Plan your ad strategy

On this page you can build the decision inputs one level above tactical ACoS and bid adjustments — the brand strategy, budget allocation, and CAC / LTV information the management layer wants to see. Beyond ad efficiency, it analyzes from the full-funnel, cross-product-line, and new-customer-acquisition angles, for monthly to quarterly strategic reviews.

About this page

Item	Details
What you can do	From Sponsored Ads performance, lay out strategy-level decision inputs: funnel decomposition, brand structure, budget allocation simulation, and per-ASIN profitability
Applies to	Sponsored Ads (SP / SB / SD). Each prompt focuses mainly on Sponsored Products (SP). Amazon DSP and AMC are separate pages (see the note below)
Data & connection needed	The Sponsored Ads performance data Picaro already holds (no extra setup needed for a connected account)
Scope	Read-only (analysis only). Budget allocation plans are proposals only; ad settings are not changed. Actual budget, bid, and keyword changes happen through the approval flows on the related pages

Terms used on this page

• CTR (click-through rate) … of the times an ad was shown, the share that were clicked.
• CVR (conversion rate) … of the clicks, the share that led to a purchase.
• ACoS (Advertising Cost of Sales) … ad spend ÷ ad-attributed sales. Lower is more efficient.
• ROAS (Return on Ad Spend) … ad-attributed sales ÷ ad spend. Higher is more efficient.
• CAC (customer acquisition cost) … the cost of acquiring one new customer.
• LTV (lifetime value) … the sales and profit one customer brings over their lifetime.
• NTB (New-to-Brand) … a new customer who had not bought your brand within a set prior period.
• ROI (return on investment) … the profit gained relative to the cost spent.

Amazon DSP and AMC are separate pages

This category covers Sponsored Ads (SP / SB / SD). For Amazon DSP order / line-item delivery performance analysis, use Analyze Amazon DSP delivery performance; for customer-lens analysis and query generation using AMC (Amazon Marketing Cloud) data, use Analyze cross-ad customer behavior with AMC.

Use at a different cadence than weekly ops

This page is for monthly to quarterly strategic reviews. For daily and weekly operational calls, use Check KPIs on the dashboard or Adjust bids.

When to use this

• Before a new build, you want to inventory the existing campaign structure to avoid naming collisions and duplicates
• You want to pin down which funnel stage (impressions → clicks → orders) is the bottleneck
• You want to compare which sells more efficiently — branded keywords or generic keywords
• You want to explain unprofitability that ACoS alone can’t reveal, through the CAC / LTV and new-customer lens
• You want to simulate which campaign is best to put additional budget into before allocating it
• You want to judge, at the ASIN level, net ad profit contribution and which products get clicks but no sales

Before you run

• Target account … if you handle multiple accounts, confirm the right one first.
• Target period … e.g. “last 30 days”. For strategic analysis that reads long-term trends, 30+ days is recommended. Today and yesterday may not be finalized, so a recently closed period is safer.
• Target brand / ASIN / portfolio (optional) … specify to narrow to a particular scope. Leave blank to cover the whole connected account.
• If you register a target ACoS / ROAS in Set targets for sales, ad spend, and ACoS, the “efficient / inefficient” judgment criteria are used automatically.

Quick reference

Goal	What you get	Changes	Prompt
Inventory the existing SP campaign structure before a new build	A list of campaign names, statuses, and daily budgets, plus naming-collision cautions	No change	Prompt 1
Find bottlenecks by funnel stage	A performance table by funnel × campaign	No change	Prompt 2
Compare efficiency between branded and generic keywords	An aggregate of the Brand vs Generic groups and an ASIN ranking	No change	Prompt 3
Explain unprofitability and propose fixes from a customer lens	A table of unprofitable target × CAC / estimated LTV × improvement suggestion	No change	Prompt 4
Get an added-budget allocation plan to grow new customers	A table of allocation target × amount × projected NTB increase	No change	Prompt 5
Run a comprehensive brand · campaign · ad-group three-tier analysis	A table of the three tiers × key metrics × optimization / new-idea proposals	No change	Prompt 6
Find the optimal allocation of added budget within existing campaigns	A table of campaign × recommended allocation × projected ROI	No change	Prompt 7
Analyze why budget isn’t being spent	A table of underspending target × likely cause × suggested action	No change	Prompt 8
Build a pre/post-event budget allocation plan by search funnel stage	A table of funnel × lead-in / day-of / follow-up allocation	No change	Prompt 9
Calculate net ad profit contribution by ASIN	A table of ASIN × sales × ad spend × net profit	No change	Prompt 10
Get a daily summary for key ASINs	A table of ASIN × date × sales / ad spend / orders / ROAS	No change	Prompt 11
Identify ASINs with high CTR but low CVR	A table of ASIN × CTR × CVR × ratio to average × hypothesized cause	No change	Prompt 12

