How a Fast-Fashion Jewellery D2C Brand Achieved a 3.9x ROAS and Cut CAC by 60% With AI-Enhanced Ad Management

Girl wearing fast fashion jewellery

Overview A fast-growing D2C jewellery brand focused on trend-driven, affordable fashion accessories was experiencing plateaued growth despite high social media engagement. Their cost-per-click was rising, their return on ad spend (ROAS) was falling below industry benchmarks, and customer acquisition costs (CAC) were eating into margins. With rising competition from both local and international players, they needed a sharper, tech-enabled performance strategy.

They partnered with a professional ad strategist who implemented a full-funnel advertising system powered by AI-driven tools—without relying on generic, one-size-fits-all automations. Within four months, the brand not only revived performance but unlocked scale with a 3.9x ROAS and a 60% drop in CAC.

The Challenge

Despite having over 150 SKUs, daily organic engagement, and trendy drops every week, the brand struggled with paid performance. Here were the core issues:

• Poor Ad Relevance Scores: Ads were being penalized for low engagement and poor alignment with buyer intent.

• Lack of Cohesive Funnel: Prospecting and retargeting campaigns were disjointed.

• Manual Testing Limitations: The in-house team could test only a few creatives and audiences at a time.

• High CAC: Cost per acquisition hovered around ₹1,600—1,800, with inconsistent conversion rates.

The brand had tried boosting posts and basic Meta ad campaigns but lacked a scientific, data-backed structure for full-funnel conversion.

The Approach

Once the specialist stepped in, the focus shifted from campaign-level execution to funnel-level optimization. The strategy was broken into 4 core pillars:

1. Data Deep-Dive & Audience Reconstruction

Using first-party data and behavioral insights, the strategist mapped out five key buyer personas. AI models were then used to:

• Identify high-intent behaviors (e.g., time spent on product pages, cart drop-offs)

• Build lookalike audiences based on LTV, not just last-click conversions

• Segment users by frequency, recency, and product category preference

2. Creative Intelligence & Auto-Testing

Instead of relying on gut-driven creative production, they used AI-enhanced tools to:

• Analyze top-performing competitor ads in real-time

• Auto-generate ad variations (headlines, CTAs, image-to-text ratio)

• Deploy dynamic creative testing where AI could scale top performers automatically

Over 80 ad combinations were tested in the first 3 weeks, allowing the team to double down on winning creative angles like "Under ₹500 Style Picks" and "Last-Minute Gift Ideas."

3. Predictive Budgeting & Placement Optimization

AI models were applied to:

• Predict which time slots and geos would yield highest engagement

• Allocate budget dynamically across Instagram Reels, Meta Stories, and Google Discovery

• Cap spending in underperforming ad sets before they burned money

The shift toward predictive, self-correcting budgeting helped scale top-performing ad sets faster without wasting on low-converting segments.

4. Retargeting With AI-Powered Personalization

The brand had basic retargeting in place, but the specialist rebuilt this layer with:

• Personalized product carousels based on user behavior

• Automated offer sequencing (e.g., "10% Off if Unpurchased in 48 Hrs")

• Smart frequency capping to reduce ad fatigue

An AI-powered messaging engine also tested 1:1 personalization, dynamically inserting product names into ad copy for returning users.

The Results

In just four months of professional, AI-supported campaign management, the brand saw significant growth:

• ROAS Increased to 3.9x: From an earlier 1.6x average

• CAC Dropped by 60%: From ~₹1,700 to just under ₹700

• Revenue Doubled: Within one quarter

• Top 10 Products Accounted for 70% of Sales: Due to smarter ad-product matching

• Creative Output Quadrupled: Without increasing design workload

Moreover, retargeting CTR improved by 37%, and AOV increased slightly due to bundle-based upselling strategies discovered through AI clustering.

Lessons Learned

1. AI Without Strategy Is Noise: It wasn't just the tools, but the specialist's structured approach that made the difference.

2. Creative Testing Needs Volume + Speed: AI helped test more hypotheses faster, but human oversight ensured creative quality.

3. Full-Funnel Thinking Wins: Instead of focusing on isolated metrics, the strategy linked discovery, engagement, conversion, and retention.

4. First-Party Data Is Gold: Using real customer behavior to fuel AI engines led to more accurate targeting and higher ROAS.

Conclusion This case shows that AI in advertising isn't about replacing marketers—it's about enhancing strategic decisions with scale, speed, and smart automation. For fast-fashion D2C brands in competitive categories like jewellery, combining AI tools with human insight can be the game-changer.

By investing in professional, AI-enabled campaign management, the brand unlocked profitable scale, sustainable CAC, and a repeatable system they could continue to build on.