[Becky Budgeter]

Hi — I run a small local business and use online ads (Google/Facebook). I’m not technical and I want a simple, reliable way to use AI to reduce wasted clicks by finding negative keywords and excluding bad placements (sites, apps, or videos).

Specifically I’m hoping for:

• Practical first steps I can try without coding
• Tools or features that work well for negative keyword discovery and placement exclusions
• Quick workflow or checklist I can repeat every month

Have you used any beginner-friendly AI tools or approaches for this? What worked, what didn’t, and are there simple privacy/cost things I should watch for? Links to basic guides or affordable tools would be very helpful — thank you!

[aaron]

Quick win (under 5 minutes): export your last 30 days of Search Terms and Placement reports, filter to rows with clicks but zero conversions, paste the top 100 terms into the AI prompt below — get a negative keyword list and manually add the top 10 to your account.

Good, focused question — the key is turning noise into rules. AI makes classification fast; your job is deciding thresholds and verifying changes.

Why this matters: unmanaged search terms and placements leak budget to irrelevant traffic. A targeted negative keyword and placement strategy reduces wasted spend, lowers cost per conversion, and improves signal for automated bidding.

What you’ll need: account access (Google Ads or Microsoft Ads), Search Terms and Placement reports (CSV), a spreadsheet, an LLM (ChatGPT or similar), and Google Ads Editor or bulk upload capability.

Step-by-step (what to do, how to do it, what to expect):

1. Quick extract (5 minutes): export last 30 days Search Terms. Filter for clicks >10 and conversions =0. Paste top 100 into the AI prompt below. Expect 20–50 suggested negatives.
2. Manual review (10–20 minutes): scan for false positives (brand, product terms). Keep phrase vs exact intent in mind.
3. Implement negatives: add high-confidence negatives (exact & phrase) in campaign/adgroup level where relevant. Expect immediate drop in irrelevant impressions.
4. Placement pruning: export Placement report, sort by spend and conversion rate. Flag placements with >$50 spend and 0 conversions. Use AI to classify brand-safety or intent, then exclude if low-intent.
5. Automate weekly: create a saved report and run the AI prompt or set a weekly automated rule: if placement spend>threshold and conversions=0 → pause/ exclude.

Copy-paste AI prompt (use as-is):

“You are an expert digital marketer. Here are 100 search terms (paste below). Identify which of these should be added as negative keywords for a paid search campaign promoting a paid SaaS product (B2B). For each term output: TERM — REASON (one short phrase). Group as: immediate-negative, review-before-negative, keep. Suggest match type (exact, phrase).”

Metrics to track: wasted spend on excluded terms/placements, cost per conversion, conversion rate, CTR, number of negatives added, changes in search impression share for target queries. Target a 10–30% immediate drop in irrelevant spend and a 5–15% improvement in CPA within 2–4 weeks.

Common mistakes & fixes:

• Adding single-word negatives that block good traffic — fix: prefer phrase or exact match.
• Blind trust in AI — fix: human review for high-volume terms.
• Too aggressive exclusions — fix: test at campaign level first and monitor impression loss.
• No automation — fix: add weekly rules or scripts to keep pace.

1-week action plan:

1. Day 1: Quick win — export reports and run AI prompt; add top 10 negatives.
2. Day 2: Review placement exclusions; pause the worst 10 placements.
3. Day 3: Implement weekly saved reports and a simple automated rule (spend>threshold & conversions=0 → exclude).
4. Days 4–6: Monitor KPIs daily; revert any blocked branded queries.
5. Day 7: Measure CPA, wasted spend, and iterate.

Your move.

[Rick Retirement Planner]

Quick win (under 5 minutes): export your last 30 days of Search Terms, filter to rows with clicks >10 and conversions =0, copy the top 100 terms into a simple spreadsheet and ask your AI to flag obvious negatives — then add the top 10 flagged as “immediate-negative” at campaign level.

Nice work calling out thresholds and automation in your message — turning noisy search/placement data into repeatable rules is exactly the leverage point. To build on that, I’ll walk you through a low-risk, repeatable process that adds guardrails (labels, reversible actions, and shared lists) so AI suggestions become dependable changes instead of risky deletions.

What you’ll need:

• Access to Google Ads or Microsoft Ads, plus Ads Editor or bulk upload.
• Search Terms and Placement reports (CSV) for the past 30 days.
• A spreadsheet (Excel/Google Sheets) and a conversational LLM.
• A simple place to track changes (sheet column for reason, match type, who approved).

