Google Ads Negative Keywords for SaaS: Build a Clean List

If your google ads negative keywords for saas campaign is getting clicks from people searching “free,” “cheap,” or “jobs,” you do not have a traffic problem — you have a negative keyword problem. That is not just a tidy PPC opinion. Deloitte (2021) explicitly recommends using negative keywords to stop ads from triggering on phrases such as “free” and “cheap,” and it also argues that paid search should start with alignment between business goals and the right keywords. For SaaS teams, that point matters more than it first appears, because one low-intent query theme can poison more than spend: it can distort CTR, mislead Smart Bidding, drag down lead quality, and teach the account to chase the wrong user.

We have seen this pattern repeatedly in SaaS search programs. A team launches broad match around a high-value category term, the campaign starts spending, conversions appear, and everyone relaxes too early. Then sales flags the obvious issue: demos are full of students, job seekers, existing users looking for login pages, or buyers hunting for a free tool that was never part of the offer. Search volume was not the problem. Traffic qualification was.

That is why the best SaaS Google Ads accounts do not win by adding more keywords. They win by saying no faster and more precisely than competitors. A clean negative keyword system protects intent, cuts wasted spend, and keeps AI-driven campaigns from learning bad habits. The rest of this article shows how to build that system without choking off real demand.

Why SaaS negative keywords matter now

Modern Google Ads accounts are less forgiving than they used to be. Karooya (2025) argues that negative keywords are now a proactive control lever in AI-heavy campaigns such as Performance Max and Demand Gen, not just a reactive cleanup tool. Pair that with Deloitte (2021), which recommends excluding phrases like free and cheap, and the message is clear: negatives now sit much closer to the core of campaign strategy.

For SaaS, this matters because search intent is unusually noisy. Many software categories attract buyers, researchers, existing users, students, job seekers, and people looking for templates or documentation — all inside the same keyword cluster. If you bid on crm software, project management tool, billing platform, or sales forecasting software, Google can find traffic that looks relevant at a surface level while being commercially useless.

Why are SaaS search terms so messy?

SaaS categories naturally produce mixed-intent search behavior. A user typing “project management software” may be a VP evaluating vendors, a freelancer looking for a free app, a current customer trying to log in, or a candidate researching careers. The keyword itself cannot tell you enough. The modifiers do.

Consider a hypothetical SaaS company spending $18,000 per month on search. One campaign targets team collaboration software and gathers 2,400 clicks at $7 CPC, which means $16,800 spent. At first glance, the campaign seems healthy because it generates 120 conversions at $140 CPA. But once sales qualification comes in, the breakdown looks different:

• 35 leads came from free or cheap queries
• 18 leads came from jobs, salary, or career searches
• 22 leads were existing users searching login or support
• 11 leads came from student or training-related searches
• Only 34 leads were sales-qualified

That means the real qualified CPA is not $140. It is $16,800 / 34 = $494. Same campaign. Same dashboard. Very different business reality.

The edge case is important: messy search terms are not always bad. In categories with emerging demand, broad intent can surface adjacent buyers you did not expect. The mistake is not broadness by itself. The mistake is broadness without a filtering system.

What negative keywords actually protect

Teams often talk about negative keywords as if they only save budget. They do more than that. They protect four assets inside a SaaS ad account:

• Budget efficiency by reducing irrelevant clicks
• Lead quality by filtering out low-value intent
• Model training data by keeping conversion signals cleaner
• Sales capacity by preventing reps from wasting time on junk inbound

That third point matters more each year. If your account keeps attracting users searching template, tutorial, open source, or free alternative, your bidding system can start overvaluing the wrong query class simply because those users convert on weak on-site actions.

This is why negative keyword work belongs next to bidding strategy, landing page alignment, and offer design. It is not a junior admin task. In fact, it often becomes more important when you improve the rest of the funnel. Once ads and landing pages become sharper, any mismatch in traffic source becomes easier to see. If you are revisiting offer-message fit, our guide to ad copy choices that qualify clicks before they happen is a useful companion.

