Sarah Adlakha for US Senate US Inactive
SARAH ADLAKHA FOR SENATE
Choose your Protein
Ad copy
In a WLOX interview, Senator Cindy Hyde-Smith said if people can’t afford beef, they should choose another protein. Easy to say while attending steak dinners paid for with campaign donations. Mississippi families don’t need lectures about downgrading their dinner plates. We raise beef here. We eat beef here. And we’re not switching. March 10. Republican Primary. 🇺🇸 Sarah Adlakha for U.S. Senate.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 36.4/100 |
| Spend / Reach / Long. / Eff. | 11.9 · 61.7 · 1.1 · 70.7 |
| Spend range | USD 100–199 |
| Impressions | 20,000 – 24,999 |
| CPM (≈ $/1k impr) | USD 6.64 |
| Est. audience size | — |
| Days live | 5 (2026-02-27 → 2026-03-04) |
| Created | 2026-02-27 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MS — Sarah Adlakha |
| Surfaced by | “senate:Sarah Adlakha” |
🧠 AI analysis
| Issue | economy taxes |
| Framing | contrast comparison |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | us flag, farm rural |
| Call to action | vote |
| Standout element | Candidate filming herself in home kitchen — deliberately everyday and relatable — while calling out opponent's steak dinners |
| Tactic | The ad uses a populist 'one of us vs. one of them' contrast tactic, weaponizing a gaffe about beef to paint Hyde-Smith as out-of-touch with Mississippi's agricultural identity while positioning Adlakha as a relatable kitchen-table candidate. |
Where it ran (delivery by region)
| Mississippi | 100.0% |
Who saw it (delivery)
| 18-24 · female | 1.0% |
| 45-54 · male | 11.5% |
| 65+ · male | 20.2% |
| 65+ · female | 15.0% |
| 55-64 · unknown | 0.1% |
| 55-64 · male | 14.9% |
| 55-64 · female | 8.0% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 4.3% |
| 18-24 · male | 3.2% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 9.0% |
| 35-44 · female | 2.6% |
| 25-34 · unknown | 0.1% |
| 25-34 · male | 7.7% |
| 25-34 · female | 1.8% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.3% |
