# Rishabh Jain (@rishabhmjain) on x

- id: koinon:creator:x_rishabhmjain
- url: https://koinonlink.com/kol/x/rishabhmjain
- schema_version: 1.4

## Identity
- platform: x
- handle: rishabhmjain
- platform_url: https://twitter.com/rishabhmjain
- website: https://fermatcommerce.com
- country: United States
- entity_type: creator
- person_id: koinon:person:x_rishabhmjain

## Reach
- followers: 3258
- following: 640
- posts_total: 11636
- posts_observed: 3
- posts_per_day: 1.845
- account_age_days: 6308
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00583
- best_post_interactions: 19
- 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: ecom_ads
- category_confidence: 0.85
- category_source: derived_from_recent_content
- topics: incrementality measurement, attribution flaws, post-click experience, FERMAT Incrementality Report
- bio: CEO @fermatcommerce - Long time lover of ads and commerce

family, and fitness come first

## Audience (inferred)
- status: present
- description: E-commerce ad marketing practitioners, primarily brand marketing teams or advertisers focused on attribution and incremental measurement
- roles: ads
- seniority: practitioner
- pain_points: Attribution models fail to capture the incremental impact of post-click experiences, Difficulty in quantifying the actual incremental revenue and purchases driven by ads, Lack of tools to estimate the incremental contribution of specific platforms or tools
- posts_analysed: 3
- 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: 73
- authenticity_basis: engagement_observed
- credibility_score: 73
- 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_rishabhmjain#ef1ea370] The flaw of attribution? It tells you nothing about the incremental effectiveness of your post-click experience. That’s why we created @fermatcommerce's Incrementality Report. (https://twitter.com/rishabhmjain/status/1861442333558137110)
- [x_rishabhmjain#f659bef6] In this week’s TiPS (Tuesday in the Product Showcase), we’re talking all about incrementality.
- [x_rishabhmjain#cef9beb1] With our Incrementality Report, brands can now estimate the incremental revenue and purchases FERMÀT is driving for them.
- [x_rishabhmjain#97211a88] What

## 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=67, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=67, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=67, 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=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=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=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: 79
- audience_updated_days_ago: 67

## 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_rishabhmjain
- Request removal: https://koinonlink.com/claim?k=x_rishabhmjain
- Terms: https://koinonlink.com/for-ai