# SEO vs GEO Budget: How to Split Your Marketing Spend Every planning cycle now I get some version of the same question in a budget meeting: "how much of this should go to GEO?" And I get why people want a number. A clean percentage feels safe. You can put it in a slide, defend it to a CFO, move on with your life. Except here's the problem. I went and looked at what different agencies and consultants are actually publishing on this, and the recommended splits range anywhere from 5% to 70% going to GEO. That's not a narrow band of disagreement, that's basically everyone guessing differently and dressing it up as a benchmark. So before I hand you a number I don't actually believe in, let me walk you through how I think about this instead, because the real answer isn't a percentage, it's a process. ## **TL;DR** - Published GEO vs SEO budget splits range from 5% to 70%, which means the percentage itself isn't a useful number to chase. - Sort every line item into three buckets instead: shared spend that helps both channels (clean structure, real answers, technical fixes), net-new spend that only helps GEO (monitoring, PR, prompt testing), and legacy spend you can pull from (thin content, metric-driven links). - Fix crawler access and your content foundation first. Nothing else works without those two in place. - Shift money based on where your buyers actually research and whether organic clicks are compressing while impressions hold steady, not based on a percentage borrowed from someone else's client. ## Why the published numbers are all over the place I noticed something once I started digging into where these splits come from. The people recommending 70% toward GEO are usually talking to a brand that has almost nothing published yet, a young site with no content foundation, where AI-focused work has a lot of low hanging fruit to grab. The people recommending 5 to 15% are usually pricing GEO as a small, separate add-on layered onto an already mature SEO program. Both of those numbers can be correct for the situation they're describing and still be completely useless for you, because your situation is neither of those exact scenarios. There's also a deeper issue underneath all this. A lot of the confusion comes from treating [GEO service](https://aeocitelab.com/services/ai-seo-services/) like it's a totally separate channel with its own dedicated budget line, when in practice most of what makes you cite-worthy in AI answers is the same work that makes you rank well in the first place. Clean structure, real answers to real questions, technical accessibility, credible sourcing. That's not two different jobs. It's one job done to a higher standard. ## The three buckets I actually sort spend into Instead of arguing over a percentage, I've found it's way more useful to take every line item in a marketing budget and sort it into one of three buckets. ### **Shared spend, the stuff that pays off in both places** This includes cleaning up structured data, writing content that actually answers a specific question clearly, fixing page speed and crawlability issues, keeping your brand info consistent everywhere it appears online, adding real author bylines. None of this is new money. It's existing SEO work, just held to a tighter standard, and it happens to help you in AI answers at the exact same time. ### **Net-new spend, the stuff that only pays off in AI surfaces** This is genuinely additive and it's a much smaller bucket than most vendors want you to believe. Think AI visibility monitoring tools, the labor to actually check what's happening when you run real prompts through ChatGPT or Perplexity, and a meaningful step up in digital PR and third party mentions, since AI models lean heavily on outside validation, not just what your own site says about you. ### **Legacy spend, the stuff you pull money from** High volume definitional content, keyword variant pages that all answer the same basic question in slightly different ways, thin location or category pages, link building pursued purely to move a domain authority number. This is where click yield is already compressing, and it's the least painful place to pull budget from when you need to fund something new. Once you sort things this way, the question stops being "what percentage goes to GEO" and becomes something much more concrete: how much of that legacy bucket should move into the net-new bucket, and how much of the shared bucket needs to be executed better without spending an extra cent. ## Two things that override everything else Before any budget conversation matters at all, there are two gates you need to check first. The first is whether AI crawlers can even reach your site. I've walked into plenty of situations where a security team panicked back when ChatGPT first launched and quietly blocked every AI bot in robots.txt, including the ones that actually matter now. If GPTBot, ClaudeBot, PerplexityBot, or Google-Extended are getting blocked, none of the rest of this matters. This is usually a fix that takes a few days, not a program, and it should happen before you spend a single extra dollar anywhere else. The second gate is your content floor. If you don't have a real base of substantive pages answering genuine buying questions, there's nothing for AI-focused work