Most sellers blur the lines between Hero SKU and secondary variants. Default lazy approach is dumping all child variants in every ad group OR advertisnig every child SKU on the same keywrods.

\- Prioritize the Hero SKU for Head Search Terms: Your highest-converting, most universally appealing variation should take the lion’s share of major volume keywords. If you sell bedding, your Hero SKU gets targeted on "Silk bedsheets."

\- Map Variants to Specific Intent: Stop bidding your Hero SKU on long-tail, hyper-specific searches if a variant matches it perfectly. Size and color variants should only be targeted on exact intent. "Queen silk bedsheet" goes directly to the Queen variant.

\- The Margin Exception: If you are currently targeting multiple variants on the same keywords and the unit economics work, leave it alone. Data beats theory.

However, if your Total Advertising Cost of Sales (TACoS) is creeping up at the parent level, the leak is almost always at the variation level.

When was the last time you isolated your TACoS and search term overlap down to the specific child ASIN?

Most sellers blur the lines between Hero SKU and secondary variants. Default lazy approach is dumping all child variants in every ad group OR advertisnig every child SKU on the same keywrods.

- Prioritize the Hero SKU for Head Search Terms: Your highest-converting, most universally appealing variation should take the lion’s share of major volume keywords. If you sell bedding, your Hero SKU gets targeted on "Silk bedsheets."

- Map Variants to Specific Intent: Stop bidding your Hero SKU on long-tail, hyper-specific searches if a variant matches it perfectly. Size and color variants should only be targeted on exact intent. "Queen silk bedsheet" goes directly to the Queen variant.

- The Margin Exception: If you are currently targeting multiple variants on the same keywords and the unit economics work, leave it alone. Data beats theory.

However, if your Total Advertising Cost of Sales (TACoS) is creeping up at the parent level, the leak is almost always at the variation level.

When was the last time you isolated your TACoS and search term overlap down to the specific child ASIN?

Most sellers blur the lines between Hero SKU and secondary variants. Default lazy approach is dumping all child variants in every ad group OR advertisnig every child SKU on the same keywrods.

- Prioritize the Hero SKU for Head Search Terms: Your highest-converting, most universally appealing variation should take the lion’s share of major volume keywords. If you sell bedding, your Hero SKU gets targeted on "Silk bedsheets."

- Map Variants to Specific Intent: Stop bidding your Hero SKU on long-tail, hyper-specific searches if a variant matches it perfectly. Size and color variants should only be targeted on exact intent. "Queen silk bedsheet" goes directly to the Queen variant.

- The Margin Exception: If you are currently targeting multiple variants on the same keywords and the unit economics work, leave it alone. Data beats theory.

However, if your Total Advertising Cost of Sales (TACoS) is creeping up at the parent level, the leak is almost always at the variation level.

When was the last time you isolated your TACoS and search term overlap down to the specific child ASIN?

Most sellers blur the lines between Hero SKU and secondary variants. Default lazy approach is dumping all child variants in every ad group OR advertisnig every child SKU on the same keywrods.

- Prioritize the Hero SKU for Head Search Terms: Your highest-converting, most universally appealing variation should take the lion’s share of major volume keywords. If you sell bedding, your Hero SKU gets targeted on "Silk bedsheets."

- Map Variants to Specific Intent: Stop bidding your Hero SKU on long-tail, hyper-specific searches if a variant matches it perfectly. Size and color variants should only be targeted on exact intent. "Queen silk bedsheet" goes directly to the Queen variant.

- The Margin Exception: If you are currently targeting multiple variants on the same keywords and the unit economics work, leave it alone. Data beats theory.

However, if your Total Advertising Cost of Sales (TACoS) is creeping up at the parent level, the leak is almost always at the variation level.

When was the last time you isolated your TACoS and search term overlap down to the specific child ASIN?

Just audited a few campaigns that were pulling orders primarily from Top of Search (TOS) on normal base bids. The placement modifiers were standard: TOS was set to 140%, and Product Pages (PP) were set to 0%.

Despite the 0% modifier, the CPC on Product Pages was hitting 3x the base bid.
Here is the diagnosis of why that happened:

The Culprit: The campaign was using Amazon's automated bidding strategy, set to target a ROAS above 4.

The Data: TOS was performing exceptionally well with a ROAS over 4.5.

However, Product Pages were bleeding 57% of spend share with a disastrous ACOS of 127%.

The Algorithmic Flaw: Because the TOS performance was carrying the campaign's average ROAS above the target threshold, Amazon’s automated rule blindly pushed bids up across the board to chase volume. It ignored placement-level inefficiencies and forced aggressive bids onto Product Pages anyway.

