See the market search funnel with SQP

Use SQP (Search Query Performance) data from Amazon Brand Analytics to decompose the market-wide search funnel (Impression → Click → Cart Add → Purchase) by product × search query. Unlike Break down sales contribution with N-gram, which uses ad data, SQP shows the search behavior of all Amazon shoppers, revealing your position within the market that ACoS alone cannot show.

When to use this category

• “Surface search queries with high Opportunity score.”
• “Identify the funnel Bottleneck per product.”
• “See monthly SQP trends.”
• “Confirm that contribution data is available before generating a report.”

Why SQP matters

ACoS represents efficiency inside your own campaigns, while SQP shows the share you captured within the overall market. Looking at both together helps you catch structural changes early, such as “ACoS is healthy but market share is shrinking.” See Why you should not look at ACoS alone for more.

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Prompt 1: SQP standard flow (product check → period selection → funnel analysis)

🎯 Purpose

Confirm the SQP-eligible product and available periods, then compute Opportunity score and Bottleneck per product × search query for each funnel stage.

📋 When to use

• You want to run an SQP analysis for the first time.
• You want to confirm which products have data first.
• You want a list of Opportunity score and Bottleneck classifications.

📝 Prompt

I want to run an SQP analysis. Follow these steps:
1. Confirm candidate products that have SQP data
2. Pick a target product and fetch its available weekly periods
3. Run a funnel analysis with top_n {{TOP_N}} for the latest finalized week
   Return Opportunity score and Bottleneck classification

Placeholders:

Placeholder	Description	Default source
{{TOP_N}}	How many top rows to return (e.g., 30)	Defaults to 30

⚙️ What the AI does

1. Confirm target product — Present a list of products that can fetch SQP data.
   • Why check first: SQP availability in Brand Analytics differs per product, so eligibility is determined before fetching periods.
2. Fetch available periods — Return the weekly periods available for the chosen product.
   • Why per product: Even within one account, data start dates differ by product, so picking the latest finalized week directly prevents grabbing bad data.
3. Decompose the funnel — Aggregate the four stages Impression → Click → Cart Add → Purchase for the finalized week.
   • Why finalized weeks: Amazon-side reports have a lag of several days, and Unfinalized weeks shift in value later.
4. Compute Opportunity score — Surface queries with high search volume but low own-share with a high score.
   • Why Opportunity score: A simple “top by own purchases” only lifts queries you already win, hiding queries with growth potential.
5. Bottleneck classification — Identify where users drop off across four buckets: low Impression / low CTR / low Cart rate / low purchase rate.
   • Why by stage: The same “low own purchase share” needs different actions depending on whether impressions, clicks, or conversions are the issue.

📊 Example Output

SQP funnel analysis Product: B0XXXXXXXX / Period: 2026-05-04 to 05-10 (finalized week)

Search term	Opportunity score	Own purchase share	Bottleneck
amino acid shampoo	87	4.2%	Low Impression
refill shampoo	72	8.1%	Low CTR
scalp care shampoo	65	12.4%	Low Cart rate
…(27 more rows)…

Of the top 30, 12 are Low Impression / 9 Low CTR / 6 Low Cart rate / 3 Low purchase rate. Consider Adjust bids to lift exposure for Low Impression queries, and review product images and titles for Low CTR queries.

🔒 Safeguards

• Unfinalized weeks are auto-excluded — Periods where Amazon-side values are still changing are removed from the analysis.
• SQP has a report finalization lag of several days on Amazon’s side, so the current latest week cannot be used (the last week in the available list is the finalized week).
• Opportunity score is a composite of search volume × share gap and should be used as a rough indicator of growth potential (final judgment requires human review).
• The analysis is read-only and writes nothing to the ad account.
• Each analysis has a 1-hour expiry; regeneration is required after that.

