The short answer
Pull Google Ads search terms and Google Search Console queries for the same product, market, and completed date window. Preserve the exact wording and each platform’s native metrics. Group the evidence by buyer need, funnel stage, objection, desired outcome, and urgency. Then use those patterns to draft LinkedIn hooks and offers for a clearly defined professional audience.
Do not paste search queries into LinkedIn ads mechanically. Search is a hand raised; a LinkedIn ad is a tap on the shoulder. The language can travel between channels, but the context cannot. A useful workflow turns query evidence into message hypotheses, not supposed proof of what a LinkedIn audience will click.
Why search intent can improve LinkedIn ad messaging
Most LinkedIn ad briefs begin with internal language: a feature list, positioning statement, or sales deck. Search data adds an outside-in view. It shows how people describe a problem when they are actively looking for an answer.
Google Ads search terms reveal searches that triggered paid ads and the visible performance attached to those terms. Google explicitly notes that this report can inspire creative and landing-page content. Search Console queries reveal the phrases associated with organic impressions and clicks, together with CTR and average position.
Used together, the two sources can surface:
Problem language: “track project profitability,” “reduce reporting time,” or “fix conversion tracking.”
Outcome language: “faster client reports,” “lower wasted spend,” or “see revenue by campaign.”
Comparison intent: alternatives, versus queries, or searches naming an incumbent.
Implementation anxiety: setup, migration, integration, permissions, or security questions.
Stage signals: broad education, solution exploration, vendor evaluation, or urgent troubleshooting.
But neither source tells you which job title, company size, seniority, or industry to target on LinkedIn. That audience decision still comes from the ICP, offer, account data, and campaign objective.
What each query source can and cannot tell you
Google Ads search terms
Use paid search terms to find commercial language tied to delivery, cost, and tracked conversions. Keep campaign, ad group, triggering keyword, match type, impressions, clicks, cost, conversions, and landing page when available.
The report is not a complete log of every search. Google omits some lower-volume terms for privacy, and a conversion does not prove that one phrase caused the sale. Treat the rows as observed evidence, not a census of demand.
Search Console queries
Use organic queries to find recurring problem language, informational questions, category terms, and page-message mismatches. Keep clicks, impressions, CTR, average position, and the landing page when page context matters.
Search Console returns top rows rather than every possible query and may omit anonymized queries. Average position is aggregated, not a fixed rank. Use completed dates for routine analysis, and end the window at yesterday or earlier.
CRM and sales-call context
If available, use CRM stages, call notes, win-loss themes, and objections as a third evidence layer. Search language tells you what people ask; CRM context helps reveal which concerns survive into evaluation. Do not merge personally identifiable information into a prompt or ad-writing workflow.
A review-first workflow
1. Fix one audience, offer, and funnel job
Start with a sentence that forces focus: “We are speaking to [role] at [company type] who is struggling with [job], and the ad should earn [next action].”
For example: “Agency owners managing five or more paid-media accounts who struggle to spot tracking failures, and the ad should earn a guide download.” That is much more useful than “Write LinkedIn ads for marketers.”
2. Pull organic queries for the same product and market
Use a completed 28- to 90-day window. Filter by country, device, page, or query only when that scope matches the LinkedIn campaign. Export enough rows to see repeated language, but keep the exact raw query and metrics.
Useful request:
For Search Console property [SITE_URL], return organic web queries for the previous 90 complete days, ending yesterday. Filter to [COUNTRY] and pages related to [PRODUCT OR USE CASE]. Include query, page, clicks, impressions, CTR, and average position. Export the rows and preserve every raw query. Do not change anything.3. Pull paid search terms for the same scope
Match the market, product line, and date window as closely as possible. Paid terms add commercial evidence because they can be reviewed beside cost and conversion outcomes.
