Jeanne Shaheen US Inactive
Shaheen for Senate
STOP COREY LEWANDOWSKI: Give now to defend NH’s Dem Senate seat!
Every dollar DOUBLED to win in NH and take back the Senate for Democrats.
Ad copy
URGENT: Corey Lewandowski was the campaign manager who put Donald Trump in the White House. And now Republicans are lining up behind him to turn New Hampshire’s Senate seat red! That’s why a group of donors is MATCHING ALL GIFTS to help us defeat the GOP – but we’re still $2,174 short of our goal. We’re counting on you to pitch in before 11:59 p.m.: Donate now to stop Corey Lewandowski, defend New Hampshire’s Senate seat and take back the Senate for Democrats!
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 36.5/100 |
| Spend / Reach / Long. / Eff. | 25.8 · 55.8 · 8.0 · 56.6 |
| Spend range | USD 500–599 |
| Impressions | 10,000 – 14,999 |
| CPM (≈ $/1k impr) | USD 43.96 |
| Est. audience size | — |
| Days live | 30 (2019-08-21 → 2019-09-20) |
| Created | 2019-08-21 |
| Creative variants | 1 body · 1 headline |
| Platforms | — |
| Languages | en |
| Candidate | NH — Jeanne Shaheen |
| Surfaced by | “senate:Jeanne Shaheen” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | semi pro |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | 'BREAKING NOW' red banner overlaid on candidate photo creates false-urgency news aesthetic |
| Tactic | Classic fundraising fear-and-urgency tactic pairing a likable candidate photo with a matching-gift deadline and a named villain (Lewandowski/Trump) to drive immediate donations. |
Where it ran (delivery by region)
| California | 13.9% |
| New York | 6.4% |
| Florida | 5.9% |
| Texas | 4.3% |
| Pennsylvania | 4.3% |
| Washington | 4.1% |
| North Carolina | 3.6% |
Who saw it (delivery)
| 55-64 · unknown | 0.5% |
| 35-44 · unknown | 0.1% |
| 55-64 · female | 22.1% |
| 65+ · male | 14.8% |
| 45-54 · female | 8.6% |
| 45-54 · male | 6.4% |
| 55-64 · male | 12.4% |
| 65+ · female | 30.1% |
| 35-44 · male | 1.7% |
| 45-54 · unknown | 0.2% |
| 35-44 · female | 2.4% |
| 65+ · unknown | 0.7% |
