₹4.46 Cr Revenue in 6 Months Using Google Ads

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Luxury Women’s Apparel

Category

Premium / High AOV

Price Range

Upper Middle Class & Affluent Buyers

Target Audience

India


Market

Shopify + Google Ads

Platform

The Real Problem

Despite being a strong luxury brand, growth was unstable.

  • Sales were inconsistent day-to-day
  • Heavy dependence on brand search
  • Poor visibility on non-brand high-intent keywords
  • New customer acquisition was unpredictable
  • ROAS fluctuated heavily during scaling attempts

Revenue existed, but it wasn’t scalable or controllable.

Objective

  • Build predictable Google Ads revenue
  • Scale non-brand & new customer sales
  • Stabilize ROAS while increasing spend
  • Improve AOV without discount dependency

Strategy

What We Actually Did

0 1

Account Restructuring
(Foundation First)

  • Segregated Brand / Non-Brand / Competitor / Shopping / Remarketing
  • Isolated high-intent keywords to protect ROAS
  • Prevented brand cannibalisation by PMax
0 2

Non-Brand Scale Using PMax
(Done Properly)

  • Seeded PMax with validated search terms
  • Fed conversion-qualified audiences only
  • Asset groups split by:
    • Category
    • Price band
    • Intent level
0 3

Search Ads for Control & Stability

  • High-intent search campaigns for:
    • Generic luxury apparel keywords
    • Dynamic Search Ads to capture long-tail demand
  • Aggressive search term mining + negatives weekly
0 4

Shopping Ads for Volume

  • Feed optimisation:
    • Titles rewritten for intent, not brand vanity
    • Price & category prioritisation
  • Shopping remarketing layered with RLSA
0 5

Audience & Remarketing Engine

  • RLSA on search & shopping
  • In-market & affinity audiences for discovery
  • Device & demographic optimisation (luxury skew)

The Strategy

This was a full-funnel Google Ads rebuild, not surface-level optimization.

  • Execution highlights:
  • Re-structured the entire account (Brand, Non-Brand, Shopping & Performance Max)
  • Eliminated keyword and audience overlap to prevent internal competition
  • Reallocated budgets toward high-intent, revenue-driving campaigns
  • Optimized product feeds for Shopping & Performance Max visibility
  • Aggressive search term mining with continuous negative keyword expansion
  • Implemented value-based bidding aligned with actual revenue, not vanity metrics
Growth was present — predictability was not.

Results (6 Months)

Performance Snapshot

₹4.46 Cr

Revenue Generated

₹38.4 L

Ad Spend

11.68

Overall ROAS

Nov 2022 – April 2023

Duration

More importantly
  • Revenue became predictable
  • Non-brand contribution increased significantly
  • Scaling stopped breaking ROAS

Why This Worked

  • Clear intent separation across all campaign types
  • Shopping and Search treated as revenue engines, not traffic sources
  • Budget scaling driven by data stability, not daily fluctuations
  • Ad wastage controlled before scale, not after losses
Most brands fail here because they attempt to scale before fixing structure.

Key Takeaway

Google Ads can scale a kids apparel brand profitably —
but only when the account is engineered for control, not chaos.

  • This wasn’t luck.
  • This was system-driven execution.

Key Takeaways

This Is the Real Lesson

  • ROAS doesn’t come from hacks — it comes from structure
  • PMax works only when search & shopping are controlled
  • Luxury brands scale when:
    • Brand is protected
    • Non-brand is isolated
    • Remarketing is intentional, not lazy

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