# Barry Hott ☄️ (@binghott) on x

- id: koinon:creator:x_binghott
- url: https://koinonlink.com/kol/x/binghott
- schema_version: 1.4

## Identity
- platform: x
- handle: binghott
- platform_url: https://twitter.com/binghott
- website: http://bhott.com
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 34694
- following: 3999
- posts_total: 42745
- posts_observed: 1
- posts_per_day: 6.63
- account_age_days: 6447
- verified: true
- size_tier: koc

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.0011
- best_post_interactions: 38
- 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: software
- subcategory: martech
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: Meta ad attribution, CPA measurement, performance marketing strategy, ad campaign optimization, machine learning in advertising
- bio: Caring waaaay too much about ads since 2008

Building https://t.co/5OLof3cdhh - Monitor your site, competitors, and search results for changes.
🎥 https://t.co/U6aZyaU5aD

## Audience (inferred)
- status: present
- description: Ads professionals focused on performance marketing, especially those managing Meta ad campaigns and attribution strategies
- roles: ads
- seniority: practitioner
- pain_points: Struggling to accurately measure CPA due to Meta's changing attribution models, Confused about whether to enable incremental attribution despite potential negative impact on reported CPA, Frustrated by lack of clarity on how Meta's machine learning algorithms assign credit to conversions
- posts_analysed: 5
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 91
- authenticity_basis: engagement_observed
- credibility_score: 91
- flagged_for_review: false
- koinon_bd_grade: A — 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_binghott#d4d7814c] Meta's incremental attribution setting will probably make your reported CPA look worse. (https://twitter.com/binghott/status/2071657715089166832)
- [x_binghott#d46b6201] Flip it on anyway.
- [x_binghott#bbe1d4d7] It comes down to the theory and logic. Would you rather Meta's advanced machine learning algo:
- [x_binghott#2d095f53] A. Chase the most conversions it can take credit for (people who clicked or even just saw the ad inside some arbitrary window, plenty of whom were gonna buy anyway)
- [x_binghott#67a40acd] orrr
- [x_binghott#38700925] B. Move people who were

## 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=79, 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=79, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=115, 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=79, 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=115, 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=115, method=platform_profile

## Freshness
- profile_checked_days_ago: 115
- content_analysed_days_ago: 79
- audience_updated_days_ago: 20

## AI retrieval
- indexing_requests: 12
- 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_binghott
- Request removal: https://koinonlink.com/claim?k=x_binghott
- Terms: https://koinonlink.com/for-ai