Cases · Archive · Logistics · 2026

Infinity Logistix: content machine

Agency-era case study (2026).

This case was first published on the old humanswith.ai WordPress site between 2019 and 2024 — before the 2025 platform pivot. Performance, SEO, CRM, content, or growth work from that era. Modern AI-search cases (with per-engine Hermes data) live at /cases. Preserved here as written at the time.


Dynamics of “reach” across social platforms (Instagram, YouTube, TikTok)

Project:

Infinity Logistix

Industry:

Logistics / dispatch service (trucking, USA)

Geography:

USA

Model:

B2B / C2C marketplace

Start date:

March 2025

Case period:

March – August 2025 (6 months)

Result:

Built a content testing system that systematically identified viral formats and engagement triggers

Result:

Peak reach of individual videos reached up to 26K views thanks to validated high-performing mechanics

Controlled experiment to build a content machine

1

Identify which formats and triggers go viral

2

Develop 3+ formulas for viral content

3

Separate strategies across YouTube, TikTok, and Instagram

4

Build a library of working content triggers

5

Scale production to 10–20 videos per week

Challenges of launching a content strategy in the logistics niche

CA Triggers

No clarity on which triggers resonate with the audience

Better coverage

Uncertainty about which platforms deliver the best reach

Video formats

Lack of understanding of which video formats go viral

Unstable results

    • Unstable performance: from 100 to 10K views per video

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Testing stages

- Stage 1. Identifying baseline content formats in the logistics niche

We started by testing basic content formats to understand what could actually work in logistics. We experimented with memes, parking footage, accident-related content, posting timing, and drone shots. Even at this early stage, performance varied significantly from 100 to 26K views.

Key takeaway: memes delivered stable reach, while accident-related content was not suitable due to platform restrictions.

- Stage 2. Shifting from formats to emotional audience triggers

We moved from format-based testing to analyzing emotional triggers. We tested themes like income, fear, disputes, immigration, and personal stories. It became clear that triggers — not production quality — drive reach and engagement.

The strongest performance came from income and immigration-related topics, reaching up to 26K views.

- Stage 3. Platform-specific strategy: YouTube, TikTok, Instagram

We tested whether the same content could work across multiple platforms. Results showed clear differences: YouTube, TikTok, and Instagram require distinct approaches. We also found that matching titles on TikTok increased views by +190%.

Key takeaway: there is no universal content format.

- Stage 4. Optimization and scaling of working formats

At this stage, we optimized existing winning formats instead of searching for new ones. We tested thumbnails, titles, trigger words, and combinations of approaches. This resulted in CTR growth of up to +30% and stable performance up to 10K views.

Conclusion: scaling comes from precise optimization, not constant new hypotheses.

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Effect of implementing a content testing system

We built a strict content testing system based on hypotheses, experiments, and analytics, which allowed us to move from random performance to controlled growth.

ROI and key metrics: what content experimentation delivered

Through systematic testing of formats, triggers, and platforms, we built a predictable model for producing viral content within 6 months.

Over 6 months:

26K views

Memes in the logistics niche consistently reach up to 26K views

Triggers

Emotional triggers (income, immigration, fear) increase reach to 26K+ views and significantly boost engagement

Multiple values

Platform differences are substantial: YouTube — up to 26K views, TikTok — ~500, Instagram — up to 2.2K

+30% CTR

Format optimization (titles, thumbnails) delivered up to +190% views growth and +30% CTR increase


End of archived case

Want a modern case? Hermes data, not Google Analytics.

The seven modern cases at /cases use a different proof model: per-engine AI mention rate, citation tracking across nine engines, and weekly delta reports. Different category, different proof, different era.