Sherrod Brown US Inactive
FRIENDS OF SHERROD BROWN
New Law to Fight Fentanyl
New Law to Fight Fentanyl
New Law to Fight Fentanyl
Ad copy
To stop fentanyl from getting into our communities, we have to stop it at the border. Sherrod Brown wrote the law to give law enforcement the resources they need to go after Chinese suppliers and Mexican cartels.
To stop fentanyl from getting into our communities, we have to stop it at the border. Sherrod Brown wrote the law to give law enforcement the resources they need to go after Chinese suppliers and Mexican cartels.
To stop fentanyl from getting into our communities, we have to stop it at the border. Sherrod Brown wrote the law to give law enforcement the resources they need to go after Chinese suppliers and Mexican cartels.
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Performance proxies
| Overall score | 57.5/100 |
| Spend / Reach / Long. / Eff. | 56.4 · 99.5 · 6.6 · 67.7 |
| Spend range | USD 9,000–9,999 |
| Impressions | 900,000 – 999,999 |
| CPM (≈ $/1k impr) | USD 10.0 |
| Est. audience size | 500,001 – 1,000,000 |
| Days live | 25 (2024-07-12 → 2024-08-06) |
| Created | 2024-07-12 |
| Creative variants | 3 body · 3 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | OH — Sherrod Brown |
| Surfaced by | “senate:Sherrod Brown” |
🧠 AI analysis
| Issue | crime safety |
| Framing | positive candidate |
| Message type | persuasion |
| Emotional appeal | fear |
| Production tier | professional |
| Who pictured | police |
| Symbols | us flag, border |
| Call to action | learn more |
| Standout element | Uniformed sheriff in official setting delivering a direct-to-camera endorsement with red bold text chyron for credibility |
| Tactic | Third-party law enforcement validator tactic — a county sheriff speaking directly to camera lends bipartisan credibility to Brown's fentanyl legislation, positioning him as tough on border crime without Brown appearing himself. |
Where it ran (delivery by region)
| Ohio | 100.0% |
Who saw it (delivery)
| 18-24 · unknown | 0.0% |
| 45-54 · male | 11.0% |
| 55-64 · unknown | 0.1% |
| 18-24 · female | 0.5% |
| 18-24 · male | 1.0% |
| 35-44 · unknown | 0.1% |
| 25-34 · female | 3.5% |
| 35-44 · female | 8.1% |
| 35-44 · male | 10.7% |
| 25-34 · unknown | 0.1% |
| 25-34 · male | 5.7% |
| 65+ · male | 10.6% |
| 45-54 · female | 9.5% |
| 55-64 · male | 10.7% |
| 55-64 · female | 12.1% |
| 65+ · female | 16.1% |
| 45-54 · unknown | 0.1% |
| 65+ · unknown | 0.2% |
