Introduction:
Ever felt like you’re running ads on the right keywords but not getting the right results? You’re not alone. That’s what one of our clients experienced – until we implemented smart keyword clustering using their Search Term Reports.
In this case study, you’ll see how we used data-driven clustering to restructure campaigns and increased their revenue by 45% in just 60 days, without increasing ad spend.
Background: The Client Profile
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Category: Herbal Supplements
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Monthly Sales: $42,000
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ASINs: 7 main SKUs
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Challenge: High TACoS (17%) and stagnating sales
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Ad Structure: Mix of Auto, Broad, and poorly optimized Manual campaigns
Step 1: Audit the Existing Keyword Setup
We started by pulling their last 30-day Search Term Report and found:
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2,100+ unique search terms
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78% of spend was going to broad and auto
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Top 15 search terms generated 62% of conversions but were scattered across multiple ad groups
Insight: High-performing keywords were buried in non-performing campaigns.
Step 2: Keyword Clustering
We exported all keywords and grouped them into logical clusters based on:
| Cluster Type | Examples |
|---|---|
| Branded | “bixa senna powder”, “bixa amla” |
| Ingredient-based | “organic senna leaf”, “amla powder bulk” |
| Use-case based | “constipation relief”, “hair growth” |
| Audience-based | “for women”, “for elderly” |
Each cluster was then mapped to a dedicated campaign.
Step 3: Rebuilding the Campaign Structure
We created 12 manual campaigns using eCommercean’s Bulk Campaign Generator, each focused on a specific cluster.
Example structure:
| Campaign Name | Targeting Type | Match Type | Bid Strategy |
|---|---|---|---|
| Amla Powder – Branded | Manual | Exact | High bids |
| Amla Powder – Generic | Manual | Broad | Medium bids |
| Hair Growth – Informative | Manual | Phrase | Lower CPC, test ROAS |
| Competitor Targeting | Product ASINs | N/A | Conservative spend |
We also added negatives to avoid keyword overlap across campaigns.
Step 4: Results After 60 Days
| Metric | Before | After |
|---|---|---|
| Monthly Revenue | $42,000 | $61,100 |
| ACoS | 28.5% | 21.3% |
| TACoS | 17.2% | 12.4% |
| New-to-Brand Customers | ~1,000/month | 1,450+/month |
| Campaigns with ROAS >3 | 4/16 | 10/12 |
Net Revenue Growth: +45%
Ad Spend Growth: +7% only
Why This Worked
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Keyword Relevance improved because each campaign targeted a tight cluster
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Conversion Rates increased due to better ad-listing match
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Wasted Spend dropped by eliminating overlap and non-performing terms
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Bid Control allowed us to push aggressively on proven keywords
Tools Used
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eCommerce Search Term Report Analyzer
For filtering high-converting keywords and negatives -
Bulk Campaign Sheet Generator
Created 12 campaigns in under 30 minutes with exact, broad, and phrase match logic -
Amazon Ads Placement Report
Helped refine top-of-search bid modifiers
Pro Tip: Set Up a Clustering SOP
| Weekly Task | Time Required |
|---|---|
| Export STR | 5 mins |
| Re-cluster high-performing terms | 10 mins |
| Adjust bids per cluster | 15 mins |
| Add negatives | 5 mins |
Total: 35 minutes/week for compounding campaign growth.
Conclusion:
This case study proves that keyword clustering isn’t just a theory – it’s a growth multiplier. With the right structure, even a stagnant account can scale profitably. If your ads feel like they’ve hit a ceiling, it’s probably time to organise your chaos.
Want us to implement this for your brand? Or want to DIY with the same tools? Explore eCommercean’s Keyword Intelligence + Bulk Campaign Suite now.