Peggy Flanagan US Inactive
Peggy Flanagan for Minnesota
Help me flip the Senate Blue >>>
Ad copy
Hi folks, this will be super quick. I’m Peggy Flanagan, running for U.S. Senate in Minnesota. Trump’s billionaire besties think this seat belongs to them - or their MAGA candidate, Michele Tafoya. It doesn’t. It belongs to working families -- to the people who can’t afford to buy a U.S. Senator. Michele and her dark money groups have got big donors with deep pockets. We’ve got each other. To beat her, I need you with me, right now. If you believe in a people-powered campaign, please chip in $20, or whatever you can spare, today so we can organize, respond to the attacks lobbed our way, and win this race together. Thank you.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 36.2/100 |
| Spend / Reach / Long. / Eff. | 23.7 · 59.2 · 1.4 · 60.6 |
| Spend range | USD 400–499 |
| Impressions | 15,000 – 19,999 |
| CPM (≈ $/1k impr) | USD 25.69 |
| Est. audience size | — |
| Days live | 6 (2026-06-23 → 2026-06-29) |
| Created | 2026-06-23 |
| Creative variants | 1 body · 1 headline |
| Platforms | |
| Languages | — |
| Candidate | MN — Peggy Flanagan |
| Surfaced by | “senate:Peggy Flanagan” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | Candidate speaking directly to camera in casual striped top on a scenic bridge, signaling accessibility and authenticity |
| Tactic | The ad uses a classic populist contrast tactic — pitting billionaire dark money against small-dollar grassroots donors — to drive urgency and fundraising action. |
Where it ran (delivery by region)
| Minnesota | 88.3% |
| California | 1.5% |
| Wisconsin | 1.2% |
| New York | 0.7% |
| Illinois | 0.7% |
| Washington | 0.6% |
| Texas | 0.5% |
Who saw it (delivery)
| 18-24 · female | 8.2% |
| 45-54 · unknown | 0.3% |
| Unknown · female | 0.0% |
| 65+ · unknown | 0.1% |
| 65+ · male | 2.9% |
| 65+ · female | 6.6% |
| 55-64 · unknown | 0.1% |
| 55-64 · male | 2.4% |
| 55-64 · female | 6.8% |
| 45-54 · male | 3.8% |
| 18-24 · male | 6.3% |
| 45-54 · female | 9.1% |
| 35-44 · unknown | 0.6% |
| 35-44 · male | 7.6% |
| 35-44 · female | 13.8% |
| 25-34 · unknown | 1.7% |
| 25-34 · male | 11.6% |
| 25-34 · female | 17.3% |
| 18-24 · unknown | 0.9% |
| Unknown · male | 0.0% |