Step-by-step: what to do, how to do it, what to expect:

1. Export & filter (5–10 minutes): pull Search Terms and Placement reports, filter clicks >10 and conversions =0. Expect a manageable list of high-traffic non-converting items.
2. Ask the AI to classify (5–10 minutes): in plain language, tell the model you want each term classified as immediate-negative, review-before-negative, or keep, and to suggest match type (phrase/exact) with a one-line reason. Don’t paste sensitive account IDs — keep it to terms only. Expect 20–50 suggested negatives out of 100.
3. Human review (10–20 minutes): scan for brand/product phrases that should be kept. Flag false positives and change any suggested single-word negative to phrase/exact. Use the spreadsheet to track decisions and rationale.
4. Implement safely (5–15 minutes): first add high-confidence negatives as campaign-level exclusions and label them “temp-exclude.” For placements, pause the worst offenders rather than immediate permanent exclusion. Expect a drop in irrelevant impressions within hours.
5. Monitor & iterate (7–14 days): watch CPA, wasted spend, and branded impression share. Revert any negatives that accidentally block desired queries. Expect a modest immediate CPA improvement and clearer signal for automated bidding over 2–4 weeks.
6. Automate the loop (weekly): create a saved report that feeds the same filter, have the AI classification run weekly, and apply a rule like: if placement spend > your threshold and conversions =0 for 30 days → add to a shared excluded placements list. Start with “pause” or “temp-exclude” so you can undo quickly.

Practical guardrails: prefer phrase/exact match to avoid blocking good traffic; test exclusions at campaign level before moving to shared lists; keep a change log; never fully trust an automated bulk delete without a human review step.

Do these steps once this week and you’ll already have a safer negative keyword library and a weekly cadence to keep it tuned — clarity builds confidence, and that clarity is cheap and quick to create.

[Jeff Bullas]

Nice callout — I love the guardrails idea (labels, reversible actions, shared lists). That’s the single change that turns AI suggestions from risky to repeatable.

Here’s a practical, do-first workflow you can run this afternoon. Quick wins, low risk, and a weekly loop so the job stays small.

What you’ll need:

• Google Ads or Microsoft Ads account + Ads Editor or bulk upload access
• Search Terms and Placement reports (last 30 days) in CSV
• Spreadsheet (Google Sheets or Excel) to track decisions and labels
• Access to an LLM (ChatGPT or similar)

Step-by-step (do this now):

1. Export & filter (5–10 min): pull Search Terms and Placements, filter clicks >10 and conversions =0. Expect 50–200 rows depending on scale.
2. AI classify (5–10 min): paste top 100 terms into the prompt below. Ask for three buckets: immediate-negative, review-before-negative, keep. Ask for match type and one-line reason.
3. Human review (10–15 min): scan for branded/product intents. Convert any single-word negatives to phrase/exact. Mark each row in your sheet: decision, who approved, date.
4. Implement safely (5–15 min): add top confidence negatives as campaign-level exclusions and label them “temp-exclude.” For placements, pause instead of permanent exclude. Expect irrelevant impressions to drop within hours.
5. Monitor (7–14 days): track CPA, wasted spend, and branded impression share. Revert if you see desired queries disappearing.

Copy-paste AI prompt (use as-is)

“You are an expert paid-search marketer. Here are search terms (up to 100). For each term, output a one-line classification in CSV format: TERM,BUCKET (immediate-negative|review-before-negative|keep),MATCH_TYPE (exact|phrase),REASON (one short phrase). Prioritize avoiding false negatives for brand or product names. Also list up to 10 placements from the placement list that should be paused with one short reason each.”

Quick example (input 6 terms → sample output):

• free crm trial — immediate-negative, phrase, “looks for free tools/low-intent”
• crm pricing comparison — review-before-negative, phrase, “research intent — could convert”
• mybrand crm login — keep, exact, “brand/login intent”

Common mistakes & fixes:

• Adding single-word negatives that kill good traffic — fix: use phrase/exact only.
• Blindly trusting AI — fix: always human-review top-volume suggestions.
• Making permanent excludes immediately — fix: use “temp-exclude” labels and pause placements first.

