16 senior-level prompts. Paste, fill in, run.

Variables (2)

• {{account_summary}}
  High-level facts about the account: vertical, monthly spend, number of campaigns, conversion model.

  B2B industrial laser markers, ~$45K/mo spend, 12 campaigns, offline conversions imported via OCT.
• {{observations}}
  Raw observations from the account — what you see in the UI. CTRs, CPLs, conversion rates, search-term notes, structural facts.

  CTR 4.2% account avg, CPL $312, 18% search-term contamination, broad match in 6 of 12 campaigns, no negative-keyword list shared across campaigns…

You are a senior B2B paid media auditor. Your job is to diagnose this account ruthlessly and propose fixes — not validate the current setup.

ACCOUNT SUMMARY:
{{account_summary}}

RAW OBSERVATIONS:
{{observations}}

Produce a structured audit covering, in this exact order:
1. Account structure (campaigns, ad groups, naming conventions)
2. Match types & keyword strategy
3. Negative keyword discipline
4. Bidding strategy & conversion model
5. Ad copy & extension coverage
6. Landing page alignment
7. Reporting & attribution

For each section:
- Score 0–10
- List specific issues, ranked by severity (critical / high / medium / low)
- For each issue, propose a concrete fix
- Estimate expected impact (e.g. "10% CPL reduction within 30 days")

End with a Top 3 prioritized action list — what to do first, this week.

Do NOT pad the response. Skip sections that genuinely have no issues. If a section has insufficient data, say so explicitly rather than guessing.

Expected output shape

A 7-section audit with numeric scores, severity-ranked issues, concrete fixes, and a Top 3 action list at the end. Roughly 800–1500 words.

Variables (2)

• {{campaign_context}}
  Campaign vertical, age, conversion volume, current bid strategy, target.

  B2B industrial Shopping campaign, 8 months old, ~120 conversions/30 days, tROAS 400% target, currently hitting 280%.
• {{symptoms}}
  What you are seeing — CPC trends, impression share, conversion rate shifts, learning-period status.

  CPC up 35% over 60 days, impression share dropped from 45% to 18%, conversion rate flat, no recent learning-period restart.

You are a Smart Bidding diagnostician. Walk through the most likely root causes for the symptoms below, ranked by probability.

CAMPAIGN CONTEXT:
{{campaign_context}}

SYMPTOMS:
{{symptoms}}

For each candidate root cause:
- Name the cause
- Explain the signal Smart Bidding is reading
- Explain why this would produce these symptoms
- Propose a diagnostic step (what to check)
- Propose a fix (what to change)

Rank candidates by probability (most likely first). Stop at 5 candidates — do not pad.

End with a one-paragraph summary: "If I had one shot to fix this, I would…"

Do not recommend "let it learn" as a primary fix unless the diagnostic step actually shows recent learning-period activity.

Expected output shape

Up to 5 ranked root causes with diagnostic + fix per cause, plus a "one-shot fix" summary paragraph.

Variables (2)

• {{mcc_summary}}
  Number of accounts, total spend, vertical mix, agency size.

  47 accounts under one MCC, $1.8M/mo total, mix of B2B SaaS + industrial + B2B services, 6-person agency team.
• {{structure_dump}}
  A representative campaign-level export across 3–5 accounts (campaign name, type, status, daily budget).

  See attached CSV; sample includes 3 industrial accounts and 2 SaaS accounts.

You are reviewing the structural health of an MCC portfolio for an experienced agency lead. Your audience is sophisticated; do not explain basics.

PORTFOLIO SUMMARY:
{{mcc_summary}}

STRUCTURE DUMP:
{{structure_dump}}

Identify:
1. Structural inconsistencies between accounts of similar type (where one account does X, another doesn't, both should)
2. Naming convention drift
3. Campaign-type misuse (e.g. PMax used where Search would be cleaner; Display where in-market would be cleaner)
4. Cross-account learning that isn't happening but could (one account's negative list could help others)
5. Risk concentrations (any one account or campaign too dependent on a single signal)

Output a prioritized punch list with concrete agency-level work items, not per-account changes. Each item: 2–3 sentences max, ending with the specific action.

