Why am I reaching irrelevant age groups?

Part 1: Why you’re reaching “irrelevant” age groups (and why it’s often not a mistake)

Age data is modeled, not perfectly known, so “off” age groups are normal

In Google Ads, age reporting is frequently inferred from signals rather than verified identity. That means you should expect a portion of impressions (and even clicks) to land in age brackets that don’t look like your ideal customer. The biggest “gotcha” is that demographic reporting isn’t a census; it’s an estimate. If you treat it like a hard truth, you’ll end up “fixing” the wrong problem.

“Unknown” age is a real bucket—and often the biggest one

Many advertisers interpret “Unknown” as wasted reach. In practice, “Unknown” often includes high-intent users whose age can’t be confidently determined (privacy constraints, limited signals, signed-out traffic, consent settings, restricted cookies, etc.). If you exclude “Unknown” aggressively, you can reduce volume and accidentally cut out strong performers, especially on Search, YouTube, and mobile-heavy traffic.

Shared devices and mixed-use behavior skew demographics

Households share devices. People browse while signed into different profiles (or not signed in at all). A parent may research on a teen’s device, or a student may use a family laptop. Google can’t always distinguish who is behind the screen, so age signals can be directionally correct at scale but wrong at the individual impression level.

Your campaign type may be designed to expand beyond your “ideal” profile

Some campaign types are built to find incremental conversions outside your initial assumptions. If you’re running Performance Max, broad match Search with Smart Bidding, or YouTube/Display with automated expansion features, the system may deliberately test beyond your core age range to uncover cheaper conversions or new pockets of demand. That expansion can look like “irrelevant reach” in reporting, even when the conversion economics are positive.

Observation vs targeting: you might be “watching” ages, not restricting them

A very common reason for reaching unwanted ages is that demographics are set up for observation (reporting and/or bid adjustments), but not truly constrained through exclusions. In many setups, advertisers add audiences and then assume they are restricting reach—when they’re actually just collecting data on who the ads reached.

Your targeting may be correct, but your creative is attracting the wrong people

Even with solid targeting, messaging can pull in the wrong click. For example, “cheap,” “student,” “free,” “beginner,” or “easy” language often attracts younger audiences, while “executive,” “enterprise,” and “done-for-you” language tends to pull older audiences. The age mismatch sometimes isn’t a targeting issue at all—it’s a positioning issue.

Part 2: A systematic diagnosis to identify the real cause (before you change anything)

Start by judging age groups by conversion value, not impressions

Irrelevant reach only matters if it creates irrelevant cost. Before you exclude anything, pull performance by age and compare conversion rate, cost per conversion, and (if you have it) conversion value or qualified-lead rate. You’re looking for a pattern: are certain age groups genuinely non-performing, or are they just smaller/not your “expected” audience?

Use this quick diagnostic checklist (10 minutes, high impact)

• Check where the age mismatch is happening: Search, Display, YouTube, Performance Max, Demand Gen, or a mixed network setup.
• Look at the “Unknown” share: If “Unknown” is large, treat it as a separate strategy decision, not an automatic exclusion.
• Review demographic settings: Confirm whether any age ranges are excluded at the campaign level (not just viewed in reports).
• Audit expansion features: Identify whether any automated expansion is enabled (audience expansion / optimized targeting-style behavior, depending on campaign type and setup).
• Verify bidding strategy: Smart Bidding (Maximize Conversions/Value, tCPA/tROAS) will explore; manual bidding tends to be more literal.
• Check search terms and placements: A “young” skew often comes from certain queries (e.g., “course,” “training,” “how to”) or specific app/site placements on Display/YouTube.
• Validate conversion quality by age: If you have lead gen, compare down-funnel quality (SQL rate, close rate, refunds, cancellations) by age where possible.

How to interpret what you find (so you don’t over-correct)

If younger or older ages have spend but no conversions, that’s the cleanest case for exclusions or tighter controls. If they have conversions but lower quality, the fix is usually better conversion definitions (import qualified conversions), stronger pre-qualification on the landing page, and value-based bidding—not just blocking age ranges.

If the mismatch is mostly in impressions and clicks but conversions are concentrated in the right ages, your account may already be working fine. In that scenario, “irrelevant reach” is often just the platform exploring while still delivering efficient results.

