Optimize Amazon PPC campaigns by identifying and managing negative keywords. Reduce ad waste by eliminating irrelevant search terms while protecting valuable converting terms.
Supported platforms: Amazon、Shopify、WooCommerce、Walmart、TikTok Shop、Etsy、eBay、BigCommerce。
How to use
分析我的亚马逊搜索词报告。我每月在PPC上花费2000美元，ACoS为40%。帮我找到可以削减浪费的负关键词。
Features
- Search term report analysis to identify negative keyword candidates
- Negative keyword classification (irrelevant, low-converting, competitor)
- Campaign-level vs ad group-level negative targeting strategy
- Negative exact match vs negative phrase match decision framework
- Estimated savings calculation after negative keyword implementation
- Ongoing negative keyword management workflow
How it works
Step 1: Collect information from the user message — product, platform, current situation, and goals.
Step 2: Ask all follow-up questions at once in multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a").
Step 3: Use the framework and methodology below to conduct research and analysis.
Search Term Efficiency Tiers
Four tiers based on conversion efficiency:
- High-converting core keywords: Directly drive sales. Protect and increase bids.
- Low-frequency precise keywords: Some conversions, low clicks. Continue monitoring.
- Weakly relevant occasional converting terms: Low conversions but occasional sales. Evaluate based on CPC and conversion value.
- Completely irrelevant wasted keywords: Must be added as negatives immediately.
Framework: Classify each search term in the search term report into the corresponding tier.
Negative Exact Match vs Negative Phrase Match Logic
- Negative exact match: Only blocks exact match search terms from triggering ads.
- Negative phrase match: Blocks all search variants containing that term.
- Decision rule: Use negative exact for a single wasted term. Use negative phrase for a category of irrelevant searches.
- Example:"free" as a negative phrase blocks "free shipping", "buy free", "totally free" — capturing all waste related to "free".
Campaign-Level vs Ad Group-Level Negative Strategy
- Campaign-level negative keywords: Apply to all ad groups under that campaign. Used for terms irrelevant to the entire product line.
- Ad group-level negative keywords: Only affect a specific ad group. Used for fine-tuning across different match types.
- Strategy example:
- Exact match ad group: use negative phrase for core keywords already targeted separately
- Broad match ad group: keep open to continuously discover new search terms
Savings Estimation Model
Estimated savings = irrelevant search spend - potential lost conversion value
Before implementing each negative keyword, evaluate the net savings effect. Ensure the benefits of the negative keyword action exceed the potential losses.
Ongoing Management Framework
- Review frequency: Weekly search term report review is recommended.
- Seasonal/temporary negative keywords: Negative keywords added for seasonal promotions should be removed after the promotion ends.
- Keyword harvesting workflow: Continuously "harvest" high-converting terms from broad/automatic campaigns and upgrade them to exact match.
Data sources
- The user exports the search term report from Amazon Seller Central (recommended: at least 14-30 days).
- The report includes: search term, clicks, spend, conversions, ACoS.
- There is no direct Seller Central API connection.
Limitations
- The completeness and accuracy of the report data directly affect the reliability of the analysis.
- The search term report has a data delay of about 72 hours.
- Amazon hides some low-frequency search terms — not all wasted spend is visible.
- Results take time to appear — short-term ACoS improvements may not be obvious.
- Search term intent is dynamic — terms that are irrelevant now may become high-converting terms later (seasonality, trends). Regular review is needed.
- Cannot connect directly to the Amazon Ads dashboard — relies on user-provided data.
Step 4: Provide structured, actionable output with specific recommendations, not vague guidance.
Output format
- Start with a summary of findings
- Include specific data points and benchmarks (if any)
- Provide prioritized action items
- Estimates based on incomplete data must be marked ⚠️
- End with concrete next steps