The Wasted Spend Skill: How Qore Finds and Cuts It on a Schedule

Ask an Amazon operator what they would hand to a machine first and the answer is almost always the same: find the wasted ad spend and stop it. Every account has a tail of search terms that take clicks and return no orders. That spend is already approved and already paid for, and it could have gone behind a term that converts.

Finding it is not the hard part. Anyone can filter a search term report for clicks with no orders. The hard part is doing it on the fifth Monday in a row, at the same thresholds, on every account you run, and then applying the negations rather than leaving them in a spreadsheet tab called "to do." Q4 arrives, a launch eats the week, and three months of waste accumulate quietly while the account still looks fine at the campaign level.

What follows is what actually counts as waste and what only looks like it, why four reasonable manual approaches all break in the same place, and how the wasted spend skill in Qore gets built, scheduled, and either held for your approval or left to run on its own once it has earned that.

Quick answer

• What it does. Runs your Amazon search term review every week without you. It finds the terms taking clicks and returning no orders, finds the ones converting above your ACoS ceiling, and proposes a negative keyword for each with a written reason attached.
• What that changes. Waste stops the week it appears rather than at the next quarterly cleanup, and the same standard reaches every account instead of only the ones somebody got to.
• You install it rather than build it. Wasted spend is the most-installed template in the Qore library, so you start from a working version and adjust it.
• The thresholds are yours. Lookback window, minimum spend, minimum clicks, and ACoS ceiling are variables, and they can differ per account and per marketplace.
• The logic opens. Every step expands, so you can read what the skill did and on what data before you trust it with anything.
• Approval first, autonomy when it is earned. Proposals land in a list and nothing touches the account until you approve. Once the calls have matched yours over several runs, you can let the skill execute on its own.

Three kinds of Amazon wasted ad spend, and only two are worth negating

A search term report will show you three different problems filed under one label, and each one calls for a different fix.

Zero-order terms with enough clicks to have been given a chance

The clean case. A search term has taken meaningful clicks over the lookback window and returned no attributed orders. The click count matters more than the spend figure here: a term with three clicks and no orders has been sampled, not tested. Amazon Ads guidance suggests waiting until a keyword has accumulated real click volume before judging it, and the same logic applies in reverse when you are deciding to shut something off.

Set a floor. Most operators land somewhere between 15 and 30 clicks with zero orders as the threshold for a confident negation, and the right number depends on your conversion rate rather than on a rule you read somewhere.

Terms above your break-even, not above a generic benchmark

The second bucket is terms that do convert, just not well enough to be worth the spend. That judgment only exists relative to your own margin. A 45% ACoS is fine on a subscription-driving SKU and a disaster on a thin-margin accessory, so a single account-wide ACoS ceiling will over-negate half your catalog. Work out the break-even per product first, then set the ceiling above it with room for the halo you believe in. Our break-even ACoS calculator does the arithmetic if you want a starting number.

The terms that look like waste and are not

These are the search terms a negation should not touch:

• Terms pointing at a listing that does not answer them. If shoppers search "stainless steel" and your bullets never mention the material, the term is not a bad target, the listing is a bad landing page. Negating it hides a content gap rather than closing it.
• Seasonal terms in their off season. A term with no orders in August may be your best converter in November.
• Brand defense terms. Competitor and defensive placements often run inefficiently on purpose.
• Terms starved by an out-of-stock SKU. Clicks against a product that cannot ship are a different problem, and we covered the fix for that in inventory-aware PPC.
• Category strings and ASIN targets. Auto-campaign category placements are not keyword negation candidates.

A numeric rule catches the first two buckets and walks straight into the third. That is the whole reason this workflow is harder to automate than it looks.

Where the manual search term review breaks down

Nobody gets to this problem without having tried the reasonable things first. Each of them keeps something and gives up something.

Export the search term report and filter it. Reliable, and you learn your account doing it. The cost is time and continuity: Amazon's search term data has a limited lookback in the console, so a week you skip is a week you cannot go back and review. Our search term report workflow covers the harvest-and-negate process in full if you are still running it by hand.

