Strategy Analysis

One level above day-to-day bid and budget tactics: prompts for the management-layer questions about brand strategy, budget allocation, and CAC/LTV. Cuts performance through funnel stages, cross-product-line views, and new-customer acquisition lenses — not just ad efficiency.

Prompt quick reference

What you want to do	Prompt
Break performance into funnel stages (impressions → clicks → orders)	Prompt 1
Compare ROAS between Brand and Generic keywords	Prompt 2
Break performance down by order × line item	Prompt 3
Explain unprofitability and propose CAC/LTV-based fixes	Prompt 4
Get a budget-add allocation plan optimized for NTB (New-to-Brand)	Prompt 5
Compare O&O vs 3P inventory performance	Prompt 6
Run a comprehensive brand × campaign × ad-group analysis	Prompt 7
Simulate optimal budget allocation for an added amount (within existing campaigns)	Prompt 8
Diagnose why budget is not being spent as planned	Prompt 9
Get a pre/post-event budget allocation plan by funnel stage	Prompt 10
Generate a CLTV analysis query (AMC)	Prompt 11
Compare ACoS by ASIN × campaign type (AMC)	Prompt 12
Calculate net ad profit contribution by ASIN	Prompt 13
Get a daily ASIN summary	Prompt 14
Identify ASINs with high CTR but low CVR	Prompt 15

For deeper specs, see Safety / Automation phases / Troubleshooting.

Prompt 1: Funnel-stage performance decomposition

Pull the search funnel KPIs (impressions → clicks → orders) across SP campaigns, then break them down by funnel stage and campaign.

Take SP campaigns from the past {{PERIOD}} and break them down by search-funnel KPI
(impressions → clicks → orders) at the funnel-stage × campaign level.
Highlight where the bottleneck sits in the funnel.

Placeholders: {{PERIOD}} (e.g. last 30 days, 2026-04). What it returns: A table of funnel stage × campaign × impressions / clicks / orders / CVR / ACoS plus the bottleneck stage call-out. Read-only — no ad settings are changed.

Prompt 2: Brand vs Generic keyword ROAS comparison

Use campaign names to distinguish Brand (branded) from Generic (non-branded) keywords and identify the top-performing ASIN groups.

Inspect all SP campaigns, distinguish those that include Brand keywords versus
Generic keywords by name, and identify the ASIN groups with the strongest
performance (ROAS / ACoS / sales).

Placeholders: none. What it returns: An aggregate table for Brand vs Generic groups plus an ASIN-level performance ranking. Naming-rule accuracy depends on how well campaign naming is set up — pair this with label management Prompt 6 for cleaner detection.

Prompt 3: Order × line item performance decomposition

Decompose performance by Picaro’s internal order × line-item units (“Branded Keyword”, “Catch-All”, etc.) to surface optimization candidates and new opportunities.

Analyze the major campaigns from the past {{PERIOD}} at the order × line item level.
Surface optimization suggestions and new opportunities.

Placeholders: {{PERIOD}} (e.g. last 30 days). What it returns: A line-item × spend / sales / ACoS / ROAS / optimization-suggestion table.

Prompt 4: Unprofitability diagnosis and CAC/LTV improvement

Decompose unprofitability through the CAC (customer acquisition cost) / LTV (lifetime value) lens, then return improvement suggestions.

Identify unprofitable campaigns and unprofitable ASINs from the past {{PERIOD}}
and explain the root causes through a CAC / LTV lens.
Frame improvement suggestions around customer value, not just ACoS.

Placeholders: {{PERIOD}} (e.g. last 90 days — long-trend data is needed, so 30+ days is recommended). What it returns: An unprofitable-campaign × CAC / estimated LTV / improvement-suggestion table. LTV is estimated from Picaro’s sales history.

Prompt 5: Additional budget allocation optimized for NTB (new customer acquisition)

For the goal of increasing New-to-Brand customers, return an allocation plan for any additional budget.

If the ad budget is increased by {{ADDITIONAL_BUDGET}}, return an allocation plan
optimized for NTB (new customer) growth.
Include campaign × allocation amount × projected NTB increase.

Placeholders: {{ADDITIONAL_BUDGET}} (e.g. $5,000, the additional budget). What it returns: A target-campaign × allocation amount × projected NTB increase × projected ACoS table.

Prompt 6: O&O vs 3P inventory comparison

Compare performance between Amazon’s owned-and-operated inventory (O&O) and third-party (3P) inventory.

Compare ad performance from the past {{PERIOD}} between O&O inventory
(on-Amazon) and 3P inventory (off-site).
Surface differences in ROAS / CTR / CVR.

Placeholders: {{PERIOD}} (e.g. last 30 days). What it returns: A side-by-side comparison of O&O vs 3P metrics. If there is no 3P delivery, the result returns O&O only.

Prompt 7: Brand × campaign × ad-group performance analysis

Run a three-tier analysis across brand, campaign, and ad group, then propose both refinements to existing ad groups and ideas for new ad groups.

