DTC Apparel Brand: Google Ads Rebuild to 6.05× Blended ROAS

Documented Account Comparison

Performance across two documented periods: before and after the rebuild

Google Ads performance from the previous 2024 period compared with the subsequent 17-month MESAscale management period. The reporting periods differ in length and are shown exactly as recorded.

Previous Period 4.79× ROAS Jan 1 – Dec 31, 2024 Previous management

+26% Relative difference

Subsequent Period 6.05× Blended ROAS Jan 1, 2025 – May 31, 2026 MESAscale management

Before Jan 1 – Dec 31, 2024 · Previous Management

Google Ads account during the previous management period showing 4.79× blended ROAS, full year 2024. Campaign names redacted per NDA.

After Jan 1, 2025 – May 31, 2026 · MESAscale

AU$755K Attributed Revenue AU$124K Managed Spend

Google Ads account during the MESAscale management period showing 6.05× blended ROAS, Jan 2025–May 2026. Campaign names redacted per NDA.

Campaign names withheld under client NDA. Performance data documented from the client's Google Ads account. All figures in Australian dollars (AU$). The two periods span different calendar years and seasonal cycles (2024 full year vs. January 2025–May 2026); they are shown exactly as recorded and should not be treated as a controlled comparison.

The feed titles were built from the original Shopify launch and never revised. They used collection names that were internal references, not the terms buyers actually type: material, cut, fit. Impression share on product-specific queries was low.

The 4.79× in 2024 looked reasonable on the surface. Managed largely in-house with occasional freelancer support, the account had never had a structural audit. The first week surfaced four issues: product titles inherited from the original Shopify build, no first-party purchaser data feeding Performance Max, broad Search covering a wide mix of generic and informational queries, and overlapping campaign responsibilities.

Six weeks of rebuild work. The account then ran for 17 months at 6.05× blended ROAS on AU$124K in managed spend.

Findings from the initial audit

Campaign responsibilities had become difficult to govern

Several ad groups and campaigns covered closely related product themes. Query routing, budget interpretation, and performance ownership had grown murkier as structure accumulated over time. Not literal auction self-competition. Unclear ownership.

Broad match was diluting Search budget

Broad match was pulling in generic fashion searches, editorial queries, and informational lookups alongside higher-intent product demand. Those searches consumed budget without converting and made it harder to concentrate spend on the terms closest to purchase intent.

Purchaser data wasn't feeding Performance Max

Performance Max was running without the brand's verified purchase history as a Customer Match signal. The account's strongest first-party asset hadn't been uploaded.

Product titles used internal collection names, not buyer search terms

Feed titles hadn't been touched since launch. They reflected internal collection naming rather than product attributes shoppers search: material, cut, fit, color. Impression share on high-intent queries was low.

If ROAS has been flat for several quarters, more optimization inside the same structure won't move it. Sometimes the account needs a rebuild, not another round of adjustments.

The Growth Diagnostic is designed to identify exactly those kinds of constraints →

Account Restructure

From Accumulated Complexity to Clearer Campaign Roles

Accumulated Architecture · Previous Period

Brand Search

Generic Search

Broad Keyword Groups

Competitor Search

DSA

Shopping & Automation

Shopping 1

Shopping 2

Performance Max

Structural characteristics

Unclear query coverage responsibilities

Overlapping ad group themes

Limited first-party audience signals

Product titles from Shopify launch

4.79× ROAS · 2024

Clearer Campaign Roles · MESAscale

Role 01 Brand Search Capture and protect demonstrated brand demand

Exact and phrase targeting
Competitor exclusions where appropriate
Query-level visibility and governance

Role 02 Shopping Feed-led product coverage with tighter query controls

Product titles rebuilt around buyer search language
Search-term and exclusion governance
Target ROAS bidding strategy

Role 03 Performance Max Broader product and audience coverage

Verified purchaser data added as Customer Match audience signal
Asset groups organized by collection
Feed and audience signals established

6.05× Blended ROAS · Jan 2025 – May 2026

Previous Documented Period 4.79× Jan 1 – Dec 31, 2024

+26% Relative ROAS difference

Subsequent Documented Period 6.05× Jan 1, 2025 – May 31, 2026

What We Changed, and in What Order

Completed a full account audit

Week one: campaign responsibilities, Search query quality, product-feed structure, audience signals, and account-level exclusions. Work was sequenced by likely impact: feed language and audience signals first, Search match-type cleanup second, campaign consolidation third.

