Eric Skrmetta Campaign Fund
If you’ve seen the vicious attacks against Eric Skrmetta and wonder if they are true, consider the source. The puppeteer behind Kevin Pearson’s bogus attacks is convicted felon Thomas Neyhart. Neyhart runs Posigen, a solar company that made tons of money on rooftop solar subsidies that cost Louisiana over $200 million. When Skrmetta fought for ratepayers to stop the unfair practice, Neyhart recruited Pearson to attack Skrmetta. Neyhart was also fined by the Louisiana Contractor Board for falsifying contractor license information. And, Neyhart was charged criminally in Nevada for battery with substantial bodily harm when he paid a guard to look the other way while he sucker punched a man permanently blinding the victim in one eye. Now, Neyhart is lying about Eric Skrmetta, and Pearson is his puppet.
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| Overall score | 27.5/100 |
| Spend / Reach / Long. / Eff. | 0.0 · 43.0 · 1.9 · 65.1 |
| Spend range | <USD 99 |
| Impressions | 3,000 – 3,999 |
| CPM (≈ $/1k impr) | USD 14.14 |
| Est. audience size | 10,001 – 50,000 |
| Days live | 8 (2020-10-26 → 2020-11-03) |
| Created | 2020-10-26 |
| Creative variants | 1 body · 0 headline |
| Platforms | |
| Languages | en |
| Candidate | LA — Eric Skrmetta |
| Surfaced by | “senate:Eric Skrmetta” |
| Issue | energy environment |
| Framing | attack opponent |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | opponent |
| Symbols | money cash |
| Call to action | none |
| Standout element | Giant puppet master hand controlling a suited figure on strings, depicting opponent as a marionette of a convicted felon |
| Tactic | Classic puppet-master attack tactic delegitimizes the opponent by linking him to a convicted felon backer, using a visceral visual metaphor to make the corruption narrative immediately intuitive and memorable. |
| Louisiana | 98.7% |
| Mississippi | 0.3% |
| Texas | 0.2% |
| Kentucky | 0.2% |
| Georgia | 0.1% |
| Indiana | 0.1% |
| Oklahoma | 0.1% |
| 18-24 · female | 0.6% |
| 65+ · female | 12.8% |
| 65+ · male | 9.9% |
| 35-44 · female | 3.1% |
| 35-44 · unknown | 0.1% |
| 45-54 · male | 15.7% |
| 18-24 · male | 4.0% |
| 45-54 · female | 6.7% |
| 35-44 · male | 10.7% |
| 55-64 · male | 15.5% |
| 25-34 · male | 7.5% |
| 55-64 · female | 11.7% |
| 25-34 · female | 1.4% |
| 45-54 · unknown | 0.2% |
| 55-64 · unknown | 0.1% |
| 65+ · unknown | 0.2% |