Analyze {{BRAND_NAME}}'s SP campaigns from the past {{PERIOD}} at the
campaign and ad-group level. Suggest both improvements to existing ad groups
and ideas for new ad groups.

Placeholders: {{PERIOD}} (e.g. last 30 days), {{BRAND_NAME}} (target brand, leave blank to span the whole connected account). What it returns: A campaign × ad group × key metrics × refinements / new-ideas table.

Prompt 8: Optimal allocation when adding budget (within existing campaigns)

Simulate the optimal allocation when adding budget to existing SP campaigns.

If the budget for {{BRAND_NAME}} is increased by {{ADDITIONAL_BUDGET}}
within existing SP campaigns, where is the optimal allocation?
Return campaign × recommended allocation × projected ROI.

Placeholders: {{BRAND_NAME}} (optional, for narrowing), {{ADDITIONAL_BUDGET}} (e.g. $10,000). What it returns: A campaign × recommended allocation × projected ROI / ACoS table.

Prompt 9: Budget underspend diagnosis

Explain why a portfolio or campaign isn’t pacing as planned.

Explain why {{BRAND_NAME}}'s {{PORTFOLIO_NAME}} is not spending its budget on pace.
Break out the root causes (bid too low / IS shortage / over-restrictive match types, etc.).

Placeholders: {{BRAND_NAME}} (target brand), {{PORTFOLIO_NAME}} (target portfolio, leave blank for the full account). What it returns: An underspending-campaign × root-cause (bid / IS / match type / competitive pressure) × suggested action table.

Prompt 10: Pre- and post-event budget allocation by funnel stage

For events like Prime Day, return a pre- / day-of / post-event budget allocation plan split by search-funnel stage (research / comparison / immediate-purchase).

For {{BRAND_NAME}}'s {{EVENT_DATE}} discount, return a pre/post-event SP budget
allocation plan split by funnel stage (research / comparison / immediate-purchase).
Vary the allocation across the lead-in, day-of, and follow-up windows.

Placeholders: {{BRAND_NAME}} (target brand), {{EVENT_DATE}} (e.g. 2026-07-15, event date). What it returns: A funnel-stage × lead-in / day-of / follow-up × budget allocation table.

Prompt 11: CLTV analysis query generation (AMC)

Generate a SQL query for Amazon Marketing Cloud (AMC) to analyze CLTV (customer lifetime value).

Generate an AMC query for CLTV (customer lifetime value) analysis.
Include per-segment purchase frequency / average purchase amount / computed LTV.
Cover the past {{PERIOD}} of data.

Placeholders: {{PERIOD}} (e.g. 12 months, within AMC’s retention window). What it returns: A runnable SQL query plus the projected result schema and execution instructions for the AMC console. Note: requires AMC account linkage.

Prompt 12: ACoS comparison by ASIN × campaign type (AMC)

Compare ACoS at the ASIN × campaign-type level (SP / SB / SD) using AMC data.

For the top ASINs of the past {{PERIOD}}, generate an AMC query that compares
ACoS by SP / SB / SD campaign type.
Return an ASIN × campaign type × ACoS summary table.

Placeholders: {{PERIOD}} (e.g. 90 days). What it returns: A runnable SQL query plus an ASIN × campaign-type ACoS summary table.

Prompt 13: Net ad profit contribution by ASIN

Subtract ad spend from SP ad sales to compute net ad profit contribution per order.

For SP ads in the past {{PERIOD}}, by ASIN, compute:
sales − ad spend = net ad profit contribution.
Also include net profit per order.

Placeholders: {{PERIOD}} (e.g. last 30 days). What it returns: An ASIN × sales × ad spend × net profit × per-order net-profit table. COGS and gross margin are not factored in, so treat this as an “ad ROI” view.

Prompt 14: Daily ASIN summary

Return a daily summary of sales / ad spend / orders / ROAS for selected ASINs.

Return a daily summary of {{ASIN_LIST}} for the past {{PERIOD}}.
Aggregate sales / ad spend / orders / ROAS at one row per ASIN.

Placeholders: {{ASIN_LIST}} (e.g. B0XXXXXXXX, B0YYYYYYYY, multiple ASINs), {{PERIOD}} (e.g. last 7 days). What it returns: A daily ASIN × date × sales / ad spend / orders / ROAS table.

Prompt 15: Identifying ASINs with high CTR but low CVR

Identify ASINs whose CTR is high but CVR is low (the ad pulls people in, but the detail page loses them).

Identify ASINs from the past {{PERIOD}} whose CTR is above the account average
but whose CVR is below.
Add hypothesized root causes (price / image / reviews / out of stock).

Placeholders: {{PERIOD}} (e.g. last 30 days). What it returns: An ASIN × CTR × CVR × delta-from-account-average × hypothesized-cause table. Useful for prioritizing detail-page improvements (LP, imagery, pricing).

View KPI dashboards — daily KPIs that feed into the strategic view
N-gram sales decomposition — search-term-level support for the strategic narrative
SQP market search funnel — compare the market funnel against your own share
Adjust budgets — execute on the strategic budget allocation
Create reports — bundle strategic-analysis findings into monthly reports