Amazon US Apparel PPC Decision Auditor

Your ads are spending. What should you change—and what should you leave alone?

A search term keeps getting clicks, but no orders. Do you exclude it, lower the bid, or give it more time?

Another term is converting—but are those sales leaving enough room for profit after product costs and advertising?

You can see the numbers. The difficult part is deciding what they justify.

Amazon US Apparel PPC Decision Auditor helps turn that uncertainty into a reviewable decision list. Using your supplied performance data, product context, and thresholds, it flags candidates for negative targeting, lower bids, or exact-match expansion; identifies winners to protect; and marks cases where more evidence is needed.

Each finding includes its reasons, relevant risks, and evidence gaps. Review the decision—not just the ACOS number.

You stay in control. The Actor does not log in to Amazon or change your ads, and it needs no model API key. It uses rule-based calculations, with readable Markdown and structured JSON reports for your review.

Start by reading the five-row synthetic example and its expected findings on the product page. See whether the reasoning fits your review process before preparing your own data. The example demonstrates the workflow; it is not a real seller success story.

Fit, setup, and price

• Current scope: Amazon US apparel Sponsored Products, one reporting period. Prepare authorized, de-identified data in the supplied JSON format, including product costs, relevance, inventory/listing status, and your own review thresholds. Raw Amazon CSV or Excel exports are not accepted directly; these inputs are not independently verified.
• Price: $1 per 1,000 audit findings, charged by actual findings—not in 1,000-result blocks. Five findings cost $0.005 in Actor event charges. No start fee or monthly rental; run usage is included. Post-run storage and downloads may incur Apify charges.
• This is an independent tool, not an Amazon service.

See the example and current pricing.

What you receive

• Calculated advertising metrics and economic reference values based on your supplied data.
• Review candidates with reasons, risk flags, missing evidence, and human-approval requirements.
• A full JSON audit, a Markdown report, an ordered findings dataset, and a completion/checksum record.

The same input and engine version produce the same rule-based outputs. Relevance, inventory, listing status, costs, and performance thresholds must come from you; the Actor does not independently verify them.

Preview the reasoning before you run

Read two saved findings from the five-row synthetic example below. This is a static excerpt of accepted example output, not an interactive demo, a new run, or a customer case study. You do not need to submit your data or start the Actor to read it.

Both example rows have zero orders. Their different review states depend on the supplied relevance label and evidence threshold, not zero orders alone.

The JSON below copies selected fields from the archived output; other fields are omitted.

[
  {
    "row_id": "irrelevant-query",
    "state": "NEGATIVE_CANDIDATE",
    "reason": "The seller explicitly marked the non-converting query or target irrelevant.",
    "risk_flags": [
      "NEGATIVE_COLLATERAL_RISK",
      "TRAFFIC_ISOLATION_REVIEW"
    ],
    "missing_data": []
  },
  {
    "row_id": "low-evidence",
    "state": "WAIT_DATA",
    "reason": "The row has 5 clicks; the seller policy requires 10 before an efficiency action.",
    "risk_flags": [],
    "missing_data": [
      "additional_clicks_for_action=5",
      "additional_orders_for_winner=2"
    ]
  }
]

How to read it (explanation, not engine output): The first row is a negative-targeting candidate because the input explicitly labels it irrelevant; its flags keep possible collateral effects and traffic isolation in the review. The second row needs five more clicks to reach this example's action threshold, while its winner criterion separately requires two orders. These are sample policy settings, not universal advertising benchmarks.

The Actor does not change your ads. Reading this excerpt does not start a run; starting one is a separate action governed by the pricing below.

Try the synthetic example

1. In Input, select JSON and paste the complete example below, including the outer auditInput field.
2. Review the current price and your Apify usage/budget settings before starting a run.
3. Run the Actor. This example contains constructed data only, not a seller case study.
4. Accept the result only when the platform run is Succeeded and RUN_INFO.status is completed.
5. Inspect the report and compare the input fingerprint before relying on any recommendation.

The example percentages are a synthetic test policy, not recommended thresholds for your account or universal Amazon benchmarks.

