Which AI engines should marketing agencies focus on first?

Q: Which AI engines should agencies prioritise first?

Start with ChatGPT and Perplexity. ChatGPT has the largest buyer audience and the broadest commercial-query coverage. Perplexity shows citations openly, which makes source attribution and reporting cleaner. Layer in Gemini, Claude, and DeepSeek as the program matures.

Q: Should agencies track all five engines from day one?

Most GEO platforms support all five out of the box, but the bottleneck is report legibility and team capacity. Two engines and twelve prompts produces clearer wins than five engines and fifty prompts. Add engines as the foundational work matures.

Q: Does engine choice depend on the client type?

Yes. B2B SaaS buyers use ChatGPT, Perplexity, and Claude. Ecommerce buyers use ChatGPT, Perplexity, and Gemini. Local services skew toward ChatGPT and Gemini. Professional-services clients live in ChatGPT and Perplexity.

Q: Why is Perplexity worth prioritising over Gemini?

Perplexity shows citations inline with the answer, making source attribution straightforward. Gemini is harder to instrument because citations are inconsistent and partly buried inside Google AI Overviews. Perplexity also skews toward shortlist-style buyer prompts where citations matter most.

Q: How many prompts should an agency track per client?

Twelve to twenty buyer-intent prompts per client is the right starting range. Larger prompt sets produce noise and dilute the action list. Quality of prompt selection matters more than volume.

Question

Which AI engines should marketing agencies focus on first?

Maciej Grabek · Accepted Answer

Most marketing agencies should start by tracking ChatGPT and Perplexity, then layer in Gemini, Claude, and DeepSeek as the program matures. ChatGPT has the largest audience and the broadest commercial-query coverage. Perplexity is the cleanest signal for buyer-intent prompts because it shows its citations openly. The other three matter, but adding all five at once usually overwhelms the team and the report. The five engines, ranked by agency relevance ChatGPT. Largest user base, most buyer prompts, most coverage of commercial queries. If a client is invisible here, it shows up in pipeline first. Perplexity. Smaller audience but heavy with research and shortlist-style prompts. Citations are visible inline, which makes the source-attribution work easier and the client report more legible. Gemini. Embedded inside Google's surface and increasingly inside Google AI Overviews. Matters more for clients whose buyers still start on Google. Claude. Smaller direct audience, but heavy with technical and B2B buyers. Worth tracking for SaaS, dev-tools, and professional-services clients. DeepSeek. Limited but growing. Worth a baseline scan; usually low priority for first 90 days unless the client has APAC exposure. How to pick the starting set per client type The answer is not the same for every client. A few patterns: B2B SaaS clients: Start with ChatGPT + Perplexity + Claude. Their buyers ask shortlist-style questions and disproportionately use Claude for technical evaluation. Ecommerce and DTC clients: ChatGPT + Perplexity + Gemini. Gemini matters because product comparison queries are heavily Google-flavoured, and the AI Overview surface is where they show up. Local services clients: ChatGPT + Gemini. Perplexity matters less because local-intent prompts skew toward Google. Professional services (legal, accounting, consulting): ChatGPT + Perplexity. These buyers research heavily and almost always shortlist before contacting anyone. Why not just track all five from day one You can, and most GEO platforms support it out of the box. The issue is not data collection. It is report legibility and team capacity to act on five engines worth of gaps simultaneously. Clients glaze over at a report that shows their brand missing from 47 prompts across 5 engines. Two engines, twelve prompts, and a clear fix list lands better and produces faster wins. Once the foundational work is done - schema, key citations, entity anchors - the program starts to show results on the other engines without needing engine-specific work. The same Forbes Advisor listicle that gets a client cited by ChatGPT often gets the same client cited by Claude and Gemini because they all draw from similar source sets. A concrete example Take a 40-person growth agency with a B2B SaaS client selling AI customer-support software. The agency runs an initial scan across all five engines with the prompt What is the best AI customer support software for mid-market SaaS? Results: ChatGPT cites 4 vendors; the client is not one of them. Perplexity cites 5 vendors with footnotes; the client appears in one footnote source but not in the answer body. Gemini cites 3 vendors; the client is absent. Claude cites 4 vendors; the client appears third. DeepSeek cites 2 vendors; the client is absent. The agency's first 90 days targets ChatGPT and Perplexity specifically because (a) ChatGPT has the largest buyer audience, and (b) Perplexity is one citation away from naming the client in the answer body. The work needed for both - getting the client on the right comparison pages, fixing entity schema, placing two analyst-style pieces - also lifts Gemini and DeepSeek as a side effect. The team reports on two engines and tracks five. Common starter mistakes Reporting on too many prompts. Twelve to twenty buyer-intent prompts per client is enough. Tracking 100 prompts produces noise and report fatigue. Treating each engine as a separate workstream. The fixes overlap. Build a single content and citation roadmap, then watch which engines pick it up first. Picking engines based on the agency team's preferences. Your strategists use Claude. The buyer uses ChatGPT. Track where the buyer is, not where the team is.