Add a bid rule. A rules engine can suppress spend on terms above a threshold and it will do it consistently. What it cannot do is read the listing to tell whether the term failed because it is irrelevant or because the page does not answer it. Every judgment is a number comparison.

Paste the report into a general-purpose AI tool. This works better than PPC people expect, and it reads a report faster than you do. The problem shows up in week three, when the same prompt on a comparable report returns a different negation threshold and a different call on your branded terms. You get a defensible answer, twice, and they disagree. We wrote up why in Qore vs ChatGPT for Amazon Ads.

Do a big quarterly cleanup. Effective, and it is the one most teams settle for. It also means the waste runs for eleven weeks before anyone looks at it, and quarterly reviews are the first thing dropped when the quarter gets busy. Our PPC audit workflow is the version worth keeping.

The common break point across all four: none of them holds your standard in a form that runs without you. The thresholds live in your head or in a prompt you rewrite each time, and the step between finding the waste and negating it is always a human retyping a list into another system.

How the wasted spend skill is built in Qore

Qore is where you take a review you do by hand and turn it into a workflow that runs itself: you describe the check once, Qore assembles it into steps you can open and read, you lock it so it stops drifting, and then it runs on your schedule. The full explanation is in what Qore is and how it works.

Start from the template, then set your own thresholds

Wasted spend is the most-installed template in the Qore library, so you are not building from a blank canvas. Installing it against an account asks you for the variables that make it yours:

• Lookback window. Defaults to 30 days. You can stretch it toward 60, but longer windows blend seasonality into the average and make a term look worse or better than it is right now.
• Minimum spend and minimum clicks. The two floors that stop the skill from negating a search term it has not yet tested.
• ACoS ceiling. The threshold above which a converting term still gets flagged.

Every one of those is editable per run and per account, which matters more than it sounds: your DE account does not behave like your US account, and the same skill running the same logic can carry different numbers in each.

Read the logic before you trust it

The skill runs as a chain of steps you can expand: load your search terms, normalize them, find the terms with clicks and no orders, find the terms converting above your ACoS ceiling, check which keywords are already negated, then surface the fixes. Open any step and you can see the data it produced. Most of the chain is deterministic joins and filters, so the same inputs return the same output. The one place a language model does work is where the workflow needs a written reason for each proposed negation, which is exactly where you want judgment and exactly where you want to be able to read it.

That inspectability is the difference between an automation you supervise and one you audit. When someone asks why a term got negated in week six, you open the run.

Turn the findings into proposed actions

A report of wasted spend is still just a report. Ask Q, the assistant that helps you shape a skill, to add negation actions for the zero-order set, and it rewrites the workflow to produce a proposed action per search term rather than a row in a table. The proposals land in an actions list with the reason attached to each one, and the skill checks whether a term is already negated so it does not stack duplicates. Negations are proposed at the campaign level by default rather than account-wide, because a term that wastes money in one campaign is often the term that earns in another. If negative keyword mechanics are new to part of your team, what negative keywords do on Amazon is the primer, and Amazon's own guidance on adding negative keywords covers the console side.

Decide how much of it runs without you

Approval is where a new skill starts, and it is a setting rather than a permanent state. The point of writing the standard down is that you can keep tightening it until it is conservative enough to act on: raise the click floor, narrow the campaigns in scope, exclude anything seasonal or branded, and what remains is the set of calls you would have approved anyway. At that point letting the skill apply them itself and escalate only the ambiguous ones costs you nothing and saves the queue.

Most teams end up somewhere in between, and per skill rather than across the board: the zero-order negations above a high click floor run on their own, while anything touching a branded term or a high-ACoS converter still comes to a person. Both live in the same skill.

Put it on a schedule and point it at your other accounts

Set a cadence and the skill runs on it. Weekly on a Monday is the usual choice for wasted spend, which lines up with how often the search term report is worth reading anyway. Then add an instance per account: same logic, own variables, one skill rather than five copies to keep in sync. Results pin to a dashboard so you can see waste across marketplaces in one view instead of opening each account to check.

