# Zendesk (@Zendesk) on x

- id: koinon:creator:x_zendesk
- url: https://koinonlink.com/kol/x/Zendesk
- schema_version: 1.4

## Identity
- platform: x
- handle: Zendesk
- platform_url: https://twitter.com/Zendesk
- website: https://zdsk.co/ZendeskLinkedIn2025
- country: United States
- entity_type: creator
- person_id: koinon:person:linkedin_zendesk

## Reach
- followers: 102610
- following: 1690
- posts_total: 57958
- posts_observed: 585
- posts_per_day: 8.623
- account_age_days: 6721
- verified: false
- size_tier: kol

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00006
- best_post_interactions: 6
- 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: 0.85
- category_source: derived_from_recent_content
- topics: Zendesk custom objects and templates, AI + human expertise in customer service, Startup customer support pitfalls, Unifying fragmented tools with AI
- bio: More self-aware than your ex. Self-improving AI agents are here. | 🔗: https://t.co/fZvAP0hBWs

## Audience (inferred)
- status: present
- description: Customer support and CX teams at startups and growing companies evaluating or using Zendesk
- roles: cx
- seniority: practitioner
- pain_points: Building custom objects and data models from scratch in support tools, Fragmented data and disconnected systems blocking AI adoption, Customer support issues slowing down startup growth, Trying to bolt AI onto legacy fragmented support stacks
- 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: 50
- authenticity_basis: engagement_observed
- credibility_score: 85
- 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_zendesk#d4155b11] Still building every custom object from scratch? Custom object templates provide prebuilt objects, fields, and relationships for common use cases like products, orders, contracts, projects, and subscriptions — so you can get up and running faster. https://t.co/u6Xt92isYK (https://twitter.com/Zendesk)
- [x_zendesk#7d1b285b] In a world of fragmented data and disconnected tools, it’s time to build a future where AI and human expertise unite. In this sponsored @WSJ post, discover how Zendesk and @AWS are laying the foundation for the agentic contact center: https://t.co/um90RmmziZ https://t.co/ZfsHvUkV
- [x_zendesk#377c226f] Growing your startup is hard enough — don’t let missteps in customer support hold you back. This Zendesk Insights blog reveals the three things most startups get wrong. Learn how to build a unified CX that drives retention and growth: https://t.co/N3QFtqZBKJ https://t.co/Rd9XoMMX
- [x_zendesk#72187c5b] Stop plugging AI into a fragmented system. In this Zen

## 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=77, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=77, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=77, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=77, 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=77, 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=77, 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: 77
- audience_updated_days_ago: 77

## AI retrieval
- indexing_requests: 7
- 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_zendesk
- Request removal: https://koinonlink.com/claim?k=x_zendesk
- Terms: https://koinonlink.com/for-ai