The distinction matters in recruitment because candidate behaviour has already moved. Candidates ask assistants which employers to consider, what a role pays, and whether a company is worth their time. The assistant’s answer is assembled from the sources it can parse and trust. GEO determines whether your brand is in that working set at all. It is one part of the wider picture covered in employer brand AI visibility.

GEO vs SEO in recruitment

The 7-point GEO checklist for recruitment

1. Organization markup on your corporate root. The machine’s anchor for who you are.
2. JobPosting markup on every live role. Without it your vacancies are pictures of jobs, not data about jobs.
3. Crawler access for GPTBot, ClaudeBot, PerplexityBot and Google-Extended. Blocked crawlers mean an empty working set. The careers site access and schema guide covers how to check yours.
4. Server-rendered careers content. Roles that only exist after JavaScript runs are invisible to most retrieval.
5. A correct Wikidata entity with your official site attached, so evidence attributes to you and not a namesake.
6. Concrete facts on career pages. Pay, progression timelines, headcounts, locations. Engines reuse specifics.
7. Measurement on a cadence. Models and indexes shift constantly, so a single reading tells you almost nothing. The SetpointHQ Index rescores its cohort monthly against a published rubric. GEO is a discipline with a scoreboard, not a one-off project.

Points 1 to 6 you can audit yourself in an afternoon. Point 7 needs a baseline: check what the assistants say about you today before you change anything, so you can tell later whether the work moved the number. If you want to know why one employer gets named and a larger one does not, that is how ChatGPT decides who to name.

What the Index data shows

We score 100 UK employer brands monthly on 5 dimensions for The SetpointHQ Index. The July 2026 retail run is instructive precisely because the sector is not weak: its median composite of 57.0 sits well above the 52.8 Index median, 3rd of the 14 sectors we track. The failure is concentrated in one dimension.

6 of the 10 retail brands score 97 or higher on knowledge graph, so the machines can recite their store counts and corporate history in detail. The sector’s median citation score, which measures whether an assistant actually names the brand when a candidate asks where to work, is 25.0 out of 100. Known and recommended are not the same thing.

The schema column splits the sector clean down the middle. John Lewis Partnership scores 80 and takes second place overall on composite, ahead of Tesco, Marks & Spencer and Sainsbury’s, all several times its size. At the other end, every one of the 9 structured data checks we run against Sainsbury’s came back empty — a schema score of 0 — while its knowledge graph score is 97.9. The machines know exactly who Sainsbury’s is and can read nothing about its jobs. See the full UK retail AI visibility report and its banking counterpart.

GEO gets you into the machine’s working material. Its sibling discipline, answer engine optimisation, is about winning the final answer itself, and the step-by-step for both is in how to make your recruitment brand show up in AI answers.

Source: The SetpointHQ Index, 100 UK employer brands, scored monthly across ChatGPT, Claude, Gemini and Perplexity. Figures as at 13 July 2026, rubric 2026.06.5.