This page goes only as far as producing decision inputs

As the “Changes” column shows (all “No change”), this page stops at analysis and proposals. The budget allocation plans (Prompts 5, 7, 9) are proposals only; when you actually change budgets or bids, follow each prompt’s “Next steps” into the approval flow on the related page. Order × line-item decomposition is in Analyze Amazon DSP delivery performance, and AMC query generation (CLTV / ACoS by ASIN × campaign type) is in Analyze cross-ad customer behavior with AMC. For detailed specs, see Automation phases / Troubleshooting.

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Analyze current structure and performance

Prompt 1: Check existing campaign structure

As a preliminary step before a new build, fetch the list of SP campaigns tied to the referenced ad profile to avoid naming collisions and duplicate builds.

When to use — before creating new campaigns, when you want to inventory the existing structure to avoid naming collisions and duplicate builds.

What you need — the ad profile ID (a connected profile ID; if only one is connected, it is referenced automatically).

List the Sponsored Products campaigns tied to ad profile {{ad profile ID}}.
For a pre-build naming-collision check and duplicate-build check, also show
status and daily budget.

What you can change — the target ad profile ID.

What you get back — a table of campaign name, status, and daily budget, plus a note when similar names exist.

Changes — no change (read-only). Nothing is written to Amazon Ads.

Next steps — once you’ve checked the structure, use Create Sponsored Products (SP) campaigns to build new ones, and Classify ads with labels and naming rules to tidy up naming rules.

About the creation date

The campaign list may not return the creation date. When you need it, check it from the Amazon Ads management console.

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Prompt 2: Break down performance by funnel stage

Reference the search-funnel KPIs (impressions → clicks → orders) and build a performance report that breaks the past period’s campaigns down by funnel stage.

When to use — when you want to pin down which stage — impressions, clicks, or orders — is leaking, i.e. the bottleneck.

What you need — the target period (e.g. last 30 days, 2026-04).

Build a performance report that breaks the SP campaigns from the past {{target period (e.g. last 30 days)}}
down by search-funnel KPI (impressions → clicks → orders), at the funnel-stage
and campaign level.
Show which funnel stage the bottleneck is in.

What you can change — the target period.

What you get back — a table of funnel stage × campaign × impressions / clicks / orders / CVR / ACoS, plus the identified bottleneck stage.

Changes — no change (analysis only).

Next steps — if the click stage is weak, consider Diagnose the catalog and draft improvements; if the order stage is weak, consider Adjust bids.

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Prompt 3: Compare performance between branded and generic keywords

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

When to use — when you want to compare whether branded search or generic search sells more efficiently, and decide where to focus.

What you need — none (use it as is).

Review all SP campaigns, compare those whose names contain Brand-type keywords
against those with Generic-type keywords, and identify the ASIN groups with the
best performance (ROAS / ACoS / sales).

What you can change — the comparison metric (ROAS / ACoS / sales).

What you get back — an aggregate table for the Brand group vs the Generic group, plus a per-ASIN performance ranking.

Changes — no change (analysis only).

Next steps — Brand / Generic detection accuracy depends on how you name campaigns. To improve it, tidy up your naming rules in Classify ads with labels and naming rules Prompt 6.

Term note — Brand keywords are keywords for branded (name) searches that include your brand name; Generic keywords are non-branded, generic keywords.

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Prompt 4: Analyze unprofitability through the CAC and LTV lens

Decompose the unprofitability that ACoS alone cannot reveal through the CAC (customer acquisition cost) / LTV (lifetime value) lens, and return improvement suggestions.