The contrarian truth about scale

Many SaaS teams assume scale comes from opening the top of the funnel wider. Often the opposite is true. Cleaner exclusions can improve downstream conversion quality enough that you can afford to bid more aggressively on the traffic that remains.

Take a simple example. Suppose you cut 15% of clicks by excluding irrelevant searches, but the remaining traffic lifts your SQL rate from 22% to 34%. Even if raw lead volume dips, the pipeline yield can improve materially. That is not glamorous work, but it is how efficient paid search accounts are built.

The next question is practical: if random keyword dumps are not the answer, how do you decide what actually belongs on the negative list? That starts with intent, not instinct.

Build your negative list from intent

Deloitte (2021) recommends aligning business goals with the right keywords before launching paid search campaigns, and Define Digital Academy (2024) advises advertisers to review performance data regularly and add negative keywords to reduce wasted spend. Put those together, and you get the right principle for SaaS: do not build a negative list by copying a giant spreadsheet from the internet. Build it by asking which queries can never produce the kind of buyer you actually want.

We use a simple decision model for this.

The Intent Filter Framework

The Intent Filter Framework classifies every query into one of six buckets: buyer, researcher, job seeker, support user, competitor, or irrelevant. The point is not to label queries for fun. The point is to make exclusion decisions consistent across campaigns and across team members.

A query should not become negative because someone on the team “doesn’t like it.” It should become negative because it repeatedly falls into a class that does not produce qualified pipeline.

Which search terms should SaaS always block?

There is no universal SaaS blocklist, but several intent classes usually belong in the negative system from day one if your goal is qualified demos or sales conversations.

Typical universal exclusions include:

• free
• cheap
• jobs
• careers
• salary
• internship
• login
• sign in
• support
• help center
• docs or documentation if you are not monetising developer education
• training or course when your product is not an education offering

Why these? Because they signal a user who is not in the market for your core commercial offer. Deloitte’s recommendation to exclude free and cheap is directly relevant here, especially for SaaS accounts where bargain intent tends to correlate with weak conversion quality rather than hidden enterprise demand.

The edge case: if you run a freemium product, free is not automatically a bad term. If your business model depends on free trial acquisition that later expands through product-led growth, blocking free account-wide would be reckless. The negative list has to reflect the revenue model, not generic PPC folklore.

How do you separate bad intent from good curiosity?

This is where teams tend to overcorrect. Not every research-oriented search is low value. A query such as “best billing software for SaaS” may indicate an early-stage buyer with real budget. A query such as “what is billing software” may still be useful if your nurture path works well. But “billing software tutorial pdf” is usually a different story.

A practical rule:

• If the query suggests commercial evaluation, keep or test it
• If the query suggests education only, lower priority and monitor
• If the query suggests existing user activity, exclude
• If the query suggests employment intent, exclude
• If the query suggests price-only bargain hunting that mismatches your product, exclude

Here is a hypothetical scoring model using the Intent Filter Framework:

Now put numbers on it. Suppose a campaign generated 500 clicks last month from five recurring low-intent modifiers: free, jobs, login, support, salary. Average CPC was $6.40. That is $3,200 in spend. If those clicks produced only 2 SQLs, while the rest of the campaign produced 28 SQLs from $9,600 in spend, your low-intent traffic had a $1,600 SQL cost, versus $343 for the rest. In that context, excluding those modifiers is not conservative. It is rational.

When curiosity becomes pipeline

There is one more nuance worth keeping. Curiosity-driven searches can become pipeline if the landing page and follow-up path are built for it. This is where keyword strategy and landing page strategy meet. A team with strong educational content, careful retargeting, and tailored conversion paths can sometimes profit from softer search terms that would fail in a direct-demo funnel. If you want to pressure-test that route, our piece on where AI-driven landing page personalisation helps and where it does not is directly relevant.

Intent mapping gives you the decision rule. The next step is operational discipline: where do these negatives actually live so the list stays usable instead of turning into account archaeology?

