Machine Vision Negative Keywords: What to Exclude

Machine vision negative keywords are terms added to prevent ads from showing for irrelevant searches. This topic matters when running machine vision PPC, because broad terms can attract low-intent traffic. Excluding the wrong searches can save budget and improve lead quality. This guide covers common machine vision negative keywords and what to exclude in practical ways.

For help aligning PPC with machine vision goals, a machine vision PPC agency can support setup and ongoing tuning: machine vision PPC agency services.

Quality also ties into ad review and campaign signals, which can connect to machine vision quality score topics like landing page fit and ad relevance: machine vision quality score.

What negative keywords do in machine vision ads

Negative keywords block search matches

Negative keywords stop ads from showing when a search includes a listed term. This includes exact matches and phrase matches, depending on how the negative keyword is added. The goal is to reduce wasted clicks from people searching for the wrong thing.

“Machine vision” can pull in many unrelated intents

The phrase “machine vision” may relate to software, cameras, research papers, robotics, or image processing. Some searches focus on theory only, while others target unrelated industries. Negative keyword lists help separate these intents.

They work with match types and account structure

Negative keywords work best when used with careful match types for keywords and ad groups. When ads are grouped by solution type, negatives can be targeted to that group. This reduces the chance of blocking useful traffic.

How to build a machine vision negative keyword list (quick framework)

Start from search terms, not guesses

The most useful negative keywords come from search term reports. Those reports show the exact phrases that triggered impressions. After review, irrelevant terms can be added as negatives.

Group exclusions by intent type

Most exclusions fall into a few intent groups. Using intent groups makes the list easier to manage and helps avoid accidental blocks.

• Jobs and career intent (resume, hiring, salary, internships)
• Academic and theory intent (paper, thesis, survey, research)
• How-to without commercial intent (tutorials, forums, “how does it work”)
• Non-machine-vision products (unrelated camera systems, consumer apps)
• Wrong application area (custom unrelated industry terms)

Use a “safe” test before large exclusions

New negatives can be added in stages. Monitoring after each change helps catch cases where excluded terms were still relevant. This approach may reduce disruptions.

Machine vision negative keywords to exclude by intent

Exclude hiring, salaries, and job boards

Searches about jobs rarely convert into B2B service leads. Common negative keyword ideas include terms tied to hiring and compensation.

• jobs, job, hiring
• salary, salaries, pay, compensation
• intern, internship, graduate program
• career, careers, recruiter, recruitment
• resume, CV, cover letter
• LinkedIn jobs, Indeed jobs, Glassdoor

In some cases, hiring queries can still be useful for recruiting pages, but those should be separated from lead-gen ads.

Exclude academic writing and citations

Academic searches may include papers, citations, and thesis terms. These clicks are often research-only and may not match service or product intent.

• paper, papers
• thesis, dissertation
• survey, systematic review
• conference, proceedings
• citation, cited by, references
• dataset, benchmark dataset
• methodology, algorithm paper

Exclude generic “how it works” and pure tutorials

Some searches aim for basic explanations, not buying or hiring. Negative keywords can reduce traffic from users looking for definitions or beginner tutorials.

• how does machine vision work
• what is machine vision
• machine vision tutorial
• machine vision training
• course, classes, bootcamp
• video tutorial, youtube tutorial
• explain, explanation
• examples only

These exclusions can be optional. If content marketing and lead capture are aligned, some tutorial traffic can still be useful. Many machine vision service campaigns prefer stricter exclusions.

Exclude software-only queries when service is the offer

If the offering is inspection systems, deployments, or consulting (not generic software downloads), then software-only searches can be blocked. This helps focus on solution seekers.

• download
• free software
• open source
• license key
• crack, cracked
• APK
• install

For vendors that sell software, these negatives may be the wrong choice. In those cases, negatives should focus on unrelated topics and academic intent instead.

What to exclude related to cameras, sensors, and hardware

Exclude consumer camera and mobile app intent

Machine vision is often tied to industrial cameras. But “machine vision” searches can include consumer camera topics or mobile apps that do not match industrial systems.

• phone app
• camera app
• filters
• beauty camera
• AR app
• consumer camera

Exclude unrelated camera categories when not offered

Some hardware searches can be outside a campaign’s scope. If there is no focus on certain camera classes, those terms can be excluded.

• webcam
• security camera (if the campaign is not about surveillance)
• DVR, NVR (if not part of the offer)
• IP camera (for non-surveillance offers)
• dashcam (unless relevant)

These negatives depend on the business model. For inspection and robotics projects, excluding security-systems terms can improve relevance.

Exclude “toy” robotics and hobby hardware terms

Hobby robotics and maker intent can produce clicks with low conversion. When the target is industrial deployment, these terms are often poor fits.

• arduino
• raspberry pi (if not used in offerings)
• maker, hobby, hobbyist
• robot kit
• beginner robotics
• 3d printed camera mount

Application and industry negatives for machine vision

Exclude industries that are not served

Machine vision can serve many sectors such as automotive, electronics, food, pharma, packaging, and logistics. If certain industries are not supported, excluding them can reduce mismatched leads.

• real estate inspection (if not supported)
• retail surveillance (if not part of the service)
• social media face recognition (if not offered)
• traffic enforcement cameras (if not offered)
• medical imaging (if not part of the scope)
• remote sensing satellites (if not offered)

Industry negatives can be especially useful when “machine vision” is combined with a specific vertical keyword.

Exclude applications that are outside the service scope

Different inspection tasks may require different approaches. If only some use cases are offered, exclusions can limit irrelevant requests.

