Google Ads demographic targeting for B2B SaaS: income, age, and what to exclude

Google Ads lets you segment and bid-adjust by demographic: age, gender, parental status, and household income (in US campaigns). Most B2B SaaS founders leave all these settings at default — which means serving ads equally to a 19-year-old student and a 45-year-old VP Sales. For software with any meaningful price point, demographic adjustments significantly improve the quality of your click pool without reducing reach among buyers.

How does household income targeting work in Google Ads?

Google segments US users into income brackets based on anonymised IRS data and third-party data: Top 10%, 11–20%, 21–30%, 31–40%, 41–50%, and Lower 50%. These brackets are available under Campaigns → Demographics → Household Income. You can set bid adjustments (bid more for higher income brackets) or exclusions (stop showing ads to lower brackets entirely). For B2B software priced above $200/month, excluding the Lower 50% and applying positive bid adjustments to the Top 10–30% is almost always the right move — business software buyers skew toward higher household income.

What age ranges should B2B SaaS founders target?

Under the Age demographic, look at your conversion data by age band — 18–24, 25–34, 35–44, 45–54, 55–64, 65+. For most B2B software, the 18–24 bracket converts poorly because this group has less purchasing authority and is more likely to be researching rather than buying. Reduce bids or exclude 18–24. The 25–44 bracket is typically the highest-converting range for B2B software — founders, VP-level buyers, and senior individual contributors. Increase bids here. Set 65+ to observation first to see the data before excluding.

Should you target by gender in B2B SaaS?

In most B2B software categories, gender targeting produces minimal improvement and risks reducing reach among qualified buyers. The exception: products with a clear gender skew in the ICP. For a product targeted at VPs of Sales in the tech industry, the buyer pool skews male — but excluding female audiences would also exclude all the female VPs of Sales who are real buyers. Set gender to "Observe" rather than "Target" — collect the data before making exclusions.

How do you apply demographic adjustments without breaking your campaigns?

Don't make all adjustments simultaneously. Start with household income — add the Lower 50% as an observation and check the data for 2 weeks before excluding. Then move to age — again, observe first, exclude or bid-adjust after you have data. Making multiple demographic changes at once makes it impossible to attribute performance changes to a specific variable. One change every 2 weeks, with enough traffic to see statistical significance, produces reliable optimisation without flying blind.