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Understand its recommendations

Hero AI's responses read like polished prose. Underneath, data lookups and reasoning steps build them. An answer mixes measured facts and the model's interpretation. Know which is which. Then you know what to act on directly, and what to verify first. The short version: trust the specific numbers, test the causal claims.

Where the answer comes from​

Hero AI builds every answer from three sources:

  1. Live Google Ads data. Performance metrics, keyword lists, ad copy, and extensions for your active account, pulled fresh when you ask. This is the most reliable layer. Any number Hero AI cites comes from here.
  2. Your product context. What Hero Marketer learned about your product during onboarding, who buys it, the jobs they hire it for, your category, objections, plus any edits you have made since. When Hero AI references "your customers" or "your product," it draws on product context.
  3. General knowledge of Google Ads and B2B SaaS. Built into the model. Useful for framing, but not specific to your account.

When Hero AI explains why something happens, the model reasons over the live data and its general knowledge to build that explanation. The explanation is not a measured fact.

Some reports carry extra interpretation rules:

  • Landing page reports group tracking URL variants into real destination pages first. Keyword rows in that report come from URL tracking parameters when present. See Read landing page reports.
  • Simulation results are Google's historical simulation points for a bid, budget, Target CPA, or Target ROAS change. Treat them as planning evidence, not a promise of future results.
  • Click diagnostics are narrow, single day checks. They are useful for lead quality, GCLID, or traffic quality questions, but too noisy for a normal campaign audit.

What is a measured fact versus an inference​

Every recommendation mixes two kinds of statement, and they earn different levels of trust:

  • Observations are facts pulled from your account. "Your CPA on the alternative to jira campaign is $245 over the last 14 days" is an observation. You can confirm it in the dashboard. Trust these.
  • Inferences are conclusions Hero AI draws from the observations. "The high CPA is likely driven by a few high cost keywords that aren't converting" is an inference. It might be right. It might be wrong. Verify these before acting.
  • Simulation points sit between the two: they come from Google Ads, but they describe modeled tradeoffs from historical data. Act on them only when observed performance points the same way.

How Hero AI weights what it finds​

Hero AI does not treat every blip as equally meaningful. It checks whether a finding holds across three time windows: the current period, the prior period, and a longer historical baseline. Then it weights its recommendations toward the findings that hold up:

  • A finding consistent across all three windows is usually structural and worth acting on.
  • A finding that shows up in a single window only may revert on its own. Hero AI flags it as worth watching. It often says it is too early to act, and it proposes when to re check.

The supporting evidence section under each recommendation shows the per window figures, so you can confirm which case applies.

When a recent change in your account lines up in time with a metric shift, Hero AI scores that change as a primary suspect, a secondary factor, or ruled out. This shows you exactly what it attributes the move to. Then you can revert the right thing and not pause the affected campaign.

How to verify before acting​

Three quick checks before you act on a recommendation:

  • Cross check the dashboard. Hero AI's numbers should match what the dashboard shows for the same date range. A mismatch is usually a date range or filter difference, not a data error.
  • Open Google Ads. For recommendations about individual keywords, the search terms report and the performance view for each keyword are the source of truth. Hero AI summarizes. Google Ads shows the underlying rows.
  • Ask Hero AI to show its work. "What specific keywords are you talking about? Show me the numbers." It lists the keywords and metrics behind the recommendation.

When recommendations are reliable, and when they are not​

Hero AI's recommendations are more reliable when:

  • The campaign has at least 100 clicks and 10 conversions of data.
  • The time window is at least 14 days.
  • You state a goal, so Hero AI knows what to optimize for.

They are less reliable when:

  • The campaign is less than a week old.
  • Volume is low, under roughly 50 clicks per week.
  • Conversions are low or zero, in which case Hero AI often says "not enough data."

When Hero AI is wrong​

Hero AI is wrong sometimes. The common failure modes:

  • It misses context outside Google Ads. A CPA spike might come from an industry shift or a competitor's launch that Hero AI cannot see.
  • It overweights recent data. A weekend dip can look like a trend if Hero AI only checked the last 5 days.
  • It can be confidently wrong. Polished prose makes a guess sound like a finding. Treat specific numbers as facts. Treat causal claims as hypotheses to test.

When Hero AI is wrong, push back: "Are you sure that's what happened? What's the underlying data?" That usually surfaces the gap, or makes Hero AI correct itself.

What to do with a recommendation​

Most recommendations come with one of two paths:

  • Hero AI runs it for you. Some changes have an action button: for example, pause a campaign, add a negative keyword, draft and apply new ad copy, or redraft landing page copy. You review and confirm before anything changes. See Actions Hero AI can take.
  • You run it in Google Ads. For changes Hero AI cannot make, for example when you restructure targeting or edit conversion tracking, the recommendation gives you detailed instructions to follow yourself.

Two rules of thumb regardless of path:

  • For changes you are unsure about. Test on one campaign first, give it a week, then evaluate.
  • For big strategic moves. For example, if you kill a campaign, restructure targeting, or double a budget, get a second opinion. Ask Hero AI to argue the opposite ("What's the case for not doing this?") or run it past your team before you confirm.

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