🚦 Execution mode

Phase	Status	Conditions
Phase 1 (now)	One-shot	Available with just a Picaro connection
Phase 2 (Q3 2026)	One-shot	Data is updated weekly, so daily runs are out of scope
Phase 3 (Q4 2026)	Monthly auto (priority ASINs)	Registered priority ASINs are bundled into the monthly report
Phase 4 (2027)	Monthly auto	All priority ASINs delivered automatically

👉 What to do next

• Break down sales contribution with N-gram — Cross-check whether top Opportunity score queries from SQP also contribute inside your own ads.
• Adjust bids — Low Impression queries are candidates for bid raises.
• Why you should not look at ACoS alone — The thinking behind combining market share and ACoS.

❓ FAQ

Q: Can I see search terms I’m not advertising on with SQP? A: Yes. SQP covers all Amazon shopper search behavior, so you can see market-wide Impression / Click / Cart Add / Purchase even for search terms you aren’t running ads on.

Q: Should I add every high Opportunity score search term to ads? A: Not necessarily. Opportunity score is just an indicator of growth potential — judge against your ACoS and margin assumptions. For newly launched products, it works well to prioritize high Opportunity score search terms as candidates for Adjust bids.

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Prompt 2: Monthly SQP trend analysis

🎯 Purpose

Capture how a product’s search funnel moves across seasonal factors and medium-to-long-term trends on a monthly basis, beyond the weekly view.

📋 When to use

• You want to see monthly SQP trends.
• You want to understand trends over the last few months.
• You want to check search behavior for seasonal products.

📝 Prompt

Run a monthly SQP analysis for product {{PRODUCT_ID}}.
After confirming available months, use the latest finalized month with top_n {{TOP_N}}
and return Opportunity score and Bottleneck classification.

Placeholders:

Placeholder	Description	Default source
{{PRODUCT_ID}}	Target product ID (ASIN or internal ID)	User input
{{TOP_N}}	How many top rows to return (e.g., 30)	Defaults to 30

⚙️ What the AI does

1. Switch aggregation to monthly — Fetch SQP monthly rather than weekly.
   • Why monthly: Weekly is good for detecting short-term swings; monthly is better for medium-to-long-term effects from seasonal products or campaigns. Pick based on the use case.
2. Confirm available months — Return periods where monthly data is available for that product.
   • Why confirm: Monthly data starts later than weekly for some products, so the analysis selects from the available-month list rather than running blindly.
3. Decompose the funnel for the latest finalized month — Aggregate Impression → Click → Cart Add → Purchase across the four stages.
   • Why finalized month: Mid-month data has incomplete finalized weeks, making funnel-stage shares unstable.
4. Opportunity score and Bottleneck classification — Apply the same logic as Prompt 1 to monthly data.

📊 Example Output

SQP funnel analysis (monthly) Product: B0XXXXXXXX / Period: 2026-04 (finalized month)

Search term	Opportunity score	Own purchase share	Bottleneck	MoM
amino acid shampoo	92	3.8%	Low Impression	-1.2pt
spring shampoo	84	6.5%	Low CTR	+2.4pt (seasonal)
…(28 more rows)…

The top 5 shares that fell MoM are estimated to reflect the seasonal rotation of search terms including “spring -” and “new-life -”. To separate seasonal factors, also check the last 3-6 months of movement.

🔒 Safeguards

• Finalized months only — Mid-month Unfinalized months are excluded from the list.
• Monthly data start dates vary by product, so selection happens directly from the available-month list.
• The AI does not assert the split between seasonal factors and structural share changes — it provides the last 3-6 months alongside and leaves the call to a human.
• The analysis is read-only and writes nothing to the ad account.
• Each analysis has a 1-hour expiry.

🚦 Execution mode

Phase	Status	Conditions
Phase 1 (now)	One-shot	Available with just a Picaro connection
Phase 2 (Q3 2026)	One-shot	Data is updated monthly, so daily runs are out of scope
Phase 3 (Q4 2026)	Monthly auto (priority ASINs)	Registered priority ASINs are bundled into the monthly report
Phase 4 (2027)	Monthly auto	All priority ASINs delivered automatically

👉 What to do next

• SQP standard flow (weekly) — Once you have the monthly trend, confirm finalized values for the latest week.
• Generate a report — Monthly SQP trends can be embedded into the end-of-month report.
• Design label taxonomy — Labeling seasonal search terms improves interpretation accuracy for trends.