{
  "auditInput": {
    "audit": {
      "audit_id": "fixture-normal",
      "as_of_date": "2026-09-03",
      "marketplace": "US",
      "currency": "USD",
      "source_note": "constructed fixture; no seller data"
    },
    "report": {
      "report_type": "sponsored_products_search_term",
      "start_date": "2026-08-01",
      "end_date": "2026-08-20",
      "conversion_lag_days": 7
    },
    "product": {
      "vertical": "apparel",
      "asin": "B0FIXTURE01",
      "parent_asin": "B0PARENT01",
      "category": "clothing_shoes_and_jewelry",
      "apparel_segment": "tops",
      "lifecycle": "evergreen",
      "launch_stage": "growth",
      "goal": "profit",
      "price_per_order": 40,
      "non_ad_variable_cost_per_order": 26,
      "listing_status": "ready",
      "featured_offer_status": "active",
      "inventory_status": "in_stock",
      "variation_health": "healthy",
      "variation_theme": "size_color",
      "cross_asin_orders_reviewed": true,
      "variation_inventory": [
        {
          "child_asin": "B0FIXTURE01",
          "size": "M",
          "color": "black",
          "inventory_status": "in_stock",
          "listing_status": "ready",
          "featured_offer_status": "active"
        },
        {
          "child_asin": "B0FIXTURE02",
          "size": "L",
          "color": "black",
          "inventory_status": "in_stock",
          "listing_status": "ready",
          "featured_offer_status": "active"
        }
      ]
    },
    "policy": {
      "target_acos": 0.25,
      "min_clicks_for_action": 10,
      "min_orders_for_winner": 2,
      "min_orders_for_expand": 2,
      "max_clicks_without_order": 15,
      "seasonal_replan_days": 14,
      "high_bid_multiplier_warning": 4,
      "allow_strategic_over_break_even": false,
      "max_bid_change_pct": 20,
      "max_budget_change_pct": 25
    },
    "rows": [
      {
        "row_id": "exact-winner",
        "campaign_id": "c-1",
        "ad_group_id": "g-1",
        "targeting_text": "women black travel shirt",
        "customer_search_term": "women black travel shirt",
        "advertised_asin": "B0FIXTURE01",
        "purchased_asin": "B0FIXTURE01",
        "match_type": "exact",
        "relevance": "relevant",
        "impressions": 1000,
        "clicks": 50,
        "spend": 20,
        "sales": 120,
        "orders": 3,
        "base_bid": 0.65,
        "daily_budget": 20,
        "placement_adjustment_pct": 20,
        "audience_adjustment_pct": 0,
        "video_adjustment_pct": 0,
        "bidding_strategy": "down_only"
      },
      {
        "row_id": "broad-winner",
        "campaign_id": "c-2",
        "ad_group_id": "g-2",
        "targeting_text": "women travel top",
        "customer_search_term": "women black travel shirt",
        "advertised_asin": "B0FIXTURE01",
        "purchased_asin": "B0FIXTURE02",
        "match_type": "broad",
        "relevance": "relevant",
        "impressions": 800,
        "clicks": 40,
        "spend": 18,
        "sales": 80,
        "orders": 2,
        "bidding_strategy": "fixed"
      },
      {
        "row_id": "converting-over-limit",
        "campaign_id": "c-3",
        "ad_group_id": "g-3",
        "targeting_text": "women casual top",
        "customer_search_term": "women casual black top",
        "advertised_asin": "B0FIXTURE01",
        "purchased_asin": "B0FIXTURE01",
        "match_type": "phrase",
        "relevance": "relevant",
        "impressions": 600,
        "clicks": 30,
        "spend": 30,
        "sales": 80,
        "orders": 2,
        "base_bid": 1.1,
        "daily_budget": 30,
        "placement_adjustment_pct": 50,
        "audience_adjustment_pct": 0,
        "video_adjustment_pct": 0,
        "bidding_strategy": "fixed"
      },
      {
        "row_id": "irrelevant-query",
        "campaign_id": "c-4",
        "ad_group_id": "g-4",
        "targeting_text": "women tops",
        "customer_search_term": "free sewing pattern software",
        "advertised_asin": "B0FIXTURE01",
        "match_type": "broad",
        "relevance": "irrelevant",
        "impressions": 500,
        "clicks": 20,
        "spend": 10,
        "sales": 0,
        "orders": 0,
        "bidding_strategy": "down_only"
      },
      {
        "row_id": "low-evidence",
        "campaign_id": "c-5",
        "ad_group_id": "g-5",
        "targeting_text": "women travel shirt",
        "customer_search_term": "women travel shirt",
        "advertised_asin": "B0FIXTURE01",
        "match_type": "exact",
        "relevance": "relevant",
        "impressions": 200,
        "clicks": 5,
        "spend": 3,
        "sales": 0,
        "orders": 0,
        "bidding_strategy": "fixed"
      }
    ]
  }
}

Expected example findings

The original private 0.1.1 acceptance test produced these five findings, with the full audit, report, and dataset matching the local expected outputs:

Example row	Review state
irrelevant-query	NEGATIVE_CANDIDATE
converting-over-limit	LOWER_BID_CANDIDATE
broad-winner	EXPAND_EXACT
exact-winner	PROTECT_WINNER
low-evidence	WAIT_DATA

These are synthetic outcomes, not evidence of real advertising performance. Other valid inputs can produce additional states, including DATA_CONFLICT when the supplied totals contradict each other.

