TL;DR
This case study explains how a DTC fragrance brand (Perfume Gyaan) transformed Meta Ads performance over 6 months (Jul–Dec 2025) by shifting from an unstructured, low-efficiency setup to a full-funnel, ROAS-first Meta Ads system. By fixing weak TOF signal quality, audience overlap, and under-leveraged remarketing-and implementing catalog-led discovery, interest + lookalike layering, and disciplined ABO scaling-the brand scaled profitably, generating ₹1.99 Cr in revenue on ₹12.53L spend, achieving a 15.91 blended ROAS.
Quick Highlights
- Industry: Fragrance / Perfume (DTC)
- Market / Geography: India
- Business Model: DTC eCommerce
- Channels Used: Meta Ads (Facebook & Instagram)
- Duration: 1 Jul 2025 – 31 Dec 2025
- Primary KPI: Purchase ROAS
- Result Achieved: 15.91 Blended ROAS
- Strategy Type: Full-Funnel ROAS Scaling (ABO)
- Key Levers: Catalog TOF, Interest + Lookalike audiences, Remarketing optimization
Case Study Snapshot
| Entity | Attribute | Value |
| Brand Type | Industry | DTC Fragrance |
| Market | Geography | India |
| Channel | Primary Platform | Meta Ads |
| Strategy | Framework Type | Full-Funnel ROAS Growth |
| KPI | Primary Metric | Purchase ROAS |
| Result | Outcome | 15.91 ROAS |
| Timeline | Duration | 6 Months |
Client Overview
Perfume Gyaan is a DTC fragrance brand offering premium perfumes designed for everyday wear and special occasions. While the brand had strong product quality and growing demand, Meta Ads performance earlier lacked consistency due to fragmented funnel execution and inefficient audience utilization.
- Website: perfumegyaan.com
- Positioning: Affordable luxury fragrances with strong gifting and daily-wear appeal
Business Objective
The primary objective was to move from campaign-level experimentation to a predictable, profitability-first Meta Ads growth system that could scale revenue without inflating acquisition costs.
Rather than chasing short-term spikes, the focus was on building a repeatable full-funnel acquisition and remarketing engine driven by ROAS discipline and first-party data.
Core Objectives
- Increase qualified reach across high-intent fragrance audiences
- Scale Meta Ads revenue without CPC and CPM inflation
- Achieve consistent profitability using ROAS-focused optimization
- Build a structured TOF → MOF → BOF system
- Turn remarketing into a primary profit lever
Why This Case Study Matters
Most DTC fragrance brands struggle to scale Meta Ads profitably because they:
- Increase budgets before fixing signal quality
- Rely heavily on cold interest targeting in a crowded category
- Fail to use catalog campaigns effectively at the top of the funnel
- Treat remarketing as a support layer instead of a primary profit driver
As a result, brands see short-term spikes in revenue but unstable ROAS and rising acquisition costs over time.
This case study matters because it demonstrates how a systematic, full-funnel Meta Ads framework-focused on signal quality, first-party data, and ROAS discipline-can transform inconsistent performance into predictable, scalable profitability, even in a highly competitive DTC fragrance market.
Challenges Before Optimization (Jan–Jun 2025)
During the Jan–Jun 2025 period, Meta Ads performance showed clear limitations despite steady spend.
Key Issues Identified:
- Inconsistent ROAS across campaigns
- High cost per purchase relative to category benchmarks
- TOF traffic not converting efficiently downstream
- Remarketing under-leveraged despite high ATC and IC volumes
Jan–Jun 2025 Performance Snapshot (Baseline)
| Metric | Value |
| Amount Spent | ₹5,54,730 |
| Purchases | 1,615 |
| Purchase Value | ₹34,80,142 |
| Cost per Purchase | ₹343.49 |
| Purchase ROAS | 6.27 |
This phase highlighted that demand existed, but funnel structure and signal quality were holding scale back.
Strategy Implemented (Jul–Dec 2025)
To unlock scalable ROAS, a full-funnel restructuring was implemented.
TOF (Top of Funnel)
- Introduced Tier-1 Catalog campaigns with exclusions
- Layered interest audiences to control CPMs
Why it worked: Catalog-led discovery pre-qualified users before conversion events.
Lookalike Scaling
- Activated ATC & IC lookalikes
- Gradually scaled budgets on high-ROAS pockets using ABO
Why it worked: Leveraged first-party intent data instead of cold interests.
Remarketing Optimization
- Cleaned audience windows
- Improved creative sequencing for BOF
Why it worked: Captured high-intent users at the lowest CPCs in the funnel.
Full-Funnel Strategy
- TOFU: Interest + Catalog Discovery
- MOFU: Lookalike (ATC / IC)
- BOFU: Website visitors & engaged users
- Retention: Short-window remarketing
Results-First-Party Data Block (Jul–Dec 2025)
| Metric | Value |
| Amount Spent | ₹12,53,067 |
| Adds to Cart | 83,367 |
| Checkouts Initiated | 75,647 |
| Purchases | 9,938 |
| Purchase Conversion Value | ₹1,99,35,191 |
| Cost per Checkout | ₹16.56 |
| Cost per Purchase | ₹126.09 |
| Purchase ROAS | 15.91 |
Before vs After Performance Comparison
| Metric | Jan–Jun 2025 | Jul–Dec 2025 | Change |
| Spend | ₹5.54L | ₹12.53L | +126% |
| Purchases | 1,615 | 9,938 | +515% |
| Revenue | ₹34.8L | ₹1.99 Cr | +472% |
| Cost / Purchase | ₹343 | ₹126 | ↓ 63% |
| ROAS | 6.27 | 15.91 | +154% |
Why This Strategy Worked
- Improved TOF signal quality lifted the entire funnel
- Lookalike scaling reduced dependence on cold traffic
- ABO budget control preserved efficiency while scaling
- Remarketing captured latent demand at peak intent
Key Takeaways for DTC Fragrance Brands
- ROAS scales only after signal quality improves
- Catalog-led TOF is critical in competitive DTC categories
- Lookalikes outperform interests when fed clean data
- Remarketing should be treated as a profit engine, not support
Expert Insight
“Most DTC fragrance brands fail by scaling spend before fixing funnel structure. This case study shows how systematic full-funnel optimization leads to sustainable ROAS growth.”
If you’re a perfume brand aiming for rapid, profitable growth using Meta Ads, our team builds ROAS-focused full-funnel systems designed to drive consistent sales and scalable revenue. Get a custom ROAS growth plan for your perfume store.
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FAQs
Q. What budget is required to run Meta Ads effectively?
Meta Ads work best when budgets generate consistent daily conversions, allowing the algorithm to exit the learning phase and optimize delivery.
Q. Why do Meta Ads performance fluctuate over time?
Performance varies due to auction competition, creative fatigue, audience saturation, and shifts in user behavior.
Q. Is broad targeting better than interest targeting on Meta Ads?
Broad targeting typically outperforms interests once enough conversion data exists, as Meta optimizes delivery using real purchase signals.
Q. How important is remarketing for Meta Ads ROAS?
Remarketing drives higher ROAS by converting high-intent users at lower acquisition costs
Q. How long does it take to achieve profitable ROAS on Meta Ads?
Most brands see stable ROAS within 2-4 weeks after sufficient data collection and structured optimization.
Q. Why should brands choose ROI MINDS for Meta Ads growth?
ROI MINDS builds profitability-first, system-driven Meta Ads strategies that focus on signal quality, full-funnel structure, and scalable ROAS-not short-term spikes.
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