Haley Stevens US Inactive
HALEY STEVENS FOR CONGRESS
Vote Haley Stevens
Haley Stevens was born and raised in Oakland County and has dedicated her career to saving and growing good-paying jobs in Michigan.
Ad copy
Haley Stevens is working to fix our supply chains with Michigan workers. This year, she passed a bipartisan law to manufacture semiconductors in America, not factories in China.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 51.1/100 |
| Spend / Reach / Long. / Eff. | 55.2 · 85.0 · 6.6 · 57.7 |
| Spend range | USD 8,000–8,999 |
| Impressions | 200,000 – 249,999 |
| CPM (≈ $/1k impr) | USD 37.78 |
| Est. audience size | 10,001 – 50,000 |
| Days live | 25 (2022-10-14 → 2022-11-08) |
| Created | 2022-10-13 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MI — Haley Stevens |
| Surfaced by | “senate:Haley Stevens” |
🧠 AI analysis
| Issue | economy taxes |
| Framing | positive candidate |
| Message type | persuasion |
| Emotional appeal | pride |
| Production tier | professional |
| Who pictured | candidate, workers labor |
| Symbols | factory industry |
| Call to action | vote |
| Standout element | Candidate shown hands-on at a manufacturing workstation alongside a worker, projecting competence and bipartisan credibility |
| Tactic | Ad uses the bipartisan semiconductor law as a concrete legislative accomplishment to build credibility with moderate voters, contrasting American manufacturing with China to trigger economic nationalism and job-security concerns. |
Where it ran (delivery by region)
| Michigan | 99.8% |
| Ohio | 0.0% |
| Florida | 0.0% |
| California | 0.0% |
| Texas | 0.0% |
| Arizona | 0.0% |
| Washington, District of Columbia | 0.0% |
Who saw it (delivery)
| 13-17 · male | 0.0% |
| 25-34 · female | 7.9% |
| 65+ · unknown | 0.2% |
| 35-44 · unknown | 0.3% |
| 18-24 · male | 0.7% |
| 18-24 · female | 0.8% |
| 45-54 · male | 6.4% |
| 35-44 · female | 15.6% |
| 25-34 · male | 4.5% |
| 65+ · female | 15.6% |
| 18-24 · unknown | 0.0% |
| 55-64 · female | 12.9% |
| 45-54 · female | 14.2% |
| 65+ · male | 6.6% |
| 35-44 · male | 8.3% |
| 55-64 · male | 5.6% |
| 45-54 · unknown | 0.2% |
| 25-34 · unknown | 0.1% |
| 55-64 · unknown | 0.2% |
