For Amazon sellers
Amazon Auto Ads: Run, Monitor, and Optimize Automatic Campaigns with AI Support
Amazon auto ads (automatic targeting campaigns) are the fastest way to start advertising on Amazon and the best source of keyword discovery data you have access to. StoreClaw monitors your auto campaign performance, surfaces the converting search terms worth promoting, and prepares bid and budget recommendations for your review.
How StoreClaw Manages Your Amazon Auto Campaigns
Turn Auto Campaign Data Into Manual Campaign Targets
The primary value of Amazon auto ads is not the direct sales they generate — it is the search term data they reveal. Every week, your auto campaigns accumulate a Search Term Report showing which customer queries triggered impressions, clicks, and conversions. StoreClaw processes this data and surfaces the converting search terms worth promoting to manual campaigns, the non-converting terms worth adding as negative keywords, and the match type strategy for each transition.
Performance Monitoring With Recommendations
StoreClaw monitors your auto campaign performance against your target ACoS and daily budget, surfaces underperforming ad groups, and prepares bid adjustment recommendations for your review. Nothing changes in your campaigns without your approval — but you see exactly what needs attention and what action would improve performance.
How Amazon Auto Ads Work and When to Use Them
Auto Targeting vs. Manual Targeting: The Right Use for Each
Amazon advertising campaigns use either automatic targeting (Amazon chooses which search queries trigger your ad) or manual targeting (you specify the exact keywords and match types). Auto ads are not a set-and-forget tool — they are a discovery tool. Amazon’s algorithm tests your product against a broad range of related searches to find which ones convert, generating a stream of search term data you don’t have access to through any other channel. Manual campaigns are where you extract value from that data by bidding deliberately on the terms that are already proving they convert.
The Four Auto Targeting Match Types
Match Type | What It Does | When to Use |
Close match | Shows ad for searches closely related to your product | Always on — the highest-relevance targeting type |
Loose match | Shows ad for broadly related searches | Use at low bids to capture discovery data |
Substitutes | Shows ad to customers browsing competitor products | Use to capture competitor consideration traffic |
Complements | Shows ad on related but non-competing product pages | Use for cross-sell and category expansion |
The Auto-to-Manual Workflow: How Serious Amazon Advertisers Use Auto Campaigns
The most effective Amazon PPC structure uses auto campaigns as keyword feeders for manual campaigns. The workflow: run auto campaigns at a controlled budget for two to four weeks, download the Search Term Report, identify terms with at least three to five clicks and at least one conversion, add those terms to manual campaigns at their appropriate match type, and add the zero-conversion terms to your negative keyword list. Repeat every two to four weeks. StoreClaw automates the analysis step: it processes your Search Term Report, categorizes terms by conversion status, and prepares a transfer list for your review.
Bid Strategy: Starting Bids for Auto Campaigns
Auto campaign bids should start conservative and increase based on performance data. A starting bid of $0.50–$0.75 for most categories gives the campaign enough budget to accumulate data without over-investing before you know which match types and search terms are converting. After two weeks of data, adjust bids by match type: close match typically earns the highest bids, loose match the lowest. StoreClaw monitors your spend-to-sales ratio by match type and surfaces bid adjustment recommendations when performance deviates from your target.
When to Pause Auto Campaigns
Auto campaigns should not run indefinitely at full budget once you have sufficient search term data. After 60–90 days of consistent data collection, the marginal value of new auto campaign discoveries decreases while the spend continues. At this point, most sellers reduce auto campaign budgets and redirect spending toward manual campaigns built on confirmed converters. StoreClaw flags this transition point based on your campaign data — when auto campaign discovery rate drops below a threshold, it surfaces the recommendation to rebalance budget allocation.
Frequently Asked Questions
Everything you need to know about StoreClaw for Amazon.
Amazon auto ads (automatic targeting campaigns) are Sponsored Products campaigns where Amazon’s algorithm determines which customer search queries trigger your ad, based on your product listing content and category. They are primarily a keyword discovery tool — the search term data they generate is used to build more precise manual campaigns.
Both, in sequence. Run auto campaigns first to discover which search terms actually convert for your product. Then build manual campaigns targeting those proven converters. Most experienced Amazon sellers run both simultaneously — auto campaigns at lower bids for ongoing discovery, manual campaigns at higher bids for known performers.
Two to four weeks generates enough data for a meaningful first analysis on most products. High-volume products may have enough data after one week. Low-traffic products may need six to eight weeks to accumulate statistically useful sample sizes.
StoreClaw monitors auto campaign performance, processes Search Term Report data, identifies converting terms to promote to manual campaigns, flags negative keyword candidates, and prepares bid adjustment recommendations — all for your review before any campaign changes execute.
Auto campaigns typically run at higher ACoS than manual campaigns because they include discovery-phase spend on non-converting terms. A target ACoS of 30–50% for auto campaigns is reasonable during the discovery phase. Once you have isolated the converting terms in manual campaigns, your overall portfolio ACoS should improve as budget shifts toward higher-precision targeting.