Useful request:
Pull the Google Ads search terms report for [CUSTOMER_ID] for the previous 90 complete days. Limit it to [CAMPAIGN OR PRODUCT SCOPE]. Include the raw search term, campaign, ad group, triggering keyword, match type, impressions, clicks, cost, conversions, and cost per conversion. Export the rows. Do not add keywords or negatives.4. Preserve exact queries before grouping themes
Normalize only for analysis: lowercase text, trim spaces, and standardize obvious punctuation. Keep the original query beside the normalized version. Let AI group singulars, plurals, and semantically similar phrases only after the raw evidence is preserved.
Each group should have a plain-language label such as “manual reporting pain,” “proof of ROI,” “migration risk,” or “competitor replacement.” Add a confidence note based on row count, volume, and consistency across sources.
5. Convert intent themes into a message matrix
A search phrase is not an ad. Translate each strong theme into four decisions: the buyer tension, the promised outcome, the proof required, and the next action. Use the table below as the working model.
Search pattern | Likely buyer state | LinkedIn message angle | Review guardrail |
Repeated problem phrases | Problem aware | Name the cost or friction in the buyer’s context | Do not assume every searcher matches the LinkedIn ICP |
How-to questions | Learning or diagnosing | Teach a checklist, framework, or first step | Use an educational CTA instead of forcing a demo |
Alternative or versus queries | Evaluating vendors | Lead with a credible trade-off or differentiator | Verify competitor claims and supporting evidence |
Setup, integration, or permissions | Implementation anxiety | Reduce perceived risk with process or proof | Do not promise one-click setup unless it is true |
Pricing, cost, or ROI | Commercial evaluation | Frame economics and decision criteria | Never invent savings, payback, or customer results |
6. Draft deliberately different LinkedIn variants
Create variants that test different hypotheses, not cosmetic rewrites of the same sentence:
Problem-led: starts with the costly or frustrating job.
Outcome-led: starts with the result the buyer wants.
Proof-led: starts with a credible mechanism, customer result, or product behavior.
Objection-led: addresses setup, risk, complexity, or trust.
Keep the ICP and offer constant during the first copy test. If audience, offer, visual, and hook all change at once, the result is hard to interpret.
7. Review the claims and the LinkedIn context
Before anything is created, a practitioner should check:
The copy describes a real capability and does not invent proof.
The hook makes sense to someone who was not actively searching.
The audience is defined from LinkedIn targeting evidence, not inferred from query text.
The landing page continues the same promise.
Competitor language is accurate, necessary, and legally sensible.
The CTA matches the funnel stage and the asset being offered.
8. Launch a small, interpretable test
Create two or three materially different messages for one audience and one offer. Use adequate time and spend before choosing a winner. Judge the result across the funnel: delivery, click quality, landing-page behavior, qualified leads, and pipeline. A high CTR can still be a weak business result.
Copyable prompt
I have a Google Ads search-terms export and a Google Search Console query export for the same product, market, and completed date window.
First, preserve every raw query and all source-specific metrics. Do not combine Google Ads cost or conversions with Search Console clicks, impressions, CTR, or average position.
Then group the evidence into buyer-need themes. For each theme, show: representative raw queries; source; funnel stage; likely problem; desired outcome; objection or anxiety; evidence strength; and any conflicting signal.
Use only themes supported by multiple rows or meaningful performance. Mark weak or inferred themes clearly.
For this LinkedIn audience: [ROLE, SENIORITY, INDUSTRY, COMPANY SIZE]. For this offer: [OFFER]. For this desired action: [CTA]. Build a message matrix with the buyer tension, outcome, proof required, hook, supporting copy, CTA, and landing-page promise.
Draft three single-image LinkedIn ad variants: problem-led, outcome-led, and objection-led. Do not paste queries verbatim unless the wording reads naturally. Do not invent claims, customer results, or product capabilities. Flag every statement that requires human or legal review. Do not create or activate an ad.Worked example: from search language to LinkedIn copy
Imagine a B2B SaaS product for agency profitability. Paid search terms include “agency profitability software” and “project margin tracking for agencies.” Organic queries include “how to know which clients are profitable” and “agency time tracking and profitability.”
