Peggy Flanagan for Minnesota
Peggy was just endorsed by Senators Elizabeth Warren, Ed Markey, Chris Murphy, Chris Van Hollen, Jeff Merkley, and Martin Heinrich! Democrats across the country are joining Team Peggy because they know she has what it takes to fight, and make a real difference for the people of Minnesota. They’re tired of hand-picked candidates by extremist billionaires. They’re ready for someone with real vision. Endorsements like these are great — they show just how strong and deep this movement is. But they don’t pay the bills. We’re hiring staff, running ads, and traveling across the state to connect with voters — and all of that takes significant resources. Peggy is powered by grassroots supporters like you. Pitch in today to continue growing this grassroots team?
↗ View in Meta Ad Library (the live creative — image/video/layout)
| Overall score | 35.0/100 |
| Spend / Reach / Long. / Eff. | 27.6 · 55.8 · 1.1 · 55.4 |
| Spend range | USD 600–699 |
| Impressions | 10,000 – 14,999 |
| CPM (≈ $/1k impr) | USD 51.96 |
| Est. audience size | — |
| Days live | 5 (2025-11-20 → 2025-11-25) |
| Created | 2025-11-20 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MN — Peggy Flanagan |
| Surfaced by | “senate:Peggy Flanagan” |
| Issue | candidate character bio |
| Framing | positive candidate |
| Message type | fundraising |
| Emotional appeal | hope |
| Production tier | diy amateur |
| Who pictured | veterans military |
| Symbols | none |
| Call to action | donate |
| Standout element | Casual selfie-style video from endorsing Senator Martin Heinrich in a flannel shirt, lending an authentic peer-to-peer credibility feel |
| Tactic | Leverages high-profile Senate endorsements to build social proof and urgency, then pivots directly to a donation ask to convert enthusiasm into financial support. |
| Minnesota | 71.6% |
| California | 4.2% |
| Wisconsin | 2.0% |
| New York | 1.8% |
| Florida | 1.4% |
| Washington | 1.2% |
| Texas | 1.1% |
| 18-24 · female | 0.4% |
| 45-54 · male | 4.9% |
| 65+ · male | 24.0% |
| 65+ · female | 32.2% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 8.1% |
| 55-64 · female | 10.8% |
| 45-54 · unknown | 0.2% |
| 45-54 · female | 5.9% |
| 18-24 · male | 1.0% |
| 35-44 · unknown | 0.3% |
| 35-44 · male | 3.4% |
| 35-44 · female | 3.5% |
| 25-34 · unknown | 0.2% |
| 25-34 · male | 2.5% |
| 25-34 · female | 1.6% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.7% |