Part 3: Fixes that reliably tighten age relevance without killing performance

1) Use age exclusions strategically (and don’t panic-exclude “Unknown”)

If you have clear evidence that a particular age range never converts (or drives consistently unqualified leads), exclude it at the campaign level. Do this after you’ve confirmed enough volume to be confident (not after a handful of clicks). For “Unknown,” my default is caution: test it rather than blanket-excluding it, because it can contain profitable intent.

2) Tighten the “why” behind your clicks: align ads and landing pages to your real buyer

When age skew is driven by messaging, you fix it by changing who feels invited to click. Use ad copy and landing-page cues that signal the right audience. Mention relevant buyer context (job role, company size, homeowner status, professional outcomes, premium positioning, time constraints). Also remove ambiguity: if your offer is for professionals, say so; if it’s not for students or beginners, clarify that gently.

3) For Search: reduce accidental clicks with intent filters, not just demographics

Age targeting alone rarely fixes irrelevant reach on Search because Search is intent-led. The fastest improvements usually come from query control. Add negative keywords that map to the “wrong audience” intent (for example, education-seeking terms, “free,” “DIY,” “definition,” “salary,” “internship,” “near me” if you’re not local, or hobby-style terms if you’re B2B). If broad match is used, make sure your negatives and your conversion tracking are strong enough to teach Smart Bidding what “good” looks like.

4) For YouTube/Display/Demand Gen: watch for expansion behavior and inventory drift

On visually-driven networks, it’s common to see age drift because the system is optimizing across massive inventory. If you’re reaching irrelevant ages there, review whether any expansion features are enabled that widen targeting beyond your selected segments. Then review where ads are showing (placements and content contexts) and remove obvious mismatches. You’ll often find that a small number of apps/channels/sites are responsible for a disproportionate share of low-quality traffic.

5) In Performance Max: improve signals and conversion definitions (that’s your control lever)

Performance Max doesn’t behave like a traditional “I target X and only X sees it” campaign. Your strongest levers are your conversion goals, the quality of those goals, and the audience signals you feed the system. If age relevance is off, strengthen first-party signals (customer lists where eligible, engaged-site audiences, high-intent remarketing pools) and ensure you’re optimizing toward outcomes that reflect real business value (qualified leads, purchases, revenue) rather than easy-but-low-value micro actions.

6) Make demographic decisions with a measurement upgrade, not just a targeting tweak

If you’re lead gen, reaching the “wrong” age group is often a symptom of optimizing to the wrong conversion event (for example, “form submit” when half of the submits are junk). Import qualified stages from your CRM where possible, or at minimum create a second conversion that represents quality (appointment held, verified lead, quote request with minimum criteria) and optimize toward that. When the bidding algorithm learns what quality is, demographic relevance usually improves as a side effect.

7) A safe optimization sequence (prevents performance cliffs)

• Step 1: Validate conversion tracking and define a “quality” conversion (or value) that represents your real customer.
• Step 2: Add negatives / placement exclusions that clearly represent the wrong intent or inventory.
• Step 3: Adjust creative to discourage the wrong audience and speak directly to the right one.
• Step 4: Exclude only the age ranges with consistent non-performance (or proven low-quality), and test “Unknown” rather than assuming it’s waste.
• Step 5: Re-check results after enough data accrues, then tighten further in small increments.

What “good” looks like after fixes

You’re aiming for a world where the age distribution in conversions (and conversion value) matches your business reality—even if impressions remain broader. If your conversion mix is correct and your costs are efficient, the campaign is doing its job. If conversions are happening in the wrong ages, that’s when you tighten targeting, improve signals, and upgrade what you’re optimizing toward.

If you’re seeing “irrelevant” age groups in Google Ads, it’s often because age reporting is modeled (inferred from signals, not verified), so some demographic “noise” is normal, and because a large share of traffic may fall into the “Unknown” bucket where Google can’t confidently infer age. It can also happen when shared devices blur signals, when you’re using campaign types and bidding setups designed to explore (like Performance Max, YouTube/Display, or broad match with Smart Bidding), or when demographics are set to “Observation” (data collection) rather than true “Targeting” (reach restriction). In many cases the real driver is intent and creative: search terms like “free,” “course,” or “training,” or messaging that skews “student/beginner,” can pull younger audiences, while certain placements on YouTube/Display can drift into low-quality inventory. If you want a faster way to diagnose what’s actually happening and what to change without over-correcting, Blobr connects to your Google Ads and runs specialized AI agents that continuously analyze performance by segment, surface wasted spend (like irrelevant queries or placements), and suggest concrete actions—such as negative keywords, placement exclusions, and ad/landing-page alignment—while you stay in control of what gets applied.