The numbers a GEO programme should live by

It is easy to drown a GEO effort in numbers that look impressive and prove nothing. The discipline is to track a handful that actually reflect whether machines are recommending you, accurately, for the questions that matter. Four tend to carry most of the weight.

Share of answer is the headline: across your set of important questions, how often does an engine name you. Mind share is the competitive cut of that: when you and a rival are both candidates, how often does the model pick you. Accuracy is the quality check: when you are mentioned, is the description true, current, and on message, because a frequent but wrong mention is its own problem. And coverage is the breadth: how many of your priority questions surface you at all, versus returning a competitor or a blank.

What these have in common is that they tie to the real goal, which is being the recommended, correctly described option at the moment of a decision. They resist gaming, because you cannot fake them without actually improving how the web talks about you. And they change slowly enough to trust, so a steady rise means the work is landing.

Everything else is supporting detail. Traffic from AI sources, when you can attribute it, is a nice confirmation. But if you only watched four numbers, these are the four that would tell you whether the programme is working or just busy.