Step one: map the moments
List the changes that reliably precede purchase in your category, personal or business: the diagnosis, the engagement, the layoff, the lost enterprise deal, the lease ending. Sales calls, support tickets and customer stories are better sources than keyword tools, because customers narrate moments naturally. Aim for the twenty to fifty situations that drive most of your demand, then phrase each the way a real person would confide it, not the way a marketer would tag it. If you need the definitional grounding first, start with what are life moments.
Step two: measure the answers
Run each moment through ChatGPT, Gemini, Claude and Perplexity and record the full cascade: needs inferred, pathways suggested, brands named, sources cited. Score yourself on presence and position, moment by moment and assistant by assistant, the method behind AI share of voice. Expect surprises: the moments you assumed you owned are often where competitors get named, and unclaimed moments, where no brand is consistently recommended, are the cheapest wins available.
Step three: fix the sources, then prove the revenue
For every losing moment, the fix runs through sources: publish content that answers the confided situation directly, correct the third party pages assistants cite, and keep facts current everywhere retrieval looks, using the workflow in correcting wrong information in ChatGPT. Then close the loop financially: tag AI referred sessions, follow them to pipeline, and report share of voice next to revenue so the program defends its own budget.
This whole loop, moment library, four assistant measurement, executed fixes and attribution, is what Contextual AI Presence Mapping© operationalizes and what Aethon runs as a platform. The fastest start is a free AI visibility audit: we run your category’s moments and walk you through the wins, losses and fix list on a 30 minute call.
