Abdul El-Sayed US Inactive
Abdul for U.S. Senate
Save Abdul from call time →
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Ad copy
This is me during one of the least glamorous parts of campaigning: call time. Most candidates would just call up a few billionaires and be done for the day. But I don’t take corporate PAC or AIPAC money, which means my campaign has to run the old-fashioned way: powered by people who believe in a government that works for them, not special-interest donors. That’s why I’m able to fight for things like Medicare for All, abolishing ICE, and ending corporate greed. But a campaign like this is only possible if everyone steps up. So I’m asking if you can chip in $10 or whatever you can today, so we can build a campaign that fights for you and shows corporate-backed candidates that their time is up.
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Performance proxies
| Overall score | 41.6/100 |
| Spend / Reach / Long. / Eff. | 34.7 · 68.1 · 3.9 · 59.6 |
| Spend range | USD 1,000–1,499 |
| Impressions | 40,000 – 44,999 |
| CPM (≈ $/1k impr) | USD 29.4 |
| Est. audience size | — |
| Days live | 15 (2026-03-17 → 2026-04-01) |
| Created | 2026-03-17 |
| 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 | healthcare |
| Framing | positive candidate |
| Message type | fundraising |
| Emotional appeal | empathy |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | healthcare, none |
| Call to action | donate |
| Standout element | Candidate hunched over a laptop in a cluttered office with a Medicare for All book visibly on the shelf, projecting relatable authenticity |
| Tactic | The 'call time' candor tactic humanizes the candidate and weaponizes grassroots contrast against billionaire-backed rivals to drive small-dollar donations. |
Where it ran (delivery by region)
| Michigan | 54.0% |
| California | 6.5% |
| Texas | 4.0% |
| New York | 4.0% |
| Illinois | 4.0% |
| Florida | 2.3% |
| Ohio | 2.2% |
Who saw it (delivery)
| 18-24 · female | 0.7% |
| 45-54 · male | 9.9% |
| 65+ · male | 7.2% |
| 65+ · female | 11.8% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 6.0% |
| 55-64 · female | 7.3% |
| 45-54 · unknown | 0.4% |
| 45-54 · female | 7.9% |
| 18-24 · male | 1.9% |
| 35-44 · unknown | 0.7% |
| 35-44 · male | 17.2% |
| 35-44 · female | 10.6% |
| 25-34 · unknown | 0.5% |
| 25-34 · male | 11.6% |
| 25-34 · female | 5.7% |
| 18-24 · unknown | 0.1% |
| 65+ · unknown | 0.4% |
