Abdul for U.S. Senate
Hey, let me be real: calling people to ask for money isn't fun, especially when it's call after call after call. But here's why I do it: I refuse to take a dime from corporate PACs. No insurance giants, no big utilities, no greedy corporations. Because when you say no to special interests, you have to rely on the power of regular people. This campaign isn't about the rich and powerful; it’s about us—our needs, our families, and our future. We have a critical fundraising deadline, and I need your help to reach it. Can you chip in today? $5, $10, even $15—every bit counts. Let’s show them what people-powered politics looks like.
↗ View in Meta Ad Library (the live creative — image/video/layout)
| Overall score | 43.2/100 |
| Spend / Reach / Long. / Eff. | 27.6 · 61.7 · 23.7 · 59.7 |
| Spend range | USD 600–699 |
| Impressions | 20,000 – 24,999 |
| CPM (≈ $/1k impr) | USD 28.87 |
| Est. audience size | — |
| Days live | 87 (2025-06-30 → 2025-09-25) |
| Created | 2025-06-29 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MI — Abdul El-Sayed |
| Surfaced by | “senate:Abdul El-Sayed” |
| 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 | casual selfie-style outdoor video creating radical authenticity and anti-polish contrast with typical polished political ads |
| Tactic | The deliberately unpolished, direct-to-camera confessional style leverages vulnerability and transparency to build trust and contrast the candidate's grassroots funding model against corporate-backed opponents, making the donation ask feel morally urgent rather than transactional. |
| Michigan | 25.2% |
| California | 12.8% |
| New York | 9.5% |
| Texas | 6.8% |
| Illinois | 4.7% |
| New Jersey | 4.5% |
| Florida | 3.9% |
| 18-24 · female | 0.5% |
| 45-54 · male | 14.7% |
| 65+ · male | 7.6% |
| 65+ · female | 4.9% |
| 55-64 · unknown | 0.3% |
| 55-64 · male | 9.4% |
| 55-64 · female | 5.2% |
| 45-54 · unknown | 0.4% |
| 45-54 · female | 6.4% |
| 18-24 · male | 2.6% |
| 35-44 · unknown | 0.5% |
| 35-44 · male | 21.0% |
| 35-44 · female | 8.7% |
| 25-34 · unknown | 0.3% |
| 25-34 · male | 12.7% |
| 25-34 · female | 4.4% |
| 18-24 · unknown | 0.1% |
| 65+ · unknown | 0.2% |