Capabilities
01Acquire
02Convert
03Scale
Google Ads
Search, Shopping, PMax for pipeline.
Meta Ads
Facebook & Instagram, built for ROAS.
Microsoft Ads
Bing Search & Audience Network.
SEO Core
Technical, on-page, content SEO.
Local SEO
Multi-location GBP and map pack.
Content Marketing
Blog strategy and pillar content.
AEO / AI Search New
ChatGPT, Perplexity, SGE visibility.
Influencer Marketing New
Creator-led with attribution.
Performance Creatives Hot
Video, UGC, static, tested weekly.
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C
Convert / Experimentation

Experimentation programs that compound.

A/B and multivariate testing programs run as a discipline rather than as a tool. Hypothesis backlog, statistical rigor, experiment archive, and cross-functional learnings that compound across quarters rather than dying with the latest test.

CapabilityA/B Testing & Experimentation
SurfaceExperimentation
EngagementNational · North America
Rating4.8 ★ · 86 reviews
By the Numbers
12tests
Average concurrent experiments on mature accounts
34%
Win rate across documented experiment archive
$420K+
Average annual revenue lift attributable to experiment program
47%
Of false-positive winners caught before scaling
What We Run

A/B Testing & Experimentation, the disciplined way.

A backlog tied to data, not opinions.

Every test starts with a hypothesis tied to a behavioral observation. The backlog is scored by impact, confidence, and ease (ICE), and the top of the backlog drives the next quarter of testing.

Statistical discipline.

Tests run to power. Bayesian or frequentist depending on the test design. We document false positives alongside wins, and we do not scale partial winners.

Experiment archive.

Every test is documented: hypothesis, design, sample size, result, learning. The archive compounds organizational knowledge so the team is not relearning the same lessons every quarter.

Cross-functional handoff.

Experiment learnings feed paid media, content, lifecycle, and product. The discipline is its own program, but the value is in how the learnings cross-pollinate.

Our Method

How we run a/b testing & experimentation engagements.

/01 Backlog
Hypothesis bank, scored.

We build the test backlog: 20-40 hypotheses tied to behavioral observations, scored by ICE, ranked for the next quarter.

/02 Design
Power calculation, instrumentation.

Each test designed with proper power calculation, success metrics, and instrumentation. No test ships without a written design doc.

/03 Run
Multiple concurrent experiments.

Multiple tests running concurrently where traffic and surface independence support it. Daily monitoring, weekly review, statistical close on schedule.

/04 Archive
Document and recompose.

Every test archived with full design, result, and learning. Quarterly recompose: what is the program teaching us, where is the next round of opportunity?

Where We Run This

Active a/b testing & experimentation engagements.

Experimentation programs pair with CRO and analytics work. Browse the convert surfaces.

CRO programs Web development Landing pages Analytics & Attribution
FAQ

Common questions.

How is this different from CRO?

CRO is the broader discipline. A/B testing is one method inside CRO. We run experimentation as a dedicated program when the account has the traffic, the surfaces, and the organizational appetite to make it a competitive advantage.

Do we have enough traffic to A/B test?

Below 10,000 monthly conversions per surface, classical A/B testing struggles. We use bandit-style allocation, qualitative research, and heuristic-driven sequential testing instead. The discipline still applies, the methods adjust.

Can you work with our existing testing tool?

Yes. Optimizely, VWO, AB Tasty, Convert, GrowthBook, and custom feature flags are all fine. We have opinions but no tooling religion.

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