Use a layered negative keyword system

Karooya (2025) recommends a layered negative keyword strategy with account-level, campaign-level, and ad-group-level exclusions. That advice is exactly right for SaaS accounts because intent pollution rarely happens in only one place. Some terms are bad everywhere. Others are bad only in specific offers. Without structure, the list becomes a pile of contradictions.

We call this the Three-Layer Negative System. It is simple enough to run weekly and disciplined enough to scale across multiple products, geographies, and funnel stages.

The Three-Layer Negative System

The framework works like this:

• Account level: universal exclusions that never produce value
• Campaign level: exclusions tied to a specific offer, audience, or buying stage
• Ad group level: precision exclusions that protect close keyword themes from cannibalising each other

Think of it as traffic governance. The higher the layer, the more confident you should be that the term is wrong everywhere.

What belongs at account level?

Account-level negatives should be boring, obvious, and durable. This is where you put terms that almost never produce a qualified SaaS buyer across the account.

Typical examples:

• jobs
• careers
• salary
• internship
• free download
• torrent
• crack
• customer support
• login
• help desk number if you do not want support traffic

Imagine a SaaS company with four campaigns: brand, competitor, category, and integration. If jobs appears in all four search term reports and consumes 220 clicks at $5.80 CPC, that is $1,276 in wasted spend. Add it at account level once, and you remove the issue globally.

The edge case is brand. Some branded queries that include support or login may still matter if your company intentionally wants paid search to help users find critical pages. Most B2B SaaS teams should not pay for that traffic, but not all businesses treat support visibility the same way.

What belongs at campaign level?

Campaign-level negatives manage offer-specific noise. They matter when one term is bad in one campaign but useful in another.

Example: suppose you sell both enterprise analytics software and a self-serve reporting tool.

• In the enterprise demo campaign, you may want to exclude free, template, excel, and download.
• In the self-serve campaign, excel template could still be useful if it leads into a lower-friction offer.

A practical scenario:

A campaign for demo requests spent $12,400 in a month and drove 1,700 clicks. Search terms containing template generated 140 clicks at $6.20 CPC, costing $868. Those clicks produced 18 form fills, but only 1 became an SQL. The same campaign’s non-template traffic produced 39 SQLs. Excluding template at campaign level cuts a weak-intent branch without affecting other campaigns where templates may still support top-of-funnel acquisition.

That distinction matters. Teams that put too much at account level lose flexibility. Campaign-level negatives are where strategy stays nuanced.

What belongs at ad group level?

Ad-group-level negatives are precision tools. They help route very similar searches to the right ad and landing page.

Suppose you run separate ad groups for:

• crm for startups
• crm for enterprise
• crm for agencies

You may add enterprise as a negative to the startup ad group, startup as a negative to the enterprise ad group, and agency as a negative to both when it belongs elsewhere. This protects message match, which in turn supports CTR, conversion rate, and Quality Score.

Define Digital Academy (2024) recommends tightly themed ad groups to improve ad relevance and Quality Score. Ad-group-level negatives are one of the least glamorous but most effective ways to make that structure hold up in practice.

A quick comparison of the three layers

Karooya also notes that Google increased the negative keyword limit in Performance Max to 10,000 per campaign and that Google now applies automatic misspelling coverage to negatives. Those are useful updates, but they do not change the core operating rule: a bigger list is not a better list. Structure beats volume.

Once you have the operating system, you can populate it much faster. That brings us to the practical part most teams actually came for: the starter list.

The SaaS negative keyword starter list

Deloitte (2021) gives the clearest universal signal by calling out free and cheap as terms advertisers should exclude, while Define Digital Academy (2024) reinforces the need to add negatives continuously as performance data comes in. So yes, you need a starter list. But you need it grouped by intent so you can apply it intelligently.

Which words usually waste SaaS spend?

For most B2B SaaS accounts, these buckets catch a large share of wasted clicks early.