Rebuilt the account around three clearer campaign roles

We consolidated overlapping structures and organized around three campaign types: Brand Search, Shopping, and Performance Max. Negative-keyword controls, campaign settings, and query governance established clear ownership across each. DSA campaigns and broad-match groups without conversion evidence were paused.

Tightened Search targeting and expanded query controls

We reviewed 90 days of search-term data. High-intent terms were moved into deliberate exact and phrase match. Editorial searches, informational lookups, and wholesale queries were excluded where conversion data supported it.

Added verified purchaser data as a Performance Max audience signal

We uploaded the brand's verified purchase history via Customer Match, giving Performance Max a direct first-party reference from real buyers rather than inferring audience from site visits alone.

Rebuilt product feed titles around buyer search language

We rewrote feed titles for the top revenue-driving SKUs to lead with what buyers search: gender, product type, material, color, fit. "Women's Linen Wide-Leg Trousers · Beige · Relaxed Fit" instead of "Coastal Edit Relaxed Trouser." Visibility on those targeted queries improved within the first reporting window.

A 4.79× account and a 6.05× account can look identical from the outside. Same products, similar ads. The difference is underneath: how customer data, product feeds, Search queries, and campaign responsibilities are organized. Here, the structural work happened in the first six weeks and held for 17 months.

Documented Management Period · Jan 2025 – May 2026

6.05× Blended ROAS Across 17 Months

The 6.05× reflects the complete 17-month management period, not a selected window. The prior 2024 period ran 4.79×. That's a 26% relative difference across two documented periods of different length and different seasonal cycles. All figures in AU$.

Metric

Previous Period
Jan 2024 – Dec 2024

MESAscale
Jan 2025 – May 2026

Change

Blended ROAS

4.79×

6.05×

+26%

Attributed Revenue

Not documented

AU$755K

—

Managed Spend

Not documented

AU$124K

—

Documented Period

Jan 1 – Dec 31, 2024

Jan 1, 2025 – May 31, 2026

17 months

Management

Previous agency

MESAscale

—

All performance data sourced from the client’s Google Ads account. Platform-attributed revenue. Reporting periods differ in length and are shown exactly as recorded.

Clearer campaign roles, stronger product data, tighter Search controls, purchaser data in Performance Max. That architecture held for 17 months without requiring another rebuild. The structural work was front-loaded. What followed was ongoing governance, not repeated reinvention.

All performance data sourced directly from the client's Google Ads account. Platform-attributed revenue. Reporting periods differ in length.

Phil Chan Head of Growth MESAscale

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About the strategist

Phil has spent over a decade in paid media across agency, enterprise, and ecommerce. Most of that time in senior account leadership, directing multi-channel portfolios at seven-figure monthly scale.

He now works directly with established ecommerce brands through MESAscale, focusing on Google, Meta, and YouTube. His work centers on account architecture: where spend leaks, how cross-channel signals conflict, and which structural changes hold over time.

10+ Years in Paid Media Enterprise + Ecommerce Google · Meta · YouTube

DTC Apparel Brand. Structure and feed rebuilt. 6.05× ROAS on AU$124K over 17 months.

Performance across two documented periods: before and after the rebuild

Findings from the initial audit

From Accumulated Complexity to Clearer Campaign Roles

What We Changed, and in What Order

6.05× Blended ROAS Across 17 Months

Find out whether the structure underneath your ROAS is still holding.