Client Snapshot
Massive catalog across phone covers with hundreds of phone models (Android + iPhone variants).
Starting ACOS: ~37%. Current ACOS: ~25%. Profitability improved without sacrificing revenue.
Maintained Top 3 seller position in a crowded category dominated by heavily funded brands.
Bootstrapped brand operating at scale where small inefficiencies compound fast.
The Problem
This was not an execution problem. It was a complexity problem.
1. Massive Catalog & Demand Fragmentation
2000+ ASINs across phone covers with hundreds of phone models (Android + iPhone variants). Demand is model-driven, not brand-driven, and phone model popularity shifts continuously.
This creates a long tail where:
- •A few models drive volume
- •Most models contribute sporadically
- •Treating all ASINs equally destroys efficiency
2. Capital Asymmetry in the Category
VC-funded competitors overspending aggressively, with CPCs inflated beyond sustainable levels. Bootstrapped brands were forced into inefficient bidding wars. The brand needed to win selectively, not everywhere.
3. Ads at a Scale Humans Can't Monitor Manually
500+ campaigns running simultaneously with mixed intent within ad groups. One model often had one campaign, one ad group, and 20+ search terms (mostly broad match). Many terms weren't spending, some overspending, most unoptimized. At this scale, manual optimization breaks down.
The Solution: Structured Decision-Making Backed by Data + AI
The breakthrough wasn't just better campaigns — it was better information flow.
All data was pulled automatically into a centralized data warehouse: Amazon Ads, retail performance, search term data, and ASIN-level metrics. AI analysis agents continuously ran queries, surfacing patterns, anomalies, and inefficiencies as insights. Humans focused on decisions, not data extraction. This eliminated guesswork and reaction-based optimization.
Auto campaigns were used deliberately for broad discovery at controlled bids and low-ACOS term discovery. Continuous AI-driven monitoring promoted profitable terms and aggressively negated wasteful terms. Autos became a signal engine, not a spend sink.
Instead of spreading budget across hundreds of models, we identified top 100 phone models by category demand, sales velocity, and conversion efficiency. Structured campaigns to focus on ranking for these models and allocate disproportionate budget where it mattered. This introduced intentional focus in a highly fragmented catalog.
Before: One campaign per model, all search terms in one ad group, broad match dominating, no spend prioritization. After: Clean separation by model, match type, and intent. Exact-match ranking campaigns for top terms with spend concentrated on proven paths to conversion. This alone removed significant waste.
AI-driven analysis revealed that cheaper phone models sold significantly more covers. Premium phones had higher CPCs, lower volume, and worse ACOS. Brand pricing was better aligned with the mass market. Action: Increased budget on cheaper, high-velocity models and let expensive models sell opportunistically. Result: ACOS dropped immediately, revenue remained stable, margins improved.
Instead of scaling based on perceived importance, we increased spend where CVR was high and spend was artificially capped. Reduced spend where CVR was weak and CPCs were inflated. This reallocation alone unlocked profitable scale.
AI surfaced a repeatable pattern: Exact match keywords with search impression share < 5% and CVR > 9%. These were easier to rank, less competitive, and highly profitable. Ranking these terms delivered quiet growth without CPC wars.
Instead of blanket bid increases, we built AMC audiences of users more likely to convert. Increased bids only for high-intent cohorts and avoided unnecessary top-of-funnel waste. Efficiency improved without expanding spend.
The Outcome
- ACOS reduced from ~37% to ~25% — significant profitability improvement
- Maintained Top 3 category position — competitive standing preserved
- Revenue preserved while profitability increased — efficient growth
- Ads became easier to monitor, scale, and reason about — systematic approach
Most importantly, the brand shifted from reactive optimization to systematic, repeatable decision-making.
Why This Worked
This wasn't about one trick, one campaign type, or one dashboard. It worked because:
- All data flowed automatically into one system
- AI handled detection, humans handled judgment
- Spend followed evidence, not pressure
In a market where money is abundant, clarity becomes the edge.