What were the results of the AI-Native GTM rebuild?

7x conversion and 43% lower CPL on flat paid spend for an established B2C brand.

What did the rebuild involve?

Three layers: a 30-page AEO-native, technical-SEO-forward site; a brand and voice system with one narrative source; and paid media rebuilt against the new narrative and landing architecture.

Why AEO rather than SEO alone?

Pages were structured to answer the questions buyers actually ask AI assistants, with FAQ schema, canonical mapping, entity-consistent naming, and build-time SEO validation so regressions fail the build.

← Back to Portfolio & Studio

Case Study

B2C Growth Rebuild

Part of the AI-First GTM & Marketing practice. Also in the Marketing & GTM Portfolio under Growth & Demand.

End-to-end GTM for an established B2C brand: 7x conversion and 43% lower CPL on flat paid spend.

Business constraint

An established consumer brand whose acquisition declined 40% year over year entering a seasonally slow period. Traffic remained, but intent did not convert efficiently and paid spend carried much of the acquisition burden.

My decision

Own implementation end to end and rebuild the website, messaging, collateral, and paid media together. I rejected an agency-led redesign because its delivery timeline extended beyond the demand window and would have preserved dependence on outside implementation for future changes.

Tradeoff

Changing multiple layers simultaneously meant the effect of any one intervention could not be isolated. I accepted that limitation in exchange for speed and a coordinated improvement to the overall customer journey.

Result

Over seven weeks on flat daily spend, conversion increased from 0.52% to 3.82% and CPL fell from $120 to $69 (43%). The combined results supported reallocating $10K per month from agency spend into new growth programs.

How it was built

Discoverability layer. A 30-page AEO-native, technical-SEO-forward site: FAQ schema, canonical mapping, entity-consistent naming, and build-time SEO validation so regressions fail the build instead of the quarter. Pages were structured to answer the questions buyers actually ask AI assistants, not just to rank.

Narrative layer. A brand and voice system with one narrative source, so site, collateral, and ads stop arguing with each other. Code-based collateral made the system reproducible rather than a one-time design deliverable.

Demand layer. Paid media rebuilt against the new narrative and the new landing architecture - audience structure, creative, and destination pages moved together instead of being optimized in isolation.

AI-Native GTM Rebuild before and after: a stale site, leaking paid spend, no review loop, no metrics and outdated collateral replaced by an AEO-native site, rebuilt paid campaigns, an AI-visible review loop, measurable AI visibility and standardized messaging, with a 7x lift in conversion rate. — Before and after: one system for strategy, build, measurement, and iteration.

FAQ

What were the results of the AI-Native GTM rebuild?: 7x conversion and 43% lower CPL on flat paid spend for an established B2C brand.
What did the rebuild involve?: Three layers: a 30-page AEO-native, technical-SEO-forward site; a brand and voice system with one narrative source; and paid media rebuilt against the new narrative and landing architecture.
Why AEO rather than SEO alone?: Pages were structured to answer the questions buyers actually ask AI assistants, with FAQ schema, canonical mapping, entity-consistent naming, and build-time SEO validation so regressions fail the build.