Peggy Flanagan US Inactive
Peggy Flanagan for Minnesota
Donate today
Donate today
Donate today
Donate today
Stand with Peggy
Stand with Peggy
Stand with Peggy
Stand with Peggy
Ad copy
This campaign is powered by grandmas, teachers, and truck drivers – not corporate PACs or billionaires. Can you chip in today?
This campaign is powered by grandmas, teachers, and truck drivers – not corporate PACs or billionaires. Can you chip in today?
This campaign is powered by grandmas, teachers, and truck drivers – not corporate PACs or billionaires. Can you chip in today?
This campaign is powered by grandmas, teachers, and truck drivers – not corporate PACs or billionaires. Can you chip in today?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 35.2/100 |
| Spend / Reach / Long. / Eff. | 17.4 · 50.6 · 14.3 · 58.7 |
| Spend range | USD 200–299 |
| Impressions | 7,000 – 7,999 |
| CPM (≈ $/1k impr) | USD 33.27 |
| Est. audience size | — |
| Days live | 53 (2025-07-28 → 2025-09-19) |
| Created | 2025-07-28 |
| Creative variants | 4 body · 4 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MN — Peggy Flanagan |
| Surfaced by | “senate:Peggy Flanagan” |
🧠 AI analysis
| Issue | economy taxes |
| Framing | positive candidate |
| Message type | fundraising |
| Emotional appeal | empathy |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | low-fi selfie-style video frame evoking authenticity and grassroots intimacy |
| Tactic | The populist contrast of 'grandmas, teachers, and truck drivers' versus 'corporate PACs or billionaires' deploys a classic us-vs-them fundraising appeal designed to motivate small-dollar donors through class solidarity and anti-elite sentiment. |
Where it ran (delivery by region)
| Minnesota | 77.6% |
| California | 2.8% |
| Wisconsin | 1.6% |
| Texas | 1.3% |
| New York | 1.1% |
| Michigan | 1.0% |
| Massachusetts | 0.9% |
Who saw it (delivery)
| 18-24 · female | 0.6% |
| 45-54 · male | 3.8% |
| 65+ · male | 18.4% |
| 65+ · female | 34.0% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 6.0% |
| 55-64 · female | 14.0% |
| 45-54 · unknown | 0.4% |
| 45-54 · female | 8.2% |
| 18-24 · male | 0.7% |
| 35-44 · unknown | 0.3% |
| 35-44 · male | 2.5% |
| 35-44 · female | 6.2% |
| 25-34 · unknown | 0.2% |
| 25-34 · male | 1.4% |
| 25-34 · female | 2.3% |
| 18-24 · unknown | 0.1% |
| 65+ · unknown | 0.8% |
