Mary Peltola US Inactive
Mary Peltola for Congress
Southeast Potholes | Mary Peltola for Congress
Tired of potholes? Mary has brought in over $8.7 billion in infrastructure investment statewide, including $128 million for Southeast Alaska, to improve our roads, bridges, docks, airports, and more so Alaskans can have the high-quality infrastructure they deserve.
Ad copy
Tired of potholes? Mary has brought in over $8.7 billion in infrastructure investment statewide, including $128 million for Southeast Alaska, to improve our roads, bridges, docks, airports, and more so Alaskans can have the high-quality infrastructure they deserve. Click here, and see what Mary is doing for Southeast Alaska. With your support, we can continue continue securing critical infrastructure funding for years to come.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 39.6/100 |
| Spend / Reach / Long. / Eff. | 23.7 · 61.7 · 10.5 · 62.5 |
| Spend range | USD 400–499 |
| Impressions | 20,000 – 24,999 |
| CPM (≈ $/1k impr) | USD 19.98 |
| Est. audience size | 10,001 – 50,000 |
| Days live | 39 (2024-09-11 → 2024-10-20) |
| Created | 2024-09-11 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | AK — Mary Peltola |
| Surfaced by | “senate:Mary Peltola” |
🧠 AI analysis
| Issue | housing |
| Framing | positive candidate |
| Message type | persuasion |
| Emotional appeal | empathy |
| Production tier | semi pro |
| Who pictured | none |
| Symbols | farm rural |
| Call to action | learn more |
| Standout element | Close-up photo of a massive muddy pothole dominating the frame paired with the bold '$128 Million' solution callout |
| Tactic | The ad uses a classic problem-solution persuasion tactic — viscerally relatable imagery of a pothole triggers voter frustration before pivoting to Peltola as the deliverer of concrete, dollar-specific relief. |
Where it ran (delivery by region)
| Alaska | 100.0% |
Who saw it (delivery)
| 18-24 · female | 1.1% |
| 45-54 · male | 9.6% |
| 65+ · male | 13.7% |
| 65+ · female | 16.0% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 9.8% |
| 55-64 · female | 9.5% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 6.8% |
| 18-24 · male | 1.6% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 10.8% |
| 35-44 · female | 7.4% |
| 25-34 · unknown | 0.1% |
| 25-34 · male | 7.9% |
| 25-34 · female | 4.8% |
| 18-24 · unknown | 0.1% |
| 65+ · unknown | 0.2% |