Free and bargain terms

• free
• cheap
• cheapest
• low cost
• discount
• coupon
• promo code
• crack
• torrent
• nulled

Jobs and career terms

• jobs
• careers
• hiring
• salary
• internship
• recruiter
• vacancies

Existing-user and support terms

• login
• sign in
• support
• help center
• customer service
• phone number
• documentation
• docs
• status page

Education-only terms

• tutorial
• pdf
• course
• training
• certification
• syllabus
• ppt
• lecture

Consumer and non-B2B terms

• personal use
• family
• students
• school project
• home use
• gaming

This is where context matters. A company selling developer infrastructure may not want to exclude docs globally. A product-led tool may keep free but still exclude crack and torrent. A SaaS business with a strong education-led content funnel might keep tutorial in one campaign and block it in another.

Which competitor terms should you exclude?

This is one area where teams often become strangely doctrinal. Some marketers insist all competitor terms are valuable. Others want them all blocked. Neither view is serious.

We split competitor searches into three classes:

• Competitor evaluation: often worth testing
• Competitor support or login: usually exclude
• Competitor careers or docs: exclude

For example:

• [competitor] alternative can show buying intent
• [competitor] pricing comparison may deserve a dedicated landing page
• [competitor] login is almost always support traffic
• [competitor] careers is noise

A hypothetical account targeting five competitor brands sees this monthly pattern:

• 320 clicks from alternative, vs, and comparison terms at $8.20 CPC = $2,624 spend, producing 14 SQLs
• 190 clicks from login, support, and help terms at $7.70 CPC = $1,463 spend, producing 0 SQLs

Excluding the second group improves the economics of competitor bidding immediately without killing the first group. If you are building competitor campaigns seriously, our guides on finding competitor keyword gaps and tracking rival ad positioning can help you separate strategic conquesting from random brand leakage.

A practical starter taxonomy

Here is a simple way to seed your system in week one:

• Put jobs, careers, salary, internship, login, support, phone number, torrent, and crack at account level
• Put free, cheap, template, pdf, tutorial, and training at campaign level unless your offer model says otherwise
• Put close-routing terms such as agency, enterprise, startup, self-serve, or small business at ad-group level

Now a numerical example. Suppose you start with 60 starter negatives across these buckets and monitor two weeks of data. Search-term review shows that 14 of them actively prevented irrelevant traffic, reducing click volume by 11%. Yet SQLs fell by only 2%, while cost per SQL improved from $410 to $332. That is the sort of trade-off you want: slightly less traffic, much better signal.

The contrarian point: giant downloadable lists often feel productive but create false confidence. If the list does not reflect your revenue model, pricing strategy, user journey, and sales motion, it is just clutter in spreadsheet form.

The starter list gets you moving. But exclusions can backfire if you become too aggressive, especially now that Google’s AI systems can discover useful demand patterns you did not predict.

When negatives can hurt performance

Google Ads Help (2025) says Smart Bidding Exploration campaigns see, on average, an 18% increase in unique search query categories with conversions and a 19% increase in conversions. That should make every SaaS advertiser pause before turning negative keyword management into a scorched-earth exercise. Exploration has value. If you block too early, you can kill the very searches that would have expanded the account.

This is the necessary counterweight to the rest of the article. Yes, clean lists matter. But overblocking is real.

Can negative keywords block good demand?

Absolutely. The most common mistake is blocking modifiers that signal early-stage commercial intent just because they do not look purchase-ready enough.

Examples that teams often exclude too quickly:

• best
• comparison
• vs
• reviews
• pricing
• software examples

For a high-consideration SaaS purchase, these terms often sit in the middle of the buying journey, not outside it. A CFO searching “best spend management software for SaaS” is not low intent simply because the query is evaluative.

Imagine you exclude comparison, best, and vs account-wide. Over the next month, clicks fall by 9%, which looks efficient. But SQL volume falls by 17%, because those searches had a stronger buyer profile than direct category searches. This is why negative work needs data, not just neat categorisation.

Why AI campaigns need guardrails, not handcuffs

Google’s push toward broader automation is not subtle. The same Google Ads Help (2025) update highlights the expansion of Ads in AI Overviews, notes that AI Max for Search is Google’s fastest-growing AI-powered Search ads product, and points to more visibility into placements and search partner reporting. The direction of travel is obvious: Google wants systems to explore more query space, not less.

