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Graham Platner US Inactive

GRAHAM FOR MAINE

ad creative

Janet Mills just entered the race

I'm running against Susan Collins, AIPAC, Super PACs, and the entire DC establishment.

Ad copy

Janet Mills just got in this race, so you're going to be bombarded with fundraising texts and emails from Chuck Schumer and her campaign. They're going to be able to raise big money. Big money from corporate PACs. Big money from AIPAC. Big money from the establishment. So I need your help. I need your help because we are only raising money with small-dollar donations from people like you. And I am counting on you to chip in now if we're going to take on the corporate interests in Washington and defeat Susan Collins. If you can, please make a monthly donation now to our campaign to help me defeat Susan Collins, flip Maine blue, and take back the Senate for Democrats.

↗ View in Meta Ad Library (the live creative — image/video/layout)

Performance proxies

Overall score57.4/100
Spend / Reach / Long. / Eff.69.6 · 100.0 · 1.1 · 58.9
Spend rangeUSD 30,000–34,999
Impressions1,000,000 – 0
CPM (≈ $/1k impr)USD 32.5
Est. audience size
Days live5 (2025-10-14 → 2025-10-19)
Created2025-10-14
Creative variants1 body · 1 headline
Platformsfacebook, instagram
Languagesen
CandidateME — Graham Platner
Surfaced by“senate:Graham Platner”

🧠 AI analysis

Issueeconomy taxes
Framingcontrast comparison
Message typefundraising
Emotional appealanger
Production tierdiy amateur
Who picturedcandidate
Symbolsmoney cash
Call to actiondonate
Standout elementcandidate in a Dropkick Murphys hoodie speaking to camera outdoors — deliberately working-class, anti-establishment aesthetic
TacticClassic grassroots fundraising contrast tactic: frames opponent as flush with corporate and establishment money to create urgency and moral contrast, driving small-dollar donors to give now.

Where it ran (delivery by region)

California10.5%
Maine9.8%
New York8.6%
Massachusetts8.0%
Pennsylvania4.0%
Washington3.9%
Illinois3.5%

Who saw it (delivery)

18-24 · female 1.4%
45-54 · male 9.9%
65+ · male 5.9%
65+ · female 7.4%
55-64 · unknown 0.2%
55-64 · male 6.5%
55-64 · female 6.8%
45-54 · unknown 0.2%
45-54 · female 7.7%
18-24 · male 4.7%
35-44 · unknown 0.5%
35-44 · male 15.8%
35-44 · female 10.9%
25-34 · unknown 0.5%
25-34 · male 14.0%
25-34 · female 7.2%
18-24 · unknown 0.2%
65+ · unknown 0.2%

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