Abdul El-Sayed US Inactive
Abdul for U.S. Senate
I need your help.
Ad copy
🚨 A Republican Super PAC is dropping $45 MILLION into this race to stop us. This isn’t happening by chance. If Republicans win this race, they will lock in a Senate majority. But if we win, we can flip the Senate blue. I don’t take a dime of corporate PAC money, which is why I need your help. Will you chip in $5 or $10 today to help us fight back against these MAGA mega-donors?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 56.7/100 |
| Spend / Reach / Long. / Eff. | 63.0 · 94.0 · 10.7 · 59.0 |
| Spend range | USD 15,000–19,999 |
| Impressions | 500,000 – 599,999 |
| CPM (≈ $/1k impr) | USD 31.82 |
| Est. audience size | — |
| Days live | 40 (2026-04-17 → 2026-05-27) |
| Created | 2026-04-16 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MI — Abdul El-Sayed |
| Surfaced by | “senate:Abdul El-Sayed” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | money cash |
| Call to action | donate |
| Standout element | Selfie-style close-up video frame creating an intimate, direct-to-camera grassroots appeal |
| Tactic | The ad uses a David-vs-Goliath money contrast — no corporate PAC money vs. $45M Republican Super PAC — to trigger urgency and inspire small-dollar donors to act as a counterbalancing force. |
Where it ran (delivery by region)
| Michigan | 41.1% |
| California | 9.3% |
| New York | 6.0% |
| Texas | 4.2% |
| Illinois | 4.1% |
| Florida | 3.1% |
| New Jersey | 2.4% |
Who saw it (delivery)
| 18-24 · female | 1.7% |
| 45-54 · unknown | 0.3% |
| Unknown · female | 0.0% |
| 65+ · unknown | 0.3% |
| 65+ · male | 7.6% |
| 65+ · female | 9.1% |
| 55-64 · unknown | 0.3% |
| 55-64 · male | 7.6% |
| 55-64 · female | 7.6% |
| 45-54 · male | 10.2% |
| 18-24 · male | 4.2% |
| 45-54 · female | 7.2% |
| 35-44 · unknown | 0.4% |
| 35-44 · male | 14.2% |
| 35-44 · female | 8.5% |
| 25-34 · unknown | 0.5% |
| 25-34 · male | 13.7% |
| 25-34 · female | 6.5% |
| 18-24 · unknown | 0.2% |
| Unknown · male | 0.0% |