That makes negative keywords more important and more delicate at the same time. In AI-led campaigns, your job is not to dictate every outcome manually. Your job is to create guardrails that remove obvious junk while preserving room for machine discovery.

A practical rule:

• Exclude known bad intent immediately
• Observe ambiguous intent until you have volume and downstream data
• Test promising research intent in separate campaign structures before excluding it globally

A simple overblocking test

Use this four-week check when performance stalls after major negative list changes:

1. Compare click volume, lead volume, SQL volume, and pipeline value before and after the exclusions
2. Segment by the exact modifiers you blocked
3. Reintroduce one ambiguous modifier in a controlled campaign if SQL loss looks disproportionate
4. Measure quality, not just top-of-funnel efficiency

A hypothetical example:

• Before exclusions: 1,900 clicks, 96 leads, 29 SQLs, $21,000 spend
• After exclusions: 1,650 clicks, 82 leads, 20 SQLs, $18,500 spend

At first glance, CPA improved. But cost per SQL worsened:

• Before: $21,000 / 29 = $724
• After: $18,500 / 20 = $925

What happened? The excluded terms likely contained useful mid-funnel traffic. This is exactly why we prefer disciplined exclusions over ideological ones.

That tension between control and discovery leads to the operational question that separates tidy theory from real account performance: how do you keep the list clean over time without letting it sprawl or calcify?

How to keep the list clean

Define Digital Academy (2024) recommends reviewing performance data regularly and adding negatives to reduce wasted spend, and Karooya (2025) stresses layered negatives plus proactive use in AI-heavy campaigns. In other words, the list is not a one-time asset. It is a maintenance system.

If you only touch negatives during quarterly cleanups, you are usually late. The account has already spent money teaching itself the wrong lessons.

How often should you review search terms?

The honest answer depends on spend and volatility.

A practical review cadence:

• Weekly for campaigns spending more than $5,000/month
• Twice weekly for new launches, broad-match tests, or unstable lead quality periods
• Biweekly for mature, low-volume campaigns with stable query patterns

If your SaaS account runs broad match or AI-assisted expansion, weekly review is the minimum. That is especially true in categories where search intent shifts quickly around pricing pressure, market news, or new feature launches.

A useful external reminder comes from Statista (2020), which reported that CPC patterns changed sharply during the pandemic, with the insurance industry reaching almost one U.S. dollar CPC in March 2020 and sectors such as pharmaceuticals, online education, and beauty/skincare seeing CPC increases. The point is not the sector mix itself. The point is that query economics move when market context changes. Static negative lists do not adapt to that.

What should you do with recurring junk queries?

When the same low-intent queries appear more than once, stop treating them as anomalies. Promote them into the formal negative system.

We recommend a simple decision threshold:

• If a term appears 3+ times with 0 qualified leads, add it to the appropriate layer
• If a modifier cluster spends more than 2% of campaign budget with weak downstream quality, review for exclusion
• If a term generates conversions but 0 SQLs over a meaningful sample, downgrade or exclude it depending on campaign goals

Example:

A campaign spends $9,400 in one month. Queries containing “template” account for $290 in week one, $240 in week two, and $310 in week three. That is $840, or nearly 9% of spend, with 7 leads but 0 SQLs. At that point, the account is not “still learning.” It is leaking.

Keep the list tied to ad-group structure

Define Digital Academy (2024) also recommends tightly themed ad groups to improve relevance and Quality Score. That advice matters because messy campaign architecture makes negative review harder. If one ad group contains six unrelated intent themes, you cannot tell whether a query is bad or merely misrouted.

This is one reason negative keyword work often reveals broader account problems. If search terms look chaotic, the issue may not just be the list. It may be campaign design, ad message, or landing-page mismatch. Teams running active landing page programs often find that better structure on the destination side makes bad intent easier to identify. If that is on your roadmap, our guides to testing page variants without breaking discovery and running a sharper CRO audit fit naturally with this process.

