Rebuilding the growth engine for a luxury DTC brand

Context

The company already had media channels, pixels, e-commerce infrastructure, email tools, and a website. What it did not have was a reliable operating system connecting them.

The problem wasn’t one bad campaign. The system couldn’t learn.

Constraint

• Measurement. Conversion tracking existed but wasn’t consistently validated. Primary Google conversions and landing-page events needed QA. Server-side measurement, CAPI, and other conversion plumbing were incomplete, and platform reporting carried too much weight.
• Media architecture. Campaigns lacked consistent audience segmentation, exclusions, retargeting swim lanes, funnel stages, and a cross-channel testing structure.
• Optimization. Cadence was inconsistent. Media needed active management across bidding, budgets, inventory, placements, devices, geography, dayparting, audiences, creative refreshes, and product economics.
• Funnel. Landing experiences were too generic: message match, offer placement, CTA clarity, page structure, and imagery all needed work.
• Lifecycle. Behavioral email, SMS, referrals, retargeting, repeat-purchase programs, and CRM data were materially underused.

The business could see activity. It could not reliably explain what was driving growth.

My role and scope

I was the hands-on growth lead across acquisition, measurement, CRO, lifecycle, BI, and optimization, with support from a small team and freelancers where needed.

Approach

Each stage was restructured to inform and strengthen the next: media, landing experience, conversion, CRM and lifecycle, repeat purchase, and referral, with a BI and decision layer reading every stage.

• Media. Rebuilt Google Ads campaigns from scratch; implemented and restructured Shopping and Performance Max; rebuilt audiences, exclusions, retargeting, and funnel swim lanes; segmented by behavioral, demographic, device, geographic, and commercial signals.
• Commerce. Restructured Merchant Center and the product feed; segmented products by margin, category, bestseller status, and commercial priority; built perishability, inventory, and product economics into media decisions.
• Measurement. Validated GTM, pixels, conversion events, and landing-page firing; improved server-side or alternative measurement where necessary; introduced consistent naming and tracking conventions.
• Experimentation. Naming structures to compare hooks, CTAs, audiences, concepts, and angles across channels. A repeatable test-and-iterate system replaced ad hoc creative launches.
• Conversion. Simplified landing-page language, moved offers and value propositions higher, used clearer and repeated CTAs, and tightened message match between acquisition and landing experience.
• Lifecycle. Implemented behavioral email and SMS, strengthened retargeting, introduced referral mechanics, and supported repeat-purchase and retention programs.
• Decision system. Built BI and reporting for the client and for ongoing optimization, regularly pulled and cleaned cross-channel data, and used the evidence to decide what to scale, change, or stop.

Outcomes

Return on spend and conversion rate rose together. Average blended ROAS across the engagement was ~5x, up from ~1.2x during a weak period, and landing-page conversion rose from ~8% to 33.5% after the rebuild. Over a 60-day Google Ads window, paid clicks scaled through the holiday period to 20K+ clicks and 1,380+ conversions at ~$39.67 per conversion.

Across the engagement, the business saw ~1,000% year-over-year revenue growth from a smaller starting base, and ~250% month-over-month growth during a major scaling period. Repeat purchasing became a meaningful revenue contributor, referral became an active acquisition channel, and behavioral lifecycle marketing became part of the operating system.

Revenue growth reflects the whole system, not any single campaign. Figures are approximate, from engagement reporting.

What changed

Growth improved because the business finally had a system that could learn.

• Trustworthy measurement. Decisions no longer depended primarily on isolated platform dashboards. Tracking, analytics, BI, and cross-channel reporting became one decision process.
• Repeatable optimization. Media gained clearer structures, testing conventions, audience logic, exclusions, optimization routines, and reporting standards.
• Compounding growth. Acquisition fed lifecycle. Lifecycle supported repeat purchasing. Customers generated referrals. Better data then improved acquisition again.