Proof

Proof That Makes The Logic Visible

Not just outcomes — the structured reasoning behind each diagnosis, what was actually causing the problem, and what changed as a result. Evidence-based proof designed for ecommerce decision-makers.

—Avg. Conversion Uplift

—Avg. Attribution Accuracy Gain

—Total Revenue Influenced

—Avg. Delivery Window

All Case Studies

— conversion rate increase

A mid-market fashion brand had declining conversion despite steady traffic. Diagnosis revealed a misalignment between ad landing pages and product search intent — visitors were entering at the wrong funnel stage. Restructuring the entry logic resolved it without increasing ad spend or redesigning the store.

Issue diagnosed:Funnel entry misalignment

What was fixed:Landing page sequencing, ad-to-page intent match

Window:6 weeks from diagnosis to result

— improvement in attribution accuracy

A home goods brand running six-figure monthly ad spend had silent tracking failures for four months. Every optimization decision during that period was made on false data. Stabilization sprint identified three critical tracking failures, corrected server-side and client-side attribution, and restored data integrity across all platforms.

Issue diagnosed:Silent tracking failure across GA4, Meta, TikTok

What was fixed:Server-side tracking, event taxonomy, attribution alignment

Window:4 weeks to full stabilization

— recovered monthly revenue

A wellness brand's checkout abandonment rate was running 60% above category benchmarks. Conversion sprint identified four structural friction points — three in the checkout UX flow, one in trust signal architecture before the payment step. Revenue impact was measurable within the first full billing cycle post-launch.

Issue diagnosed:Checkout UX friction + trust signal gap

What was fixed:Checkout flow, trust architecture, mobile experience

Window:5 weeks from sprint kickoff to live

— revenue per session increase

An apparel brand with three active agency relationships and declining performance could not identify the root cause. Each vendor pointed to a different problem. The diagnosis produced a single ranked view: product page trust signals were failing at the critical decision moment. The sprint resolved it in four weeks.

Issue diagnosed:Product page trust signal failure

What was fixed:Trust architecture, review display, social proof placement

Window:7 weeks total (diagnosis + sprint)

— ROAS increase after correct measurement

A supplements brand believed their paid campaigns were marginally profitable at 2.1x ROAS. After stabilization revealed attribution errors — primarily duplicate conversion events and cross-device gaps — actual ROAS was 3.2x. The brand had been under-investing in a profitable channel due to inaccurate measurement.

Issue diagnosed:Duplicate conversion events, cross-device attribution gaps

What was fixed:Event deduplication, enhanced conversions, CAPI setup

Window:3 weeks to corrected measurement baseline

— mobile conversion rate improvement

A furniture brand's mobile experience was structurally broken — navigation patterns designed for desktop, product images not optimized for mobile viewport, and checkout steps that created excessive friction on smaller screens. A mobile-first conversion sprint resolved the gap in six weeks.

Issue diagnosed:Mobile UX structural failure

What was fixed:Mobile navigation, image delivery, checkout simplification

Window:6 weeks

See If This Applies to You

Every Case Started With a Diagnosis

Before any of these results existed, there was a structured diagnostic conversation. That is where yours starts too.

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Proof That Makes The Logic Visible

All Case Studies

Conversion Was Not the Problem — the Funnel Entry Was

Optimization Was Happening on Four Months of Broken Data

The Checkout Was Losing 34% of High-Intent Buyers

Three Vendors, One Real Problem — Identified in Week One

ROAS Was Positive — But the Numbers Were Wrong

Mobile Was Delivering 60% of Traffic — and 18% of Revenue

Every Case Started With a Diagnosis