Google Ads guide

How to Set Up Google Ads Search Terms Report for Startups

To set up a Google Ads search terms report for a startup, write down the current ideal customer profile and experiment goal, then classify queries by problem, stage, fit, and next action. Review spend and clear mismatches quickly, but give promising adjacent language a separate learning path. Connect the report to activation, qualified accounts, paid conversion, revenue, or retention before deciding what to scale.

Google’s search terms report shows searches that resulted in ads being shown. It reveals customer language, but it cannot tell whether the searcher matches the startup’s current ICP or reaches the product’s first value event. The setup below adds that context.

Write the current experiment brief

Start with the hypothesis the campaign is testing. State the product or offer, the current ideal customer profile, problem, market, role, use case, price or plan, and the event that shows the person received value. Add the audiences that are explicitly outside the experiment, such as students, job seekers, unsupported regions, free-only users, or teams without a required integration.

Date the brief. A startup’s ICP and positioning may change quickly, and an older rule can make a new query look irrelevant when it is actually evidence for a new direction. Keep “outside this experiment” separate from “not a customer.”

Choose a primary outcome. It might be activated account, paid conversion, qualified opportunity, completed order, or retained user. Use supporting signals such as signup, product event, meeting, revenue, margin, or cohort retention, but do not let a low-quality signup stand in for the outcome.

Pull the report with experiment context

Open the search terms report for the correct account and campaign. Include search term, campaign, ad group or asset group, keyword, match type, impressions, clicks, cost, conversions, and conversion value. Add experiment name, landing page, audience, device, region, signup, activation, account, or CRM status where the data is dependable.

Verify the date range, time zone, conversion actions, and filters. A fast spend review may use a short window; an activation or retention review needs more time. Keep both views if needed: one for runway protection and one for product or revenue learning.

Annotate launches, pricing tests, onboarding changes, product outages, new creative, budget shifts, sales changes, and ICP changes. These events explain why the same query may perform differently in different weeks.

Label queries by intent and fit

Give each important row an intent label and a fit label. Intent may be problem, category, product, comparison, competitor, pricing, implementation, education, support, jobs, or unrelated research. Fit may be core ICP, adjacent hypothesis, outside current scope, or unknown.

Then add an action: protect, expand, improve the path, validate, or exclude. A query that describes a real problem for an adjacent industry may be worth an interview or bounded test, even if it is not ready for the core campaign. A query that names the product may still be a student or support request.

For every important row, ask:

  1. What does the searcher want?
  2. Who might the searcher be?
  3. Does the current product fulfill the implied need?
  4. Does the page and onboarding path match the promise?
  5. What evidence would justify a change?

Keep uncertainty visible. “Needs customer or product review” is a valid classification. It is better than turning a guess into a negative keyword or a budget recommendation.

Review spend and promising evidence first

Sort the report by costly mismatches, then by activated or qualified outcomes, then by recurring language and adjacent hypotheses. Read the exact query and matching context, not only the keyword or campaign label.

For a promising search, record the problem, ICP fit, destination, product event, and next test. The action could be a clearer headline, use-case page, onboarding route, form question, keyword, or content experiment. Do not add a large keyword set merely because one phrase sounds good.

For a costly mismatch, state the reason. It may be a job search, unsupported service, free request, unrelated product, out-of-market user, or broken campaign promise. Check whether the mismatch is universal or limited to one experiment before excluding it.

For a low-volume query, avoid making a strong conclusion. Keep it in the learning log if it is plausible, and record what would make the signal stronger. A startup can protect runway from clear waste without pretending that every small sample is a market verdict.

Check the route to first value

For good-looking searches that do not activate, inspect the path from ad to product value:

  • Does the ad accurately name the problem and audience?
  • Does the page show who the product is for and why it is different?
  • Does signup or checkout ask only what is needed?
  • Can the user reach the first value event quickly?
  • Is the product, integration, pricing, or support expectation correct?

A relevant query can fail because the page is generic, proof is missing, onboarding is confusing, or the product does not support the use case yet. A negative keyword would erase that learning. Record a page, product, onboarding, or positioning action for the responsible team.

Use customer language from recurring searches in messaging and research. It may become a headline, use-case page, product prompt, sales question, or interview topic even when it is not immediately scalable as paid coverage.

Add negatives with experiment scope

Use the Google Ads negative-keyword guidance and keep a proposal log. For each negative candidate, record the exact query, why it is outside the current offer, proposed wording, campaign or ad-group scope, good queries checked, owner, and review date.

Start with clear exclusions such as jobs, homework, unrelated support, unsupported regions, or a service the startup cannot fulfill. Be careful with category, competitor, comparison, education, and “free” terms while positioning is still being tested. A phrase that is outside one experiment may be a valuable clue for another.

Use a narrow scope when the meaning is local to one campaign. Shared negative lists should be reviewed as governed assets. After applying a negative, check spend, core ICP coverage, activated users, qualified accounts, and learning. If good coverage falls, inspect the wording or scope before adding more.

Do not use negatives to hide broken tracking, weak onboarding, a missing feature, slow follow-up, or an unclear offer. Those are findings, not unwanted traffic.

Connect search terms to activation and revenue

Join query themes to the startup’s next meaningful event. A signup is not necessarily activation. A trial is not necessarily paid conversion. A lead is not necessarily a target account. A purchase is not necessarily profitable or retained. Mark unknown outcomes when the data is not joined.

Google’s measurement guidance provides platform context. Product analytics, CRM, and order records provide later evidence. Compare themes by activated rate, qualified rate, paid conversion, pipeline, revenue, margin, or retention according to the experiment brief.

When the funnel is weak, inspect query, message, page, signup or form, onboarding, product fit, and event tracking in that order. Assign the fix to the right owner. A media change should not be the default response to every activation problem.

Keep a learning and change log

For each material finding, record the hypothesis, evidence, action, owner, scope, date, and next signal. Keep proposed, approved, implemented, and observed separate. In a weekly runway review, summarize clear waste and immediate fixes. In a longer product review, summarize ICP language, activation, revenue, and adjacent hypotheses.

Explain tradeoffs to the team. Excluding a broad research phrase may reduce cheap signups but also reduce early discovery. Expanding a category may reveal an audience before the startup has the content or onboarding to serve it. Use a bounded experiment when the hypothesis is valuable and the risk is affordable.

The startup Performance Max guide can provide wider PMax context. adz.so can help inspect supported account data and organize the workflow, but it does not determine product-market fit, replace product analytics, or guarantee activation.

Validate the startup setup

Before making the process routine, check that:

  1. the current ICP, offer, experiment, and non-fit rules are documented;
  2. the primary outcome and supporting activation or revenue signals are defined;
  3. intent, fit, action, and uncertainty are separate fields;
  4. account, campaign, date range, filters, and conversions are correct;
  5. costly mismatches and activated or qualified outcomes were reviewed;
  6. negative candidates have exact wording, scope, and evidence;
  7. page, onboarding, product, sales, and tracking issues have owners; and
  8. the next review matches the data volume and learning cycle.

A startup search terms report is set up well when it protects runway and improves the next decision. Let current ICP fit and downstream value guide expansion, while preserving useful evidence from adjacent language until the product and market hypothesis are genuinely tested.