I immediately stripped out the automated rule. I shifted the campaign back to 'Dynamic bids - down only' and am relying on manual placement modifiers to force the focus back onto TOS.

I will be tracking the efficiency shift and sharing the exact results in upcoming weeks. Stay connected.

Just audited a few campaigns that were pulling orders primarily from Top of Search (TOS) on normal base bids. The placement modifiers were standard: TOS was set to 140%, and Product Pages (PP) were set to 0%.

Despite the 0% modifier, the CPC on Product Pages was hitting 3x the base bid.
Here is the diagnosis of why that happened:

The Culprit: The campaign was using Amazon's automated bidding strategy, set to target a ROAS above 4.

The Data: TOS was performing exceptionally well with a ROAS over 4.5.

However, Product Pages were bleeding 57% of spend share with a disastrous ACOS of 127%.

The Algorithmic Flaw: Because the TOS performance was carrying the campaign's average ROAS above the target threshold, Amazon’s automated rule blindly pushed bids up across the board to chase volume. It ignored placement-level inefficiencies and forced aggressive bids onto Product Pages anyway.

I immediately stripped out the automated rule. I shifted the campaign back to 'Dynamic bids - down only' and am relying on manual placement modifiers to force the focus back onto TOS.

I will be tracking the efficiency shift and sharing the exact results in upcoming weeks. Stay connected.

Sohail Qurban - Fix Your Ecom - Claude AI

Amazon's Search Query Performance report (SQPr) measures brand-level search visibility, click share, and conversion share across every query or search term that triggered a session. However, raw SQPr data is inert without segmentation. The Fix Your Ecom’s SQPr method powered with Claude, structures data into three pools (branded, generic, competitor), feeds the segmented CSV into a trained Claude skill, and outputs pool-specific, prioritized recommendations in under 5 minutes, replacing 5 hrs of manual work.

1 - What Is Amazon Search Query Performance Report (SQPr) and Why Does It Matter for Brand’s Marketing Strategy?

Amazon's Search Query Performance report (SQPr) is a Brand Analytics tool that shows KPI’s like impression share, click share, and purchase share at keyword level. Giving brand owners or managers a direct view of where they win and lose visibility weekly, monthly and quarterly.

2 - What Is the Old SQPr Analysis Workflow and What Does It Cost?

The traditional SQPr workflow involves exporting a raw CSV from Brand Console, loading it into Google Sheets or Excel, building pivot tables to segment keywords by type, and manually writing insight narratives, a process that averages 60 - 300 minutes per brand per reporting cycle with no standardized output.

Step 1 — Export: Download from Brand Analytics.

Step 2 — Clean: Remove duplicate rows, fix formatting, normalize column headers.

Step 3 — Pivot: Build separate pivot tables for branded vs. generic vs. competitor queries. Requires manual query classification — the single largest time cost.

Step 4 — Interpret: Manually identify patterns — declining click share, conversion gaps, impression volume shifts. No standardized framework means different analysts produce different outputs.

Output Quality: Insights are analyst-dependent, inconsistently formatted, and rarely include a prioritized action list tied to specific metrics.

3 - How Does the Fix Your Ecom's SQPr-Claude Method Work?

SQPr-Claude workflow is a three-stage setup and now you can just run batches with in 5 minutes :

Stage 1 — Structured Export: The SQPr template is pre-formatted to match Amazon's exact CSV column structure. Paste raw export data into Tab 1 without reformatting. No header changes required.

Stage 2 — Pool Segmentation: A classification column labels each query row as branded, generic, or competitor. Focus on the top 50–100 queries by impression volume. These represent 80%+ of visible search signals for most brands.

Stage 3 — Claude Skill Execution: The Fix Your Ecom’s Claude skill and prompt are trained to read each data pool independently and output:

Output Format: Insights are delivered in a structured data file with SQPr analysis, summary and POA.

Amazon gives SQP report that shows exactly where your brand ASINs wins and loses CVR against the market.

Here's how to read it in 4 easy steps:

1. Pull it from Brand Analytics > Search Query Performance report

2. Filter for your top 20 keywords by search volume

3. For each keyword, look at following columns:

- Market clicks & Market purchases

- Brand Clicks & Brand Purchases

Now divide mkt purchases by mkt clicks to get mkt conversion rate and do the same for brand.

4 - Find difference between mkt vs brand cvr and you will have an idea on how is your CVR holding up.

For example if market CVR is 13% and brand share is 7%? What does it tells you? Cvr needs fixing, it is not a traffic problem. Traffic is coming in, listing isn't closing. Fix product page , price, or reviews.