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Commissioner Eric Skrmetta, LPSC US Inactive

Eric Skrmetta Campaign Fund

ad creative

Ad copy

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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Performance proxies

Overall score27.5/100
Spend / Reach / Long. / Eff.0.0 · 43.0 · 1.9 · 65.1
Spend range<USD 99
Impressions3,000 – 3,999
CPM (≈ $/1k impr)USD 14.14
Est. audience size10,001 – 50,000
Days live8 (2020-10-26 → 2020-11-03)
Created2020-10-26
Creative variants1 body · 0 headline
Platformsfacebook
Languagesen
CandidateLA — Eric Skrmetta
Surfaced by“senate:Eric Skrmetta”

🧠 AI analysis

Issueenergy environment
Framingattack opponent
Message typepersuasion
Emotional appealanger
Production tiersemi pro
Who picturedopponent
Symbolsmoney cash
Call to actionnone
Standout elementGiant puppet master hand controlling a suited figure on strings, depicting opponent as a marionette of a convicted felon
TacticClassic 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.

Where it ran (delivery by region)

Louisiana98.7%
Mississippi0.3%
Texas0.2%
Kentucky0.2%
Georgia0.1%
Indiana0.1%
Oklahoma0.1%

Who saw it (delivery)

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%

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