When to use — when you want to separate, for a high-ACoS target, whether it’s simply worse efficiency or an investment in acquiring customers.

What you need — the target period (e.g. last 90 days; 30+ days recommended to read long-term trends).

Identify the unprofitable campaigns / unprofitable ASINs from the past {{target period (e.g. last 90 days)}},
and explain the causes through a CAC / LTV lens.
Give improvement suggestions from a customer perspective, not on an ACoS axis.

What you can change — the target period.

What you get back — a table of unprofitable campaign × CAC / estimated LTV / improvement suggestion.

Changes — no change (analysis and proposal only).

Next steps — to turn the suggestions into budget and bid changes, run them through the approval flow in Adjust budgets or Adjust bids.

Term note — LTV is an estimate from the sales history Picaro holds.

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Simulate budget allocation and brand structure

Prompt 5: Build an added-budget allocation plan to grow new-to-brand customers

For the goal of growing new customers (NTB, New-to-Brand), present an allocation plan for the additional budget.

When to use — when you want to steer additional budget toward acquiring new customers rather than harvesting existing ones.

What you need — the additional budget amount (e.g. ¥500,000).

If the ad budget is increased by {{additional budget (e.g. ¥500,000)}},
return the allocation plan best for growing NTB (new customers).
Include campaign × allocation amount × projected NTB increase.

What you can change — the additional budget amount.

What you get back — a table of target campaign × allocation amount × projected NTB increase × projected ACoS.

Changes — no change (proposal only). It only builds an allocation plan; it does not change budgets.

Next steps — to carry out the allocation, apply it through the approval flow in Adjust budgets.

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Prompt 6: Analyze performance by brand, campaign, and ad group

Analyze performance comprehensively across the three tiers of brand, campaign, and ad group, and propose both optimizations to existing ad groups and ideas for new ad groups.

When to use — when you want to view the account structure across three tiers and decide which ad groups to grow and which to create.

What you need — the target period (e.g. last 30 days), and the target brand name (leave blank for the whole connected account).

Analyze {{target brand name (e.g. Brand X)}}'s SP campaigns from the past {{target period (e.g. last 30 days)}}
at the campaign and ad-group level.
Propose both optimizations to existing ad groups and ideas for new ad groups.

What you can change — the target period / target brand name.

What you get back — a table of campaign × ad group × key metrics × optimization / new-idea proposals.

Changes — no change (analysis and proposal only).

Next steps — create new ad groups in Create Sponsored Products (SP) campaigns, and revisit existing bids in Adjust bids.

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Prompt 7: Find the optimal added-budget allocation within existing campaigns

Simulate where additional budget is best allocated within existing SP campaigns.

When to use — when you want to place additional budget where it works best inside existing campaigns, without adding new ones.

What you need — the target brand name (optional, for narrowing), and the additional budget amount (e.g. ¥1,000,000).

If {{target brand name (e.g. Brand X)}}'s budget is increased by {{additional budget (e.g. ¥1,000,000)}}
within existing SP campaigns, where is the optimal allocation?
Return campaign × recommended allocation × projected ROI.

What you can change — the target brand name / additional budget amount.

What you get back — a table of campaign × recommended allocation × projected ROI / ACoS.

Changes — no change (proposal only). It only builds an allocation plan; it does not change budgets.

Next steps — to carry out the allocation, apply it through the approval flow in Adjust budgets.

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Prompt 8: Analyze why budget is not being spent

Explain why a portfolio or campaign is not spending its budget as planned.

When to use — when you want to isolate why a reserved budget isn’t being fully used.

What you need — the target brand name, and the target portfolio name (leave blank for the whole account).

Explain why {{target brand name (e.g. Brand X)}}'s {{target portfolio name (e.g. Portfolio X)}} is not
spending its budget as planned. Break out the causes (bid too low / not enough
impression share / match types too narrow, etc.).

What you can change — the target brand name / target portfolio name.

What you get back — a table of underspending campaign × likely cause (bid level / impression share / match type / competitive strength) × suggested action.

