Angie Craig US Inactive
ANGIE CRAIG FOR CONGRESS
Angie Craig for Congress
Ad copy
Congress should be working for you, not themselves. I led the charge to block congressional pay raises, the bipartisan effort to stop politicians from trading stocks, and I wrote the bill to permanently ban members of Congress from ever becoming registered lobbyists.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 43.5/100 |
| Spend / Reach / Long. / Eff. | 29.2 · 73.9 · 3.0 · 67.7 |
| Spend range | USD 700–799 |
| Impressions | 70,000 – 79,999 |
| CPM (≈ $/1k impr) | USD 9.99 |
| Est. audience size | 50,001 – 100,000 |
| Days live | 12 (2024-08-23 → 2024-09-04) |
| Created | 2024-08-23 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MN — Angie Craig |
| Surfaced by | “senate:Angie Craig” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | none |
| Symbols | none |
| Call to action | learn more |
| Standout element | Scoreboard graphic showing Angie Craig beating Washington 2-0, framing anti-corruption wins as a game score |
| Tactic | The scoreboard metaphor cleverly quantifies Craig's legislative achievements against Washington insiders, making abstract policy fights feel like tangible victories and positioning her as a fighter for constituents over self-interest. |
Where it ran (delivery by region)
| Minnesota | 100.0% |
| California | 0.0% |
| Colorado | 0.0% |
| Georgia | 0.0% |
| North Dakota | 0.0% |
| Pennsylvania | 0.0% |
| Nebraska | 0.0% |
Who saw it (delivery)
| 65+ · unknown | 0.0% |
| 55-64 · female | 4.9% |
| 18-24 · male | 5.2% |
| 35-44 · unknown | 0.2% |
| 18-24 · unknown | 0.1% |
| 45-54 · male | 7.7% |
| 25-34 · unknown | 0.2% |
| 55-64 · male | 4.0% |
| 65+ · female | 1.9% |
| 45-54 · unknown | 0.1% |
| 18-24 · female | 7.8% |
| 65+ · male | 1.6% |
| 35-44 · female | 15.0% |
| 35-44 · male | 11.1% |
| 25-34 · male | 13.3% |
| 45-54 · female | 10.0% |
| 25-34 · female | 16.7% |
| 55-64 · unknown | 0.1% |
