Lindsey Graham US Inactive
TEAM GRAHAM, INC.
NOW IS THE TIME TO FIGHT BACK!
Will you chip in $20.20?
Ad copy
🚨🚨🚨 My opponent raised $57 MILLION in the last three months – the most ever raised in a single quarter by any Senate candidate in U.S. history! This brings his grand total to $86 million to fund his massive negative ad campaign against me. I need your help. Will you chip in $5, $10, or even $25 to help me fight back?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 55.7/100 |
| Spend / Reach / Long. / Eff. | 63.0 · 97.1 · 1.4 · 61.3 |
| Spend range | USD 15,000–19,999 |
| Impressions | 700,000 – 799,999 |
| CPM (≈ $/1k impr) | USD 23.33 |
| Est. audience size | — |
| Days live | 6 (2020-10-12 → 2020-10-18) |
| Created | 2020-10-12 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | SC — Lindsey Graham |
| Surfaced by | “senate:SC — Lindsey Graham” |
🧠 AI analysis
| Issue | economy taxes |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | Tight close-up of Graham's face conveying personal, direct urgency like a video call plea |
| Tactic | Classic underdog fundraising tactic using opponent's record-breaking $57M haul as a threat multiplier to trigger fear and spur small-dollar donations. |
Where it ran (delivery by region)
| California | 9.5% |
| Texas | 8.8% |
| Florida | 6.8% |
| New York | 5.2% |
| North Carolina | 3.8% |
| Pennsylvania | 3.8% |
| Ohio | 3.4% |
Who saw it (delivery)
| 65+ · unknown | 0.2% |
| 65+ · female | 7.8% |
| 18-24 · unknown | 0.0% |
| 45-54 · unknown | 0.3% |
| 18-24 · female | 0.4% |
| 35-44 · unknown | 0.3% |
| 35-44 · male | 13.5% |
| 65+ · male | 10.3% |
| 45-54 · female | 7.2% |
| 18-24 · male | 1.7% |
| 55-64 · unknown | 0.3% |
| 35-44 · female | 4.8% |
| 55-64 · male | 16.5% |
| 25-34 · female | 2.1% |
| 25-34 · male | 8.1% |
| 45-54 · male | 17.8% |
| 55-64 · female | 8.6% |
| 25-34 · unknown | 0.1% |