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Prompt 3: Contribution data precheck for reports

🎯 Purpose

Before generating the end-of-month report, run a lightweight check that confirms the SQP and ad data needed for the contribution slide is available.

📋 When to use

• You want to confirm the data is available before building the report’s contribution slide.
• You want to run only the contribution-analysis precheck up front.
• You want to confirm the data is ready including the comparison period.

📝 Prompt

Run a contribution data precheck for period {{PERIOD}} with comparison period {{COMPARE_PERIOD}}.
Return the data-source discovery result, row counts for both periods,
the share of anonymized search terms, and fallback availability.

Placeholders:

Placeholder	Description	Default source
{{PERIOD}}	Target period (e.g., 2026-04-01 to 2026-04-30)	User input
{{COMPARE_PERIOD}}	Comparison period (e.g., 2026-03-01 to 2026-03-31)	User input

⚙️ What the AI does

1. Discover data sources — Identify which of SQP / ad reports / sales are available for the target period.
   • Why precheck: Report generation takes time, so we avoid failing late if data is missing.
2. Row counts for target and comparison periods — Decide whether both periods have enough data based on counts.
   • Why check the comparison too: Contribution is reported as “delta vs. prior period”, so the comparison cannot stand if only one side is complete.
3. Share of anonymized search terms — Check what share is anonymized by Amazon.
   • Why care about anonymization: Periods with high anonymization yield coarser query-level contribution breakdowns, affecting report granularity.
4. Fallback availability — Decide whether a degraded source (ad data only, etc.) can still produce a report when data is short.
5. Return status — Present the result in three levels: ready / needs_confirmation / blocked.

📊 Example Output

Contribution data precheck Period: 2026-04 / Comparison: 2026-03

Item	Result
SQP data	Available (target 4 weeks / comparison 4 weeks)
Ad reports	Available
Anonymized search terms	12.4% (within tolerance)
Fallback	Not required
status	ready

You can proceed directly to report generation. Use Generate a report to build the end-of-month report.

🔒 Safeguards

• Precheck only — No report generation and no writes to the ad account occur.
• When the anonymized share is high, status becomes needs_confirmation and a human decides whether to proceed.
• Periods with missing data return blocked together with remediation steps.
• Because SQP is subject to report finalization lag, prechecks immediately after month end may fail (re-run after a few days).

🚦 Execution mode

Phase	Status	Conditions
Phase 1 (now)	One-shot	Available with just a Picaro connection
Phase 2 (Q3 2026)	Manual at month end	Run as a pre-report check at the end of the month
Phase 3 (Q4 2026)	Auto at month end	Runs automatically before report generation, notifying only on blocked
Phase 4 (2027)	Auto at month end	Linked with ad-hoc anomaly-triggered runs

👉 What to do next

• Generate a report — Move to report generation once ready is returned.
• SQP standard flow — When blocked, revisit the target product and period.
• Support and feedback — Where to ask when the anonymization rate is unusually high.

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Prompt quick reference

What you want to do	Use
Standard SQP analysis (weekly)	Prompt 1
Monthly SQP trend	Prompt 2
Pre-report contribution data check	Prompt 3

Related categories

• Break down sales contribution with N-gram — Cross-check your own ad N-gram analysis against market-wide SQP.
• Generate a report — SQP-derived contribution is embedded into the report.
• Adjust bids — High Opportunity score search terms from SQP are candidates for bid raises.
• Design label taxonomy — Offensive / defensive / competitor labels improve SQP interpretation accuracy.
• Why you should not look at ACoS alone — The thinking behind multi-dimensional judgment combining SQP and ACoS.