to cite in the first place. In that case, the right GEO investment is just good content production, full stop. Trying to layer citation monitoring and prompt tracking on top of a thin site is like buying a dashboard to measure a store that hasn't opened yet. ## How to decide which way to lean Once those two gates are clear, here's roughly how I think through where new money should go. Lean toward funding more GEO work when your own sales conversations show buyers are already researching through AI tools, your content foundation is solid, your structured data is clean, and you're seeing your organic clicks compress even while impressions hold steady. That last pattern is a big tell. It usually means people are getting their answer directly inside the AI response and never clicking through at all, which means the audience shift is already happening whether you fund it or not. > [Statistics Canada's](https://www.statcan.gc.ca/en/trust/ai/techstat-ai-impact-canada) own tracking shows business AI adoption climbing year over year, which lines up with why more buyer research is starting inside an AI tool before it ever touches a search engine. Lean toward holding most of your spend in classic search when organic is still producing real, measurable pipeline, your category is more transactional than research-heavy, and your content floor still has real gaps. In that case, fix the foundation first. Redirecting a big chunk of budget toward citation monitoring at this stage just buys you a dashboard tracking a presence you haven't earned yet. And one rule that matters more than people expect: if classic search is currently carrying real pipeline for you, don't cut it to fund something unproven. Ring-fence new money instead. Pulling budget away from a channel that's actually working to chase one that might work is the version of this decision that can genuinely hurt the business, not just misallocate a bit of spend. ### Where the money should actually come from If you've decided to shift some budget, pull it from the legacy bucket first. High volume definitional content is usually the first candidate, since those are exactly the kinds of queries that AI answers are best at absorbing directly, and they were often producing weak returns already. Link acquisition pursued purely for domain metrics is another easy source, since that spend was always a bit of a leap of faith to begin with. What you shouldn't do is take budget away from a working program just because GEO sounds newer and more exciting in a meeting. New doesn't mean better funded. It means better justified. ### How often to revisit the split I check this quarterly as a default, with three things that would trigger an earlier look: a real shift in AI referral traffic, a platform or eligibility change in your market, or an organic decline steeper than what's normal for your category. Anything faster than quarterly is just reacting to noise, honestly, since AI models can give a different answer to the exact same prompt from one week to the next. The teams that end up defending their budget successfully a year from now aren't the ones who picked a clever percentage in a slide deck. They're the ones who can point to each line item and say exactly what it was supposed to buy, and then show whether it actually did. ## FAQs ### **What percentage of my budget should go to GEO?** There isn't a portable number, and I'd be skeptical of anyone who hands you one without asking about your content maturity first. Sort your spend into shared, net-new, and legacy buckets instead. For most mid-sized companies, the genuinely net-new bucket ends up being smaller than you'd expect, monitoring tools, some access and policy fixes, and a step up in PR and third party mentions. ### **Does GEO replace SEO?** No. The technical work that makes your site crawlable, the content that answers real questions, and clean structured data all serve both channels at once. Most GEO work is really just SEO fundamentals executed to a higher bar, not a separate discipline running in parallel. ### **Where should the reallocated money come from?** Pull from your lowest performing, most compressible content first, thin definitional pages, keyword variants answering the same question repeatedly, and link building done purely for metrics. If organic search is currently producing pipeline for you, don't touch it. Fund GEO with new money instead. ### **How fast will I see results after shifting budget?** Technical access fixes show up within days. Content and structured data improvements usually take four to eight weeks to register as pages get re-crawled. PR and third party authority work moves on more of a quarterly rhythm. Whatever you do, record a baseline before you shift anything, otherwise you won't be able to tell the difference between real progress and normal week to week variation in AI answers. ### **Is this split different for B2B SaaS specifically?** The core logic holds, but B2B buyers lean on AI tools heavily during vendor comparison and shortlist building, so the citation gap tends to matter earlier for SaaS than it does for more transactional categories. If your sales team is already hearing "we asked ChatGPT and it recommended someone else," that's a strong signal to move faster on the net-new bucket.