Do NOT recommend "consolidate accounts" or "deconsolidate accounts" unless the data clearly supports it. Stay structural.

Expected output shape

A prioritized agency-level punch list with concrete work items. Typically 10–20 items.

Variables (2)

• {{industry_context}}
  Vertical and target persona — needed to distinguish B2B intent from B2C contamination.

  B2B industrial fluid power, target persona is plant maintenance engineer at $20M+ manufacturers.
• {{search_terms}}
  Search-term report rows: term, impressions, clicks, cost, conversions. Tab-separated or CSV.

  See attached CSV — 3,200 rows, 90-day window, ~$28K spend.

You are reviewing a 90-day search-term report for a B2B account. Your job is to produce two outputs: a comprehensive negative keyword list, and a list of expansion candidates worth promoting to keywords.

INDUSTRY CONTEXT:
{{industry_context}}

SEARCH TERMS (term, impressions, clicks, cost, conversions):
{{search_terms}}

NEGATIVE KEYWORD LIST:
Group negatives by reason. Standard buckets:
- B2C contamination (DIY, hobby, retail signals)
- Wrong-product (we don't sell this)
- Competitor terms (decide per-account whether to include)
- Job seeker / informational (jobs, careers, what is)
- Geographic mismatch
- Pricing intent we don't serve (cheap, free, sample-only)
- Unclear / low-volume (recommend monitoring, not adding)

For each negative, suggest match type (negative exact / negative phrase / negative broad). Default to negative phrase unless exact is needed.

EXPANSION CANDIDATES:
List terms with conversion rate above the account average, ranked by total conversions. For each: suggest match type for promotion, suggest the campaign / ad group it should go in.

Do not invent terms not present in the data. Do not recommend "let it run longer" — the window is already 90 days.

Expected output shape

A categorized negative-keyword list and an expansion-candidate list, both with match-type recommendations. Typically 50–200 negatives + 10–30 expansion candidates.

Variables (2)

• {{seed_keyword}}
  The keyword to expand from.

  industrial laser marker
• {{industry_context}}
  Vertical, target buyer, common materials / applications / specs.

  Industrial part marking. Buyers: manufacturing engineers, quality leads. Materials: stainless 316L, anodized aluminum, plastic. Applications: serial numbers, 2D matrix codes, traceability.

Generate 100 long-tail keyword variants from the seed below. Group by funnel stage so they can be routed to ad groups directly.

SEED KEYWORD:
{{seed_keyword}}

INDUSTRY CONTEXT:
{{industry_context}}

GENERATE:
- 30 TOFU variants (informational / problem-aware): "what is X", "how to X", "X vs Y", "best practices for X"
- 35 MOFU variants (solution-aware, comparing): "X for [application]", "X for [material]", "best X for [use case]", "compare X options"
- 35 BOFU variants (vendor-aware, ready to buy): include part-number patterns, dimensional specs, brand prefixes, "buy X", "X price", "X for sale [region]"

For each variant, include match type recommendation (exact / phrase). Default BOFU to exact, MOFU to phrase, TOFU to phrase or broad with tight negatives.

Do NOT generate consumer variants ("for home", "for kids", "DIY") — this is a B2B account.
Do NOT pad to 100 if the seed doesn't support it — return fewer high-quality variants instead of 100 weak ones.

Expected output shape

Up to 100 keyword variants in 3 funnel-stage buckets, each with a match-type recommendation.

Variables (3)

• {{job_title}}
  Specific job title at the buyer company.

  Plant Maintenance Manager
• {{industry}}
  Industry of the buyer company.

  Food & beverage manufacturing, $50M–$500M revenue, 200–1500 employees.
• {{product}}
  What the persona is ultimately searching for.

  Industrial laser markers for traceability on packaging lines.

Generate the actual queries a {{job_title}} at a {{industry}} company types into Google when researching {{product}}. Stay in their voice, not yours.