Changes — no change (analysis only).

Next steps — if bids are the cause, address it in Adjust bids; if the budget cap is the cause, in the approval flow of Adjust budgets.

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Prompt 9: Build a pre/post-event budget allocation plan by search funnel stage

Around sales and campaign events, build a budget allocation plan for the lead-in and follow-up windows, split by search funnel stage (research / comparison / immediate-purchase).

When to use — when you want to vary how you spend for a sale, from building awareness beforehand to harvesting on the day.

What you need — the target brand name, and the event date (e.g. 2026-07-15).

Build a pre/post-event SP budget allocation plan for {{target brand name (e.g. Brand X)}}'s
{{event date (e.g. 2026-07-15)}} discount, split by search funnel stage (research / comparison / immediate-purchase).
Vary the allocation across the lead-in, day-of, and follow-up windows.

What you can change — the target brand name / event date.

What you get back — a table of funnel stage × lead-in / day-of / follow-up × budget allocation amount.

Changes — no change (proposal only). It only builds an allocation plan; it does not change budgets.

Next steps — to carry out the allocation, use Adjust budgets; to log it as an initiative, use Log initiatives and external events and verify their impact.

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Analyze profitability and performance by ASIN

Prompt 10: Calculate net ad profit contribution by ASIN

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

When to use — when you want to see ad profit contribution at the ASIN level and decide which products to push and which to scale back.

What you need — the target period (e.g. last 30 days).

For SP ads in the past {{target period (e.g. last 30 days)}}, compute by ASIN:
sales − ad spend = net ad profit contribution.
Also include net profit per order.

What you can change — the target period.

What you get back — a table of ASIN × sales × ad spend × net profit × net profit per order.

Changes — no change (analysis only).

Next steps — to shift more budget to high-profit products, use Adjust budgets; to improve the product pages of low-profit ones, use Diagnose the catalog and draft improvements.

Term note — COGS and gross margin are not factored in, so treat this as an “ad ROI” metric.

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Prompt 11: Get a daily summary for key ASINs

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

When to use — when you want to check the day-by-day movement of products you’re watching, together over time.

What you need — the target ASIN list (e.g. B0XXXXXXXX, B0YYYYYYYY; multiple allowed), and the target period (e.g. last 7 days).

Give the daily summary for {{ASIN list (e.g. B0XXXXXXXX, B0YYYYYYYY)}} over the past {{target period (e.g. last 7 days)}}.
Aggregate sales / ad spend / orders / ROAS at one row per ASIN.

What you can change — the target ASIN list / target period.

What you get back — a daily table of ASIN × date × sales / ad spend / orders / ROAS.

Changes — no change (analysis only).

Next steps — to see overall daily KPIs, use Check KPIs on the dashboard.

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Prompt 12: Identify ASINs with high CTR but low CVR

Identify ASINs whose CTR (click-through rate) is high but whose CVR (conversion rate) is low (the ad pulls strongly, but people drop off on the product page).

When to use — when you want to find products that get ad clicks but don’t convert to sales, and set the priority for product-page improvements.

What you need — the target period (e.g. last 30 days).

Identify ASINs from the past {{target period (e.g. last 30 days)}} whose CTR is above
the account average but whose CVR is low.
Add hypotheses for the likely causes (price / image / reviews / out of stock).

What you can change — the target period.

What you get back — a table of ASIN × CTR × CVR × ratio to account average × hypothesized cause.

Changes — no change (analysis only).

Next steps — for prioritizing product-page improvements, use Diagnose the catalog and draft improvements; to check inventory, use Manage inventory and sales opportunities.

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Related

• Check KPIs on the dashboard — daily KPIs as a lead-in to strategic planning
• Analyze high-performing search patterns with N-grams — search-term analysis that backs up the strategy
• Analyze where shoppers drop off from search to purchase (SQP) — compare the whole-market funnel against your own share
• Analyze cross-ad customer behavior with AMC — customer-lens analysis and query generation using AMC data
• Analyze Amazon DSP delivery performance — DSP order / line-item analysis
• Adjust budgets — execute budget allocation based on the strategy you’ve planned
• Create reports — fold the planning results into monthly reports