# Ecom Goat (@GoatOfEcom) on x

- id: koinon:creator:x_goatofecom
- url: https://koinonlink.com/kol/x/GoatOfEcom
- schema_version: 1.4

## Identity
- platform: x
- handle: GoatOfEcom
- platform_url: https://twitter.com/GoatOfEcom
- website: https://t.me/goatofecom
- country: Netherlands
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 630
- following: 50
- posts_total: 75
- posts_observed: 4
- posts_per_day: 0.118
- account_age_days: 636
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.08889
- best_post_interactions: 56
- measurement_source: x_post_interactions
- measurement_formula: interactions on one post — likes, or likes + reposts + replies depending on the discovery path (the two are not distinguishable in the stored data)
- comparable_pool: peak_interactions
- status_note: We have only the single best observed post, so no mean exists. peak_post_engagement_rate is an UPPER BOUND, systematically higher than an average — never compare it against an average rate.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: ecommerce
- subcategory: dropship
- category_confidence: 0.9
- category_source: derived_from_recent_content
- topics: Facebook Ads competitor research, Google Shopping scaling, Ecommerce product validation, Scaling profitable stores using data from top performers
- bio: Pushing numbers, printing money

## Audience (inferred)
- status: present
- description: Ecommerce sellers and operators focusing on scaling via paid ads, especially Facebook and Google Shopping, with a bias toward data-driven, high-traffic niches
- roles: ecom_ops
- seniority: practitioner
- pain_points: Struggling to find profitable product ideas with proven traffic and conversion data, Having difficulty scaling Google Shopping campaigns without wasting budget on unoptimized setups, Lacking a clear, step-by-step system to replicate top-performing stores' strategies
- posts_analysed: 2
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.9
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 59
- authenticity_basis: engagement_observed
- credibility_score: 59
- flagged_for_review: false
- koinon_bd_grade: B — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [x_goatofecom#628100b6] Go to the Facebook ads library and find 10 stores with 200K+ monthly visitors Stay in one niche and find all of their best sellers from the last 30 days Copy them all to your store and regenerate all the images to make it unrecognisable from the source Find the most GOATED media  (https://twitter.com/GoatOfEcom/status/2073370394623365134)
- [x_goatofecom#f2e5e803] I PARTNERED UP WITH https://t.co/btODWmyFrE TO EXACTLY TEACH YOU HOW TO SCALE YOUR GOOGLE STORE FROM A-Z FOLLOWING MY SCALING METHOD. NO NONSENSE. NO BULLSHIT. ROLL IN TODAY AND MAKE ME HAPPY. https://t.co

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=78, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=78, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=110, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.5, freshness_days=78, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=110, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=110, method=platform_profile

## Freshness
- profile_checked_days_ago: 110
- content_analysed_days_ago: 78
- audience_updated_days_ago: 20

## AI retrieval
- indexing_requests: 13
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=x_goatofecom
- Request removal: https://koinonlink.com/claim?k=x_goatofecom
- Terms: https://koinonlink.com/for-ai