# Affise (@GetAffise) on x

- id: koinon:creator:x_getaffise
- url: https://koinonlink.com/kol/x/GetAffise
- schema_version: 1.4

## Identity
- platform: x
- handle: GetAffise
- platform_url: https://twitter.com/GetAffise
- website: https://affise.com
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 5015
- following: 5094
- posts_total: 1059
- posts_observed: 5
- posts_per_day: 0.266
- account_age_days: 3986
- verified: false
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.0002
- best_post_interactions: 1
- 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: Mobile Attribution (MMP), Customer Acquisition Strategy, Referral Marketing Automation, Performance Scaling
- bio: Affiliate Marketing and Mobile Attribution Platform.

## Audience (inferred)
- status: present
- description: App marketers and performance advertisers using mobile attribution platforms, mainly growth teams and digital acquisition specialists in mid-to-large app companies
- roles: ads
- seniority: practitioner
- pain_points: Struggling to choose the right Mobile Measurement Partner (MMP) due to complex evaluation criteria, Facing challenges in scaling customer acquisition without manual effort, Finding it hard to implement effective referral marketing automation
- posts_analysed: 4
- 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: 32
- authenticity_basis: engagement_observed
- credibility_score: 47
- flagged_for_review: false
- koinon_bd_grade: C — 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_getaffise#8801a8a2] Switching to a new MMP is a big decision, but asking the right questions can make all the difference. Our latest guide covers the essential questions every app marketer should ask before switching MMPs, so you can migrate with confidence. 🔗 https://t.co/DgQIYsDVgA https://t.co/R (https://twitter.com/GetAffise/status/2072675901624455385)
- [x_getaffise#2e7e0915] 🚀 We’re at Ace Alliance in Riga! If you’re attending Ace Alliance, let’s meet and talk performance, attribution, and scaling 🚀 See you in Latvia! https://t.co/3vzZFxUjcW
- [x_getaffise#3b569879] Customer acquisition doesn't have to be manual. Discover how referral marketing automation can help your business grow faster in our latest guide 👇 🔗 https://t.co/NxhH2c1wue #ReferralMarketing #CustomerAcquisition #PartnerMarketing #MarTech #Affise https://t.co/vLUlvwOCHE
- [x_getaffise#58b3062c] Choosing a Mobile Measurement Partner is one of the most important decisions for any app business. Our latest guide walks through the key criteria to consi

## 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=108, 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=108, 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=108, method=platform_profile

## Freshness
- profile_checked_days_ago: 108
- content_analysed_days_ago: 78
- audience_updated_days_ago: 20

## AI retrieval
- indexing_requests: 9
- 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_getaffise
- Request removal: https://koinonlink.com/claim?k=x_getaffise
- Terms: https://koinonlink.com/for-ai