The useful theme is not the repeated word “profitability.” It is the underlying tension: owners can see hours recorded but still discover margin problems too late.
A direct query copy-paste might say: “Looking for agency profitability software?” That fits a search ad, where intent already exists. On LinkedIn, the stronger hypothesis could be:
Hook: Timesheets tell you where the hours went. They do not tell you which clients are quietly eroding margin.
Supporting copy: See project profitability before month-end, while there is still time to fix scope, staffing, or pricing.
Proof needed: a real product view or verified explanation showing how cost and revenue connect at project level.
CTA: See the profitability workflow.
The query supplied the buyer language. The LinkedIn version supplied context, tension, and a reason to care before the buyer opened a search box.
How HireOtto supports this workflow
HireOtto can assemble the evidence and prepare the supported ad workflow inside the AI client you already use:
Pull Google Ads search-term performance for a chosen account, campaign, ad group, and date range, with summary or CSV output.
Pull Search Console query and page performance, filter by market or device, and export up to the documented per-request limit.
Keep paid cost and conversion metrics separate from organic clicks, impressions, CTR, and average position while the AI groups themes.
Turn approved themes into commentary, headline, destination, CTA, and alt-text options for a supported single-image LinkedIn ad.
Validate the supported LinkedIn hierarchy before writing, create campaign objects and the single-image creative as drafts, and read the saved result back for review.
LinkedIn Ads is currently documented as beta and is available where access is enabled. HireOtto does not prove that query language will perform on LinkedIn, choose the right audience autonomously, verify the final rendering, validate browser-level tracking, or approve claims. Search Console is read-only. The current first-class LinkedIn creative path is a normal single-image Direct Sponsored Content ad. Campaign Manager remains the final checkpoint for Page identity, preview, policy warnings, tracking, and activation.
Use the HireOtto Search Console reporting guide, Google Ads reporting guide, LinkedIn campaign creation guide, and getting-started guide for the verified workflows.
Common mistakes
Treating high-volume language as automatically persuasive.
Assuming a search query identifies a LinkedIn job title or company segment.
Mixing informational organic demand with bottom-of-funnel paid intent.
Letting AI erase raw queries while clustering themes.
Using different markets, products, or date windows without labeling the mismatch.
Writing variants that change only one adjective.
Inventing proof to make a promising hook sound credible.
Judging the test only by CTR instead of qualified outcomes.
Frequently asked questions
Can search queries be used as LinkedIn targeting?
No. LinkedIn targeting uses professional and audience criteria available in Campaign Manager. Search queries can influence messaging, but they do not map directly to job titles, industries, seniority, skills, or company lists.
Should I use Google Ads or Search Console first?
Use both when possible. Google Ads search terms add commercial and conversion context. Search Console usually adds broader problem language and organic page context. If only one is available, state the missing perspective instead of pretending the dataset is complete.
Can I use a query verbatim in the ad?
Sometimes, especially when it is natural language and matches the audience’s situation. Usually the better move is to preserve the meaning while rewriting it for interruption-based discovery. Read the line as if the buyer had not searched for anything that day.
How many themes should I test?
Start with two or three strong, distinct themes for one audience and offer. More variants create more combinations than most B2B LinkedIn budgets can evaluate cleanly.
Can AI create the LinkedIn ad after drafting the copy?
HireOtto can validate and create the supported single-image workflow as drafts when the required account, image, targeting, budget, destination, and permissions are available. A person should approve the message, claim, audience, visual, landing page, and final activation.
About Me
I’m Suyash – badminton junkie, ex‑GroupM ad‑ops grunt, first marketer at a B2B SaaS startup, and creator of Hiretto: MCP servers for performance marketers.
My mission: less clicking, more thinking.
Let’s build leverage together.