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Infinity Logistix: hypotheses in targeting

Case data Promotion features From hypothesis to LP creation ROI and key indicators Website: Infinity Logistix Industry: Logistics / dispatch service (trucking, USA) Geography: USA Model: B2B / C2C marketplace Budget: Up to $50 per hypothesis test (iterative testing, 70+ hypotheses over 14 months) Project start: Not spe

Preserved from an earlier website era Case study March 24, 2026

Case data
Promotion features
From hypothesis to LP creation
ROI and key indicators

Website:

Infinity Logistix

Industry:

Logistics / dispatch service (trucking, USA)

Geography:

USA

Model:

B2B / C2C marketplace

Budget:

Up to $50 per hypothesis test (iterative testing, 70+ hypotheses over 14 months)

Project start:

Not specified (case period — 14 months of work)

Result:

Stable flow of 40–50 leads per week with CPL of $13–20 thanks to segmentation of campaigns and focus on high-performing combinations (Car Hauler + B2C)

Result:

Improved lead quality and higher share of qualified leads through clear segmentation (truck driver, car hauler) and stronger focus on “warm” language audiences (Russian, Tajikistan, Uzbekistan)

Lead generation for a logistics dispatch service

Attract drivers who convert into signed contracts

Keep CPL within the $15–20 range

Increase the share of qualified leads

Rapidly test hypotheses (up to $50 per test)

Filter out unqualified leads at an early stage

Challenges of promoting logistics through Meta Ads

Cheap traffic

Cheap leads did not convert into signed contracts

No real deals

B2B audiences failed to generate real deals

Inappropriate traffic

Algorithms drove irrelevant, low-quality traffic

Budget without result

Broad campaigns wasted budget without delivering results

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Optimization stages: from “cheap leads” to stable contracts

Stage 1. Realizing that CPL ≠ results
At the start, campaigns produced “nice-looking” leads at $16–25, but the funnel data showed failure: people didn’t answer calls, didn’t match criteria, and didn’t convert into deals. It became clear that CPL is a superficial metric that does not reflect business outcomes. From that point, every hypothesis was evaluated based on lead movement toward qualification and contracts.
Stage 2. Hypothesis testing and audience discovery
We tested B2B, B2C, different language segments, and landing pages. B2B looked good on paper but didn’t generate contracts, while Typeform proved inefficient in cost per lead. In contrast, Russian-speaking and Central Asian audiences delivered higher CPL but significantly better lead quality.
Stage 3. Working with look-alike and quality filtering
We launched look-alike audiences and separate optimization events. Some hypotheses failed due to either high cost or lack of funnel progression. The best results came from LAL based on website visitors, where both lead volume and real movement toward deals appeared.
Stage 4. Funnel optimization and abandoning “cheap leads”
We shifted from “lead” optimization to deeper funnel events (funnel key events) and adjusted geography. CPL increased, but lead quality improved: more qualified calls and signed contracts. This reinforced a key principle — it’s better to pay more if it leads to real revenue.
Stage 5. Repositioning and focus on the Car Hauler segment
We restructured offers around real driver motivations and pain points. This revealed a strong segment — Car Hauler — which became a separate campaign with dedicated creatives and messaging. It resulted in scalable lead flow, gradually lower CPL, and consistent deal flow.
Stage 6. Failed experiment with broad automation
We tested a single broad campaign with automated budget allocation (Andromeda). It led to higher CPMs and CPCs without qualified leads. The experiment showed that in niche markets, relying solely on algorithms doesn’t work — precise segmentation and manual control are essential.

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Impact of systematic Meta Ads optimization

We built not just a set of campaigns, but a managed system: from segmentation to funnel design, where ads are evaluated not by CPL, but by contracts and lead quality. ROI and key metrics: what systematic hypothesis testing delivered As a result, advertising stopped being a source of random inquiries and became a predictable acquisition channel: clear segments, controlled unit economics, and a stable flow of drivers reaching contracts. Over 14 months:

40–50

Leads per week

To 13–20$

CPL reduced

70+

Hypotheses tested

Segments

Profitable segments identified