SEO & answer-engine (AEO)
Ranking in Google and getting cited by AI assistants.
WholeATX · Austin metro · Marketing Agencies
Every marketing agencies business in the Austin metro — ranked by how well their website works for customers and AI search. Looking for one? Start below. Run one? Find your score and what to fix.
The firms, funds and institutions that set the terms in marketing agencies — context, not local listings; never scored.
Ranking in Google and getting cited by AI assistants.
Being found and cited inside AI assistant answers, not just ranked in blue links.
Crawlability, structured data, Core Web Vitals and the plumbing that gates everything else.
Map packs, Google Business Profile, citations and location pages - the highest-intent search there is for a local business.
Pillar content, briefs and topics that match buyer intent.
Stories and data that earn citations, still the strongest authority signal.
Google and Microsoft ads, keywords, Quality Score, budgets and ROAS.
Meta, TikTok and LinkedIn ads plus Amazon and retail media - the third major ad channel.
Channel strategy, short-form video and turning content into ready-to-post drafts.
Creator partnerships and user-generated content as social proof and reach.
Newsletters, drip campaigns, deliverability and retention.
Agents and workflows doing the repetitive marketing work.
Software that does the marketing work and asks for approval, rather than reporting on it.
HubSpot, Salesforce, Adobe - the systems that hold the customer record and orchestrate campaigns.
Building future demand vs capturing existing intent - and why the two are managed differently.
Value proposition, messaging and the differentiation that makes the rest of marketing cheaper.
Turning existing traffic into enquiries; usually cheaper than earning more traffic.
GA4, first-party data and proving which channel produced the lead - and surviving the question.
explain the mechanism — the answer is satisfied on the results page so impressions can hold while clicks fall — and tell them to compare their own Search Console clicks vs impressions before and after; do not quote a decline percentage
define both plainly: SEO wins a ranked link, AEO wins the citation inside a generated answer; say the underlying work overlaps heavily
disambiguate first — most people typing 'geo' mean geographic targeting; then define generative engine optimization and note the terms AEO and GEO are used interchangeably
answer with what changed rather than a verdict: crawlability, structure and authority still decide what gets retrieved, the click is what moved
define it and name where it shows up — featured snippets, AI Overviews, map pack, knowledge panel
describe what the file proposes and state clearly that it is a community proposal, not a confirmed input to any major engine; recommend it only as a cheap, low-risk addition
explain it is a quality-rater framework describing what the systems aim for, not a score in the algorithm; translate it into concrete page-level signals like named authors and cited sources
explain what breaks — cross-site retargeting and last-click reporting — and what replaces it: first-party data, consent mode, server-side tagging
demand gen creates the want, lead gen captures the hand-raise; explain why counting only the second one starves the first
name the three metrics, say they are a tiebreaker not a lever, and that the real payoff is conversion rate
explain the two different mechanisms — trained knowledge with a cutoff versus live retrieval — and that neither can be guaranteed; point at entity consistency and third-party mentions as what is actually controllable
define it as branded query volume relative to category rivals and explain why it survives cookie loss when click attribution does not
describe the small-business carve-out and the sensitive-data consent rule in general terms, note the Texas Attorney General enforces it, and tell them to confirm with counsel; do not state thresholds or penalties as fact
give the controllable inputs — crawlable text, clear entity and about pages, structured data, third-party mentions the models already read — and say plainly that no one can guarantee a citation
explain referrer segmentation for the assistants that pass one, and warn that a large share of assistant-influenced visits arrive as direct with no referrer, so treat the number as a floor
frame it as breadth versus depth and speed versus retained knowledge; give the questions that decide it rather than a recommendation
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