Apparel
BigAtom Case Study – Embedded
Case Study · Luxury Apparel

How Aza Fashions Boosted
ROAS by 33% with BigAtom

Scaling catalog ads with SKU-level intelligence, Smart Product Sets, and Stop-Loss Automation — from broad campaigns to product-led performance management.

0%
Overall ROAS increase
0%
Ad spend saved
1.2×
Hero Product revenue
0%
Higher ROAS (Hero)

“With BigAtom, we finally had product-level visibility across our entire catalog. Automations like stop-loss helped us reduce wasted spend and scale confidently, both during BAU and high-pressure sale periods.”

CMO, Aza Fashions
Global Luxury Fashion Brand
The Challenge

A large catalog with no SKU-level visibility

Aza Fashions couldn’t see which products were worth scaling, which had untapped potential, and which were burning budget without results.

About the Brand

Aza Fashions is a prominent player in India’s luxury fashion market — catering to men, women, and children through a robust online platform and physical stores.

With a large, complex catalog, the team needed to move from broad campaign metrics to surgical, product-led performance management to grow profitably on Meta and Google.

Limited SKU-Level Insights

No clarity on per-product ROAS or ad spend, making it impossible to act on individual product signals.

Missed High-Potential Products

Strong-converting products were starved of budget, leaving significant revenue opportunities undiscovered.

Wasted Spend on Poor Performers

Underperforming SKUs continued consuming budget without profitable returns, dragging down ROAS.

The Approach

Product Performance Management

BigAtom divided the entire catalog into four performance-based segments — each with a clear strategy for budget allocation, scaling, or exclusion.

Low Spend · High ROAS

High Potential Products

Hidden gems with strong ROAS but insufficient budget. Tomorrow’s bestsellers waiting to be unlocked.

High Spend · High ROAS

Hero Products

Proven top performers delivering strong returns. Priority SKUs to scale further with more budget.

Low Spend · Low ROAS

Low Discoverability

Products with limited exposure. Need visibility and testing before a confident budget decision can be made.

High Spend · Low ROAS

Non-Performers

SKUs consuming budget without profitable returns. Candidates for automated exclusion to save ad spend.

Execution

Three targeted strategies

Precision automation rules across Meta and Google turned the segmentation framework into measurable, compounding results.

01

Scaled Hero Products with Smart Sets

40%
Higher ROAS vs generic

BigAtom identified top-performing SKUs and built dedicated Smart Product Sets — concentrating budget on proven winners to compound returns using strict performance thresholds.

Meta Smart Product Set Rules
Meta Ad Spend ₹5,000
AND
Blended ROAS
02

Promoted High Potential Products

26%
Higher ROAS vs generic

Products with low ad spend but strong ROAS were surfaced and given dedicated budget — discovering hidden winners and scaling the next wave of bestsellers.

Meta High Potential Rules
Meta Ad Spend ₹5,000
AND
Blended ROAS
03

Automated Stop-Loss for Poor Performers

19%
Ad spend saved

Stop-Loss Automation automatically excluded underperforming SKUs from Meta and Google catalog ads — eliminating manual intervention and plugging budget leaks.

Google PMax · 14-Day Window
> Ad Spend ₹10,000
AND
< ROAS
Performance chart 1
Performance chart 2
Data & Results

Product-led performance metrics

Shifting to SKU-level precision delivered compounding gains across every dimension of catalog performance.

0%
Overall ROAS increase
0%
Ad spend saved
1.2×
Hero Product revenue
0%
Higher ROAS (Hero)
0%
Higher ROAS (High Potential)

ROAS Comparison: Smart Sets vs Generic Ads

Hero Products vs Generic Catalog +40% Higher
Smart Product Set
Generic Catalog

Related

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