Output 50 queries, organized:
1. Pain-point queries (10) — they are searching for a problem, not a solution. Words like "why does my…", "issue with…", "won't…"
2. Internal-language queries (10) — they use the technical / operational vocabulary of their own job. Specific machines, processes, regulations, internal acronyms where relevant.
3. Vendor-research queries (10) — they are evaluating vendors. Brand names, comparisons, "vs", "versus", "alternatives to".
4. Specification-driven queries (10) — they have a specific spec they need. Materials, dimensions, throughput, certifications.
5. Procurement-driven queries (10) — they are about to buy. Pricing, lead time, RFQ, quote, sample, trial.

Do NOT use marketing voice ("revolutionize", "transform", "unlock"). Stay in the buyer's voice.
Each query should be plausible — what would actually appear in a search log if you watched this person work for a week.

Expected output shape

50 queries in 5 stages of buyer voice, no marketing fluff.

Variables (1)

• {{industry_context}}
  Industry, target persona, what you do NOT sell.

  Industrial fluid power. We sell hydraulic pumps for OEMs and large maintenance contracts. We do NOT sell: hobby/auto/home/jewelry/medical/aerospace/defense.

You are building a negative keyword list from scratch for a new B2B account. The account has not run yet, so you need to anticipate contamination patterns proactively.

INDUSTRY CONTEXT:
{{industry_context}}

Build a comprehensive negative keyword list organized into these buckets:

1. Wrong vertical (we don't sell this)
2. Wrong customer type (B2C, hobbyist, DIY, school, etc.)
3. Job seekers (jobs, careers, salary)
4. Informational only (what is, how to, definition, wikipedia)
5. Geographic exclusions (countries / regions you don't serve)
6. Pricing intent we don't serve (cheap, free, sample, trial)
7. Adjacent products that look similar but aren't ours
8. Pure brand-protection (competitor brands you do not want to bid on, even broadly)

For each negative, suggest match type (negative exact / phrase / broad). Aim for 100–200 negatives total. Quality over quantity — every negative should have a clear bucket.

Do NOT include obvious universal negatives like "free porn", "free download" — assume those are at the account level already.

Expected output shape

A bucketed negative-keyword list of 100–200 entries with match-type per term.

Variables (2)

• {{our_product}}
  What you sell, the persona, key differentiators.

  Industrial laser markers built for line-rate traceability. Persona: manufacturing engineer at $50M+ industrial OEM. Differentiators: 24/7 service contract, US-built, integrated I/O.
• {{competitor_ads}}
  Three competitor ads, fully — headlines and descriptions verbatim.

  Competitor A: "Best laser markers — Save up to 30%! Free shipping. 30-day trial." Competitor B: …

You are writing counter-messaging RSAs for a B2B account. Your job is to position against the three competitors below without naming them.

OUR PRODUCT:
{{our_product}}

COMPETITORS:
{{competitor_ads}}

For each competitor, identify their pitch (the angle they're claiming). Then write counter-messaging that:
- Concedes nothing
- Stays in B2B vocabulary (no "save more", no "limited time", no consumer hooks)
- Highlights a specific structural strength of our product that contradicts their angle
- Does NOT name them or quote them

Output:
- 5 distinct headline angles (≤30 chars each)
- Generate 3 headline variants per angle (15 total)
- 4 description variants (≤90 chars each)
- For each headline and description, note the implied competitor angle being countered

Then write a one-paragraph creative brief for the account team explaining the positioning logic.

Do NOT use words like "leading", "premier", "innovative" — they trigger nothing in B2B procurement. Write in the language of the buyer, not the brand.

Expected output shape

15 headlines + 4 descriptions, each tagged with the competitor angle countered, plus a brief.

Variables (2)

• {{pmax_assets}}
  All assets in the asset group: headlines, long headlines, descriptions, image descriptions, video descriptions.

  Headlines: "Save 20% Today!", "Best industrial markers", "Free shipping", … (paste all)
• {{product_context}}
  What you are selling and to whom — needed to flag B2C contamination.

  Industrial laser markers, B2B procurement audience, average deal $40K, sales cycle 3 months.