• OCR scanning for documents (if not offered)
• facial recognition (if not offered)
• object tracking for sports broadcasts (if not offered)
• barcode scanning (if not offered)
• text-to-image generation (if not offered)

Exclude “surveillance” and “tracking” intent when offer is inspection

Machine vision is sometimes requested for surveillance, tracking, or monitoring rather than quality inspection. If inspection and process control are the focus, surveillance intent can be excluded.

• surveillance
• monitoring system
• tracking people
• face detection for security
• intrusion detection

Exclude job titles and skill searches that do not convert

Exclude “skills” and tool-based searches

Tool terms can trigger searches from candidates or students. Some campaigns may prefer excluding tool-heavy intent if the offer is not training or hiring.

• PLC integration training
• computer vision certification
• image processing course
• machine learning for vision class
• TensorFlow tutorial
• PyTorch tutorial

If the service includes model development, some of these terms may be relevant. Negative keywords should align with what is actually sold.

Exclude common job titles

Job-title searches can be strong signals of career intent. These are usually better targeted by recruiting ads, not lead-gen campaigns.

• computer vision engineer
• machine vision engineer
• vision systems engineer
• image processing engineer
• quality vision engineer
• machine learning engineer (vision)

Exclude terms tied to “free,” “cheap,” and DIY when offers are premium or managed

Free and budget-only intent

Budget-only searches often look for downloads, templates, or low-cost options. These can be added as negatives when the offering is paid projects, managed services, or custom deployments.

• cheap
• discount
• free
• no cost
• affordable
• lowest price

DIY and template intent

DIY intent can overlap with tutorial intent, but some searches look for tools rather than help. Excluding DIY terms can reduce mismatched leads.

• template
• example code
• sample project
• github
• step by step build
• homemade
• self install

Negative keywords for “Google Ads” and ad management terms

Exclude irrelevant “ads” searches

If the campaign is for machine vision services, ad management terms can still trigger unrelated searches. These are often better excluded so clicks do not consume budget.

• machine vision ads
• PPC
• Google ads
• ad agency for machine vision
• keyword research for machine vision

Note: if an ad agency service is being sold, then these terms are not negatives. The list should match the business goal.

Exclude “ad extension” and internal platform questions when not relevant

Platform learning queries may attract people looking for how-to help, not machine vision solutions. Some teams may choose to exclude generic Google Ads help terms.

• ad extensions how to
• Google ads optimization
• account setup

For machine vision marketing teams, the optimization topic can be handled separately. If needed, relevant learning pages can connect to areas like: machine vision Google Ads optimization, and ad formats like: machine vision ad extensions.

Example negative keyword lists by campaign type

Example: machine vision services (inspection, integration, consulting)

These examples assume a services offer focused on industrial deployments.

• jobs, salary, internship
• paper, thesis, survey
• tutorial, training course, certification
• download, free software, open source
• phone app, consumer camera
• surveillance, face recognition for security
• cheap, discount
• template, github, sample project
• webcam, IP camera, DVR/NVR (if not offered)

Example: machine vision software product vendor

These examples assume a software download or licensing offer.

• jobs, salary, internship
• thesis, paper, dataset benchmark (unless selling research tools)
• security camera DVR/NVR (if unrelated)
• dashcam, webcam (if unsupported)
• resume, CV, cover letter

For software vendors, negatives should focus more on unrelated industries and academic intent. Blocking “download” may be counterproductive if downloads are part of the funnel.

Example: machine vision training provider

If the offer is training or certification, then training-related terms should be treated differently. Negatives should focus on unrelated purchase intent.

• consulting quote, integration cost (if not offered)
• service near me (if not offered)
• system installation (if not offered)
• inspection deployment (if not offered)

Common mistakes when choosing machine vision negative keywords

Blocking terms that are too close to the offer

Some exclusions can block relevant searches. For example, “training” could be useful if the offer includes onboarding. Testing and review of search terms helps reduce these risks.

Using negatives too broad too early

When a negative is added too broadly, it can prevent ads from showing for valid needs. Staged additions and match-type care can help keep results stable.

Not updating negatives after campaign changes

Search behavior can shift after ads and landing pages change. Review search term reports on a regular schedule and update negatives as new irrelevant queries appear.

Skipping negative keywords for local and location intent

Location phrases can bring high intent, but they can also bring irrelevant areas if coverage is limited. If service regions are restricted, location-based negatives may help, but only after careful review.

Process for ongoing review of machine vision negative keywords

Review search terms on a set cadence

A regular check helps catch new irrelevant queries. The cadence can be monthly or after major campaign changes.

Decide whether to exclude or refine keywords

Not every mismatch needs a negative. Some cases can be fixed by refining keyword targeting, ad copy, or landing page alignment.

Keep a changelog of negative updates

A simple list of when negatives were added and why can reduce confusion. This also helps align optimization work across teams.

Checklist: machine vision negative keywords to exclude (starter list)

• jobs, hiring, recruiter, salary
• resume, CV, cover letter
• paper, thesis, dissertation, survey, references
• tutorial, course, certification, training course
• download, free software, open source (if not part of offer)
• phone app, consumer camera, webcam
• surveillance, tracking people, face recognition for security
• cheap, discount, lowest price
• template, sample project, github, example code
• security camera DVR/NVR (if not offered)

Next steps for improving machine vision lead quality

Pair negatives with strong landing page fit

Negative keywords reduce irrelevant traffic, but landing page relevance still matters. Align the landing page with the promised solution, such as inspection type, deployment scope, and industry fit.

Use machine vision quality score guidance to support relevance

Some teams connect ad relevance and landing page experience to their machine vision quality score work. Tracking these signals can help keep traffic quality from search term tuning.

Learn more about machine vision quality score.

Keep optimization connected to ad formats and testing

Ad extensions and optimization can change which queries perform. If expanding coverage, negatives may need updates to protect relevance.

Machine vision ad extensions and machine vision Google Ads optimization can support that ongoing work.