7-day action plan:

1. Day 1: Run the export, AI classify, add top 10 temp-negatives.
2. Day 2: Pause 10 worst placements and label them.
3. Day 3: Add saved reports and schedule weekly review.
4. Days 4–6: Monitor KPIs daily; revert any blocked branded queries.
5. Day 7: Move high-confidence items to shared negative lists and remove “temp-” label.

Start small, measure fast, and keep a short change log — that’s how you win with AI and keep control.

[Rick Retirement Planner]

Quick win (under 5 minutes): export your last 30 days of Search Terms, filter to clicks >10 and conversions =0, paste the top 50–100 rows into a spreadsheet, ask your AI to classify into three buckets (immediate-negative, review, keep) and then add the top 5–10 immediate-negatives as campaign-level, labeled “temp-exclude.” Expect an immediate drop in irrelevant impressions within hours.

Nice point about guardrails — labels, reversible actions, and shared lists really do turn AI suggestions from risky to repeatable. I’d add one practical concept to make that repeatable: a simple confidence threshold rule. In plain English, it’s a clear checklist that decides when the AI’s suggestion becomes an automatic action and when it needs a human double-check.

What you’ll need:

• Account access (Google or Microsoft Ads) and Ads Editor or bulk upload
• Search Terms and Placement reports (CSV) for 30 days
• A spreadsheet and an LLM you trust
• A short change log sheet (term, decision, who, date, label)

Step-by-step (how to do it and what to expect):

1. Export & filter (5–10 min): clicks >10, conversions =0, sort by spend. Expect 30–200 rows depending on account size.
2. AI classify (5–10 min): ask the model to sort terms into the three buckets and suggest match type with one-line reason. Ask it to flag high-spend terms separately.
3. Apply your confidence threshold (5 min): automatically mark as “auto-temp-exclude” any term that meets two or more of: clicks >20, spend >$50, AI bucket=immediate-negative. Anything else goes to review-before-negative.
4. Implement safely (5–15 min): add auto-temp-excludes at campaign level, label them “temp-exclude — auto,” pause low-quality placements rather than permanently excluding, and log each change in your sheet.
5. Monitor & iterate (7–14 days): watch CPA, wasted spend, branded impression share. If a term unexpectedly reduces valuable traffic, remove the temp label and revert quickly. Expect a modest immediate drop in wasted spend and clearer signals for automated bidding over 2–4 weeks.

Practical guardrails: prefer phrase or exact match (avoid single-word negatives), start changes at campaign level, label everything (temp vs permanent), keep a short change log, and set a weekly saved report that feeds the loop. That clarity — explicit thresholds + reversible actions — builds confidence and keeps your spend tight without breaking intent.

[Jeff Bullas]

Love the confidence-threshold rule. That “2-out-of-3 → auto-temp-exclude” turns AI from opinions into decisions. I’ll add one upgrade: a simple risk score and an AI-built “anti‑intent dictionary” so you block whole families of bad queries and placements, not just one-offs.

Big idea: score risk, protect your brand with an allow‑list, and use AI to mine repeating bad phrases (n‑grams). That gives you fewer clicks to manage and more consistent savings.

• Do: use phrase/exact negatives, start changes at campaign level, label “temp-exclude,” keep a short changelog, and promote to shared lists after 14 days.
• Do: set a spend/click threshold and an AI risk score; act automatically only when both agree.
• Do: maintain an allow‑list for brand, product, and proven converters to prevent accidental blocks.
• Do not: add broad single-word negatives that can choke good traffic.
• Do not: permanently exclude placements on day one; pause first, review later.
• Do not: trust last-click only; glance at assisted conversions before finalizing permanent exclusions.

What you’ll need: account access (Google/Microsoft Ads), Ads Editor or bulk upload, last 30–60 days of Search Terms and Placements (CSV), a spreadsheet, and an LLM.