Use your own data

Replace the example with authorized, de-identified data. The auditInput object requires audit, report, product, policy, and rows. Keep the marketplace US and product vertical apparel.

Use one consistent reporting period, currency, attribution definition, and relevant product context. Do not combine materially different product economics into a single context. Use unique row_id values. Supply non-negative impressions, clicks, spend, sales, and orders.

Rates use decimals: 0.25 means 25%. Bid adjustment percentages use percentage points. Enter your own evidence thresholds and documented product costs; do not copy the synthetic policy into a live decision without review.

Adapter limits are 1–1,000 rows, 2 MiB of input, 4,096 characters per text field, and nesting depth 16. These are validation limits, not a runtime or performance guarantee. The hosted acceptance example contained five rows.

Where to find the results

Output	Purpose
OUTPUT	Complete audit JSON in the run's key-value store
REPORT.md	Readable Markdown audit report
Default dataset	Ordered, structured findings
RUN_INFO	Completion status, versions, input/output fingerprints, and finding count

Failed runs can leave partial artifacts. A file being present is not proof that the audit completed. Reuse neither an old result nor a failed run's partial dataset as a completed audit.

A candidate for a bid or budget change is not an instruction to apply it automatically. Check attribution, inventory, economics, and the underlying report before making any change in Amazon.

Supported scope and limits

This version supports normalized JSON for US apparel Sponsored Products and one reporting period. It does not support raw Amazon report uploads, CSV/XLSX uploads through this Actor, URL imports, other marketplaces, other product categories, or period-over-period comparisons. The separate original engine's CSV interface is not an input feature of this hosted Actor.

Adapter release: 0.1.2; unchanged calculation engine: 1.1.0.

The engine uses a dated rule snapshot, recorded in RUN_INFO.rule_snapshot; the tested snapshot is dated 2026-09-03. This is not live Amazon policy monitoring. Recheck applicable current rules before consequential action.

Data handling

No model API key or Amazon credentials are needed. Do not submit customer personal data, passwords, tokens, or confidential data you are not authorized to process.

The calculation engine does not fetch URLs or send data to a model provider. When hosted, the Actor uses Apify APIs for run input and output storage; it is not a claim that data stays only on your computer. Retention and access depend on Apify storage and your account settings.

Credential-like field names are rejected, but this is not comprehensive personal-data or secret detection. De-identify the input yourself, and never post sensitive reports or signed storage links in public issues.

Pricing

$0.001 per finding written to the default dataset ($1 per 1,000 results). This is a per-result rate, not a minimum purchase of 1,000 results. Each user's own runs are metered separately. Five findings cost $0.005 in Actor event charges. All finding states, including WAIT_DATA and DATA_CONFLICT, use the same rate.

There is no Actor start fee or monthly rental. Platform usage during the run is included in the event price; storage and downloads after the run can incur Apify's standard charges. Check the live pricing panel and your account settings before starting.

The Actor requires enough remaining event budget for the entire audit. If it cannot afford all findings, it stops with RUN_INFO.status=budget_exceeded, zero dataset findings, and no full report. The platform may show this graceful stop as Succeeded; it is not a completed audit. Increase the budget only if you choose to start a new run.

A later storage failure or interruption can leave partial artifacts. Results already written to the default dataset may be charged even if the final completion marker is missing; this Actor does not automatically refund them. Inspect the run before repeating it. Resurrecting an existing run is blocked to prevent duplicate dataset writes. Starting a new run is a separate, billable request.

The synthetic dataset event is charged by Apify automatically, with no second manual charge. A runtime billing count is not proof of a collected payment or publisher revenue.

Questions and troubleshooting

Does it use DeepSeek or another AI model?
No. This version uses deterministic calculations and rules.

Will it change my ads?
No. All external changes require a separate human decision and are outside this Actor.

Why do I see WAIT_DATA or DATA_CONFLICT?
A finding may identify missing evidence, an immature report, or inconsistent totals. Review the named evidence; the Actor does not silently repair contradictory source data.

How do I report a problem?
Use this Actor's Issues tab. Include the adapter version, the error message, and a minimal synthetic reproduction. Do not include credentials, personal data, or private advertising reports.

Use is subject to the terms linked from the Actor's Apify Store page. This documentation does not grant permission to redistribute the underlying code.