The maintenance rule most teams skip

Document why a negative exists.

That sounds administrative, but it prevents two expensive mistakes:

• nobody remembers why the term was blocked, so it stays forever
• someone removes it during a restructure and the old waste returns

Keep a simple note next to high-impact negatives:

• Modifier
• Layer
• Reason for exclusion
• Last reviewed date
• Owner

This is dull work. It is also the kind of work that keeps a growing SaaS account from relearning the same bad lesson every quarter.

A clean maintenance habit still needs one final rule: what should remain under human control, especially now that automation keeps expanding into search and placement decisions?

The clean-list rule for SaaS accounts

Forrester (2024) offers a useful warning from another part of digital advertising: static keyword exclusion lists have mistakenly demonetised content containing words like “protest,” “gay,” and “covid,” and even a 2023 TIME Person of the Year article featuring Taylor Swift was reportedly marked unsafe because it contained the word “feminism.” Forrester’s conclusion is blunt: static exclusion logic is not enough. That lesson maps neatly onto SaaS search. Keyword blocking without context can create blind spots.

At the same time, Google Ads Help (2025) shows that AI-led exploration can increase both query diversity and conversions. Put those two sources together and you get the operating rule we trust: exclude with evidence, not instinct.

What should never go on autopilot?

Three things should stay under active review:

• Ambiguous modifiers such as best, comparison, pricing, reviews
• Competitor terms that split between support noise and commercial conquesting
• Category-adjacent educational searches that may support demand creation in some funnels

These are the terms most likely to swing between useless and valuable depending on campaign goal, landing page, and offer structure. They deserve human judgement.

How do you avoid overblocking in AI-era Google Ads?

Use what we call the Clean-List Rule:

If a term does not help you find a qualified buyer, and it keeps showing up in wasteful clicks, exclude it. If it might reveal new demand, test it before blocking.

That sounds obvious, but it solves a real habit problem. Teams either block too little because they fear losing volume, or they block too much because tidy lists feel efficient. The right move sits in the middle: remove obvious waste, isolate ambiguous demand, and let the account prove what belongs.

A final practical checklist

Before you add any negative, ask four questions:

• Does this term repeatedly attract the wrong intent class?
• Has it spent enough money to justify a decision?
• Would excluding it here break another campaign’s strategy?
• Do we have downstream evidence from SQLs, opportunities, or pipeline — not just form fills?

If the answer pattern is yes, yes, no, yes, the exclusion is probably right.

One more nuance matters. Google Ads Help (2025) also notes that advertisers now have more placement reporting visibility and can use curated third-party exclusion lists for brand-safe placements. The broader lesson is that visibility should shape exclusions. We should block based on observed risk and observed waste, not assumptions from six months ago.

A clean negative keyword system is not a giant blacklist. It is a living filter between your budget and the market. Once you see it that way, the work becomes less about housekeeping and more about protecting the economics of acquisition. That is exactly where the right tooling can make the difference between occasional cleanup and consistent control.

Build cleaner acquisition with dynares.ai

If this article sounded familiar, it is because many SaaS teams do not actually need more traffic — they need better traffic control, faster search-term analysis, and tighter alignment between ads, landing pages, and conversion quality. dynares.ai helps with that by giving teams sharper visibility into performance patterns, faster iteration across paid acquisition workflows, and the operational structure needed to act before wasted spend compounds. That matters when you are trying to spot junk intent early, test ambiguous queries without polluting core campaigns, and connect click-level patterns to landing-page outcomes instead of judging everything on raw lead volume.

The same discipline we covered here runs through the rest of the paid growth system. Cleaner exclusions work best when paired with stronger ad qualification, better page-message match, and faster experimentation cycles. That is where dynares.ai fits: helping SaaS marketers build more precise acquisition systems so they can stop paying for the wrong clicks and start learning from the right ones. If your account is spending too much time rediscovering the same waste, now is a good time to put a cleaner operating system in place.