 0.3% |
| 35-44 · male | 6.1% |
| 35-44 · female | 8.7% |
| 25-34 · unknown | 0.2% |
| 25-34 · male | 2.6% |
| 25-34 · female | 3.2% |
| 65+ · unknown | 0.6% |
