How demand forms now
Car decisions start as life changes: a new job, a growing family, a move, a totaled vehicle. People describe those situations to AI assistants and get specific guidance back, often including which dealers or brands to consider. By the time someone searches an inventory site, the assistant has usually already framed the shortlist.
Move 1: Map your buying moments
List the twenty situations that actually bring customers to you: end-of-lease, first EV, teen driver, fleet refresh, post-accident replacement. Run each through the four assistants and record who gets named. This baseline is your moment map, and it usually shows a competitor owning conversations you never knew existed.
Move 2: Fix what AI believes about you
Assistants pull from your Google Business Profile, review consensus, dealer directories, and local press. Wrong hours, dated inventory claims, and