Reilly Neill for U.S. Senate US Inactive
REILLY FOR MONTANA
Ad copy
Millionaires and billionaires want to make sure that Montanans don’t have a choice when it comes to data centers. One dirty money tech millionaire alone, Jason Carroll, just dumped a million dollars into a Democratic PAC in the primary to try and make sure we don’t win. Why? We’re listening to the people. Montanans are saying no to data centers. You can’t tell us what to do and you cannot buy our votes. #ReillyforMontana #CloseUp #NoDarkMoney #mtpol #mtnews
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Performance proxies
| Overall score | 31.4/100 |
| Spend / Reach / Long. / Eff. | 0.0 · 53.0 · 0.3 · 72.5 |
| Spend range | <USD 99 |
| Impressions | 9,000 – 9,999 |
| CPM (≈ $/1k impr) | USD 5.21 |
| Est. audience size | — |
| Days live | 2 (2026-05-22 → 2026-05-24) |
| Created | 2026-05-22 |
| Creative variants | 1 body · 0 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MT — Reilly Neill |
| Surfaced by | “senate:Reilly Neill” |
🧠 AI analysis
| Issue | economy taxes |
| Framing | attack opponent |
| Message type | fundraising |
| Emotional appeal | anger |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | money cash |
| Call to action | none |
| Standout element | Intense close-up selfie video of the candidate with furrowed brow conveying righteous indignation |
| Tactic | The ad uses populist anger framing — pitting everyday Montanans against a named 'dirty money' tech millionaire — to drive urgency and earned credibility through direct, unpolished authenticity. |
Where it ran (delivery by region)
| Arizona | 26.6% |
| Washington | 16.4% |
| Colorado | 11.1% |
| Nevada | 9.9% |
| Oregon | 9.0% |
| Utah | 4.5% |
| Washington, District of Columbia | 4.1% |
Who saw it (delivery)
| 18-24 · female | 0.2% |
| 45-54 · male | 6.1% |
| 65+ · male | 38.2% |
| 65+ · female | 20.3% |
| 55-64 · unknown | 0.1% |
| 55-64 · male | 19.7% |
| 55-64 · female | 7.8% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 2.1% |
| 18-24 · male | 0.4% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 2.5% |
| 35-44 · female | 0.9% |
| 25-34 · unknown | 0.0% |
| 25-34 · male | 0.8% |
| 25-34 · female | 0.5% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.2% |