This is the part that changes the economics of the review. You worked out the standard once. It now runs in as many places as you have accounts, at the same thresholds, whether or not anyone opens the tool that week.

Manual pass vs. general AI tool vs. a scheduled wasted spend skill

A scheduled skill will not out-think you on a situation nobody wrote a rule for. What it does better than you is apply a rule you already wrote, on time, in twelve places at once. That trade is the whole proposition, and it is worth understanding before you decide how much to hand over.

A bad negative keyword is the failure you never see

Negation is close to a one-way door. A term you negate stops accruing data, so a bad negation is invisible: nothing breaks, you just quietly stop appearing somewhere you should have. That is why actions in Qore start as proposals rather than changes.

A first run on a real account will typically surface far more candidates than you would ever apply in one sitting. That is the backlog of a review nobody has run in a while arriving at once, and it is exactly the moment to be selective rather than to click approve on everything. Work through the reasons attached to the proposals, spot-check the ones that surprise you against the account, and skip anything seasonal.

The honest limits:

• Qore works alongside whatever runs your bids. It reads the account, runs the analysis, and applies a negation you have approved regardless of which platform manages those campaigns, including no automation platform at all. If a third-party bid tool later overwrites a change you approved in Qore, that is a constraint of how that tool syncs, and worth knowing about before you point a skill at those campaigns.
• Qore does not invent your standard. Q can interview you about the review you already do and turn it into a workflow, and it will suggest checks worth adding. What counts as waste in your catalog is still your call, and a skill built on thresholds you have not thought about will apply the wrong ones consistently.
• Locking the logic removes drift in the logic, not in the market. Outputs change week to week because the account changes. That is the point, and it is also why comparing runs only tells you something once the logic has held still.

Qore is in open beta. You can sign up and install the wasted spend skill against a connected account today.

How to validate the wasted spend skill in four weeks

Four weeks, and then decide:

1. Week one. Install the template, set your thresholds, and run it without actions. Read the output as a report only. Compare its zero-order list against a manual export for the same window and see where the two disagree.
2. Week two. Add negation actions. Approve nothing. Read the reason attached to each proposal and mark the ones you would not have made yourself, then adjust the thresholds or the logic to account for them.
3. Week three. Approve a small batch, weighted toward the obvious cases. Note which campaigns they touch so you can check for a spend or impression shift you did not intend.
4. Week four. For the accounts where the calls have been right three weeks running, tighten the rules around the clear-cut cases and let those execute on their own. Leave approval on everything else.

If a skill has not earned auto-execute by week four, that is useful information rather than a failure. It usually means the standard needs another pass, not that the workflow does.

Cutting Amazon wasted ad spend is a scheduling problem

Finding wasted spend has never been the difficult part of this job. Finding it on the same terms every week, across every account, and then acting on what you found is where the value sits, and it is the part that gets dropped first when the week fills up.

The review becomes durable when it stops depending on someone's calendar. Write down the thresholds you already use, encode them as a skill, read the logic once so you trust it, and let it run weekly. The judgment stays yours. The Monday morning export stops being your job.

The bottom line for ecommerce teams

• Measure your own baseline before believing any benchmark. Pull 30 days of search terms, filter for terms above your click floor with zero orders, total the spend. That number is the only one that matters for your account.
• Set a click floor, not a spend floor. Three clicks and no orders is a sample, not a test. Most operators land between 15 and 30 clicks, adjusted for conversion rate.
• Use break-even ACoS per product, not one account-wide ceiling. A single ceiling over-negates half your catalog.
• Three things look like waste and are not: a content gap on the listing, a seasonal term out of season, and clicks against an out-of-stock SKU. Negating any of them hides the real problem instead of fixing it.
• Negation is close to a one-way door. A negated term stops accruing data, so a bad negation never announces itself. Approve selectively on the first run.
• The value is the schedule, not the scan. Weekly, same thresholds, every account. That is the part a manual pass cannot hold, and the part worth automating first.

If you are still working the review by hand, the search term report workflow is the version to standardize on first, and our overview of AI for Amazon ads covers the wider landscape of what these tools can and cannot do.