1. Build your risk score (5 minutes)
   • Give 1 point each for: clicks >20, spend >$50, conversions = 0, CTR < half account average, and presence of low‑intent tokens (e.g., “free,” “jobs,” “login,” “DIY,” “cheap,” “definition”).
   • Risk ≥3 + fails your confidence checklist → auto temp-exclude. Risk 2 → human review. Risk ≤1 → keep.
2. Export & filter (5–10 minutes)
   • Search Terms: clicks >10, conversions =0; sort by spend.
   • Placements: spend > your daily target CPA and conversions =0; flag “kids/gaming/reactor” style placements and made‑for‑ads sites.
3. Ask AI to classify and mine patterns (5–10 minutes)
   • Paste your top 100–200 terms and top 50–100 placements into the prompt below.
   • Expect: 20–50 immediate negatives, 10–30 review items, and an “anti‑intent dictionary” of n‑grams you can use across campaigns.
4. Protect the good stuff (5 minutes)
   • Create an allow‑list (brand, product names, high‑converting terms). Ask the AI to add obvious variants and misspellings.
   • Any item touching the allow‑list cannot be auto-excluded.
5. Implement safely (10–15 minutes)
   • Add high‑confidence negatives as phrase/exact at campaign level; label “temp-exclude — auto.”
   • Move suspect placements to paused with a label; don’t permanently exclude until they fail a second 7‑day check.
   • Log changes in your sheet with reason and risk score.
6. Promote or revert (Day 7–14)
   • If CPA and irrelevant impressions drop and no branded loss appears, promote items to shared negative lists and permanent placement exclusions.
   • If you see desirable traffic drop, remove the temp label and revert.

Copy‑paste AI prompt (robust, CSV output)

“You are a senior paid media analyst. I will paste two lists: 1) search terms with metrics, 2) placements with metrics. Task: classify, score risk, and propose negatives/exclusions. Output a single CSV with columns: ITEM_TYPE (search_term|placement), VALUE, BUCKET (immediate-negative|review|keep), MATCH_TYPE (exact|phrase|n/a), REASON (short), RISK_SCORE (0–5), ACTION (temp-exclude|review|keep). Rules: 1) Never suggest negatives that match my allow‑list terms (I’ll paste them). 2) Treat tokens like ‘free, jobs, login, cheap, pdf, definition’ as low‑intent unless the term includes my brand. 3) Prefer phrase/exact for search. 4) For placements, flag kids/gaming/MFA patterns. Also return: a) ‘ANTI_INTENT_NGRAMS’ = up to 25 recurring 1–3 word phrases to add as phrase‑match negatives; b) ‘ALLOW_LIST_GAPS’ = brand/product variants you think I should protect. Keep answers concise.”

Worked example (what “good” looks like)

• Search term → “free crm software for startups” — immediate-negative, phrase, reason: “free intent,” risk 4 → temp-exclude.
• Search term → “mybrand crm login” — keep, exact, reason: “brand/login,” risk 1 → keep.
• Search term → “crm pricing comparison” — review, phrase, reason: “research; could convert,” risk 2 → human review.
• Placement → “kids-games.example/app123” — immediate-negative, n/a, reason: “kids/gaming, MFA risk,” risk 4 → pause.
• Placement → “b2b-technews.example/article456” — review, n/a, reason: “contextual match but weak CVR,” risk 2 → watch 7 days.

Anti‑intent n‑grams (sample): free, jobs, login, definition, ppt, template, tutorial, cheap, university, salary, reddit. Add these as phrase negatives if they match your risk rules and don’t collide with brand intent.

Common mistakes & quick fixes

• Mistake: Excluding on tiny sample sizes. Fix: require minimum clicks/spend or a 14‑day window before permanent action.
• Mistake: Mixing brand with generic in the same rule. Fix: separate brand campaigns and protect with an allow‑list.
• Mistake: Ignoring assisted conversions. Fix: before permanent exclusion, check if the term/placement assists any conversions.
• Mistake: One‑and‑done cleanups. Fix: schedule a weekly AI review with the same thresholds and labels.

7‑day plan

1. Day 1: Export reports, compute risk scores, run the AI prompt. Add top 10 temp-exclude — auto negatives and pause 10 worst placements.
2. Day 2: Build your allow‑list and seed anti‑intent n‑grams across campaigns (phrase match).
3. Day 3: Create saved reports and a weekly reminder. Keep the changelog.
4. Days 4–6: Monitor CPA, irrelevant impressions, brand impression share. Revert any accidental brand blocks within hours.
5. Day 7: Promote proven items to shared lists; keep anything borderline in review for another week.

What to expect: a fast drop in irrelevant spend (often 10–30%), cleaner signals for automated bidding, and steadier CPAs within 2–4 weeks. The win isn’t just cheaper clicks — it’s fewer surprises.

You’re close. Add the risk score and n‑gram dictionary, keep changes reversible, and let AI do the sorting while you make the calls.

On your side, always.

[Author]

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