Critique the PMax asset group below for a B2B account. Be brutal — PMax assets are typically over-padded with consumer-tone headlines that pull the algorithm toward the wrong audience.

ASSETS:
{{pmax_assets}}

PRODUCT CONTEXT:
{{product_context}}

Score and report on:

1. B2C tone violations (any "save", "today", "free shipping", "limited time" type language). List each violation, why it's a problem in B2B, and a B2B replacement.
2. CTA strength. Are CTAs vague ("Learn more"), procurement-aware ("Request a quote"), or specific ("Book a 30-min spec review")?
3. Asset diversity. Are headlines too similar? Are descriptions all the same length? Is there a mix of feature, benefit, and proof?
4. Persona alignment. Does the language sound like an engineer's voice or a marketing manager's?
5. Missing assets. What's not there that should be? (Specifications? Certifications? Lead-time? Pricing-tier signal?)

Output: numbered findings, each ≤3 sentences, ending with a concrete fix. End with a Top 5 priority list.

Do NOT recommend "test more variants" without specifying which variants and why.

Expected output shape

A numbered findings list with concrete fixes, plus a Top 5 priority list.

Variables (3)

• {{product_brief}}
  Product, key features, differentiators, certifications, supporting proof.

  Industrial laser markers. Features: 100W fiber, integrated I/O, 24/7 service contract. Differentiators: US-built, ISO 9001, 4-week lead time. Proof: 2,400+ deployments, top OEMs.
• {{persona}}
  The buyer persona — title, pain points, internal vocabulary.

  Manufacturing engineer. Cares about: throughput, downtime, traceability compliance. Hates: vendor lock-in, long lead times, generic SaaS demos.
• {{campaign_intent}}
  What the campaign is supposed to do — the commercial outcome.

  Drive RFQ submissions from manufacturing engineers at OEMs evaluating new laser systems for line traceability.

Write Google Ads RSA copy for a B2B account. Stay in the persona's voice. Avoid marketing fluff.

PRODUCT:
{{product_brief}}

PERSONA:
{{persona}}

CAMPAIGN INTENT:
{{campaign_intent}}

OUTPUT:

15 headlines (≤30 chars). Mix of:
- Feature-led (3): name a specific feature with a number where possible
- Benefit-led (3): connect to a buyer pain point
- Proof-led (3): cert, deployments, lineage
- Process-led (3): lead-time, RFQ, spec-review hook
- Brand+vertical-led (3): "[Brand] for [vertical]" framing

4 descriptions (≤90 chars). Mix of:
- One feature-stack
- One outcome-led
- One proof-led
- One process / RFQ hook

Each output: write the asset, then a 1-sentence note on the angle and which character of the persona it speaks to.

Do NOT use: "leading", "premier", "innovative", "revolutionary", "cutting-edge", "best", "trusted", "industry-standard". Strike them on sight.

Expected output shape

15 headlines + 4 descriptions, each with a 1-sentence angle note.

Variables (2)

• {{portfolio_data}}
  Per-account: name, monthly budget, MTD spend, CPL, conversion volume, ROAS or close-rate.

  See attached CSV — 12 accounts, total $145K/mo budget.
• {{constraints}}
  Hard constraints — total budget cap, min spend per account, accounts that cannot be cut.

  Total budget cannot increase. Account "Industrial-A" must keep $25K/mo for relationship reasons. No account below $5K/mo.

Propose a budget reallocation across the portfolio below. Optimize for total return (revenue per ad dollar) within the stated constraints.

PORTFOLIO:
{{portfolio_data}}

CONSTRAINTS:
{{constraints}}

OUTPUT:

1. A reallocation table: per-account, current budget → proposed budget → delta (in $ and %).
2. Per-account justification (2–3 sentences each): why are you cutting / increasing / holding this account?
3. A top-line summary: total expected change in conversions / revenue if proposed allocation runs for 30 days.
4. Risk callouts: which accounts in the proposal have weak data (low conversion volume, recent strategy change, etc.) and need a smaller move.

Do NOT cut any account by more than 30% in one move unless the data is overwhelming.
Do NOT increase any account by more than 50% in one move — Smart Bidding can't absorb that without going off the rails.
Stay within constraints exactly. If constraints make optimal allocation impossible, flag it explicitly.

Expected output shape

A reallocation table, per-account justifications, top-line projection, and risk callouts.

Variables (2)

• {{current_names}}
  A representative sample of current campaign / ad-group names in the account.

  Campaigns: "Brand", "NB Search - Generic", "PMax Industrial 2024", "Search - Wide Match Test", … (paste 30+ rows)
• {{context}}
  Account complexity — how many regions, brands, channels, languages, levels of segmentation.

  1 brand, 3 regions (US, CA, UK), 4 product lines, search + shopping + pmax channels, English + French.

Propose a naming convention for the account below. Audience: an experienced agency team that will roll this out across 30+ accounts.

CURRENT NAMES (representative sample):
{{current_names}}

CONTEXT:
{{context}}

OUTPUT:

1. The proposed convention: a single template, e.g. "BRAND_REGION_CHANNEL_OBJECTIVE_AUDIENCE".
2. Token definitions: for each token, list the allowed values (with abbreviations).
3. The regex that validates the convention.
4. Migration plan: how to rename existing campaigns without breaking history. Specifically, what to copy vs rename in place, what to leave alone.
5. Edge cases: campaigns / ad groups that don't fit the template — call them out and propose extensions.

Constraints:
- Convention must be ≤80 characters total (Google Ads field limit).
- Tokens must be unambiguous (a token can be parsed back without lookups).
- Convention must work for both English and French campaigns.

Do NOT use spaces — use underscores. Do NOT use special characters — only [A-Z0-9_].

Expected output shape

Template + token definitions + regex + migration plan + edge cases.

Variables (1)

• {{account_context}}
  Vertical, monthly spend, conversion volume, current bid strategy, current match-type mix.

  B2B industrial fluid power, $30K/mo, 80 conversions/30 days, tCPA bidding, currently 60% broad, 30% phrase, 10% exact.

Recommend a match-type framework for the account below. Default to skeptical of broad match in B2B; default to skeptical of overly-restrictive exact-only in low-volume verticals.

ACCOUNT CONTEXT:
{{account_context}}

OUTPUT:

1. Recommended match-type mix (% across exact / phrase / broad) for this vertical and volume profile.
2. Reasoning — why this mix, not another. Tied to: conversion volume, Smart Bidding signal needs, contamination risk.
3. Implementation plan: how to migrate from current state to recommended state without disrupting Smart Bidding learning. Sequence and timing.
4. Negative-keyword discipline this implies (broad needs aggressive negatives; exact needs almost none).
5. The exit signal: what would tell you this framework isn't working and you need to adjust?

Do NOT recommend "all exact" without examining whether conversion volume can support it. Smart Bidding needs ≥30 conversions/30 days at the campaign level to optimize meaningfully — if exact alone won't deliver that, mixing is structurally required.

Do NOT recommend "all broad" — broad match in B2B without Smart Bidding monitoring is malpractice.

Expected output shape

A recommended mix with reasoning, migration sequence, negative-discipline implications, and an exit signal.

Variables (2)

• {{page_url}}
  The landing page URL (or pasted markup).

  https://example.com/products/laser-markers
• {{campaign_intent}}
  What the ad-traffic visitor is expecting and what we want them to do.

  Visitors are searching "industrial laser marker for stainless steel". We want them to submit a quote request or book a spec review call.

Critique the landing page below for B2B paid-traffic readiness. Audience: a senior CRO + paid-media joint review.

PAGE:
{{page_url}}

CAMPAIGN INTENT:
{{campaign_intent}}

Score on these axes (0–10 each, 70+ total = solid):

1. Intent match: does the H1 align with the search query?
2. Above-the-fold clarity: in 5 seconds, can a visitor say what this is and what to do next?
3. CTA clarity & placement: is the primary CTA unambiguous and visible without scrolling?
4. Proof: does the page show specific proof (deployments, certifications, named customers) or generic claims?
5. Friction: count clicks/fields between landing and conversion. Is there hidden friction (account creation, mandatory phone)?
6. B2B vocabulary: does the copy sound like an engineer wrote it or a content marketer?
7. Mobile readability: does the page work on a phone in a manufacturing-floor wifi context?
8. Technical execution: page speed, contrast, broken links, lazy-load failures.

Output: scores per axis, 1–2 sentences of evidence per score, the total, and a Top 5 highest-impact fixes ranked by effort:impact ratio.

Do NOT recommend a redesign as a fix unless the page is genuinely unsalvageable. Find the surgical wins first.

Expected output shape

8 axis scores with evidence, total score, and a Top 5 fixes list.

Variables (2)

• {{funnel_data}}
  Stage counts: leads, MQLs, SQLs, opportunities, closed-won. Plus stage-to-stage conversion rates.

  90 days. Leads: 480. MQLs: 220 (46%). SQLs: 60 (27% of MQL). Opportunities: 22 (37% of SQL). Closed-won: 5 (23% of Opp).
• {{channel_context}}
  Where the leads come from, what we know about lead quality by source.

  Mostly Google Ads paid search. Some PMax. No social. Sales feedback: "PMax leads are flakier."

Diagnose the conversion path below. Identify which stage transitions are broken vs healthy, and propose hypotheses for each broken stage.

FUNNEL:
{{funnel_data}}

CONTEXT:
{{channel_context}}

OUTPUT:

1. Stage-by-stage health assessment: each stage transition (lead→MQL, MQL→SQL, SQL→Opp, Opp→Won) gets healthy / weak / broken with the rate and your reasoning.
2. For each weak/broken stage, propose 3 distinct hypotheses about why. Don't pile up hypotheses about the same thing — make them genuinely different (e.g. "intent mismatch", "qualification too strict", "sales handoff bug").
3. For each hypothesis, propose a diagnostic test that would confirm or rule it out.
4. A prioritized investigation plan: which hypothesis to test first and why.
5. Channel-level callouts: do any of the hypotheses apply differently to PMax vs paid search?

Do NOT recommend "improve sales follow-up" as a primary hypothesis unless the funnel data clearly shows MQL→SQL is the broken stage.
Do NOT recommend "test more landing pages" without naming the specific failure mode you'd be testing for.

Expected output shape

Stage assessment + 3 hypotheses per broken stage + diagnostic tests + prioritized investigation plan.

Variables (2)

• {{last_click_data}}
  Per-channel: conversions and revenue under last-click attribution.

  Last 90 days, last-click: Paid Search 200 conv / $1.2M, Organic Search 80 conv / $400K, Direct 60 conv / $800K, Email 40 conv / $200K.
• {{context}}
  B2B context — sales cycle length, multi-touch reality, branded-search prevalence.

  B2B SaaS, 90-day sales cycle, ~7 touches per closed deal, brand search dominant in last-click.

Translate the last-click attribution data below into an estimate of what data-driven attribution would credit instead. Be explicit about your assumptions.

LAST-CLICK DATA:
{{last_click_data}}

CONTEXT:
{{context}}

OUTPUT:

1. Estimated data-driven attribution per channel. For each: conversions and revenue under DDA, and the % delta from last-click.
2. Reasoning per channel: why DDA would credit this channel more / less than last-click. Tie reasoning to the cited context (sales cycle, branded prevalence, etc.).
3. The "branded-search ceiling": what fraction of last-click branded-search revenue is genuinely incremental vs would have closed anyway?
4. Confidence rating per channel (high / medium / low) — where is the model most likely wrong?
5. A "if we could only run one DDA experiment" recommendation.

Do NOT claim DDA gives "the truth" — both models are estimates. Be explicit that this is one estimate among several.
Do NOT inflate paid search credit beyond what the cited context can support.

Expected output shape

Per-channel DDA estimate with reasoning, branded-search ceiling discussion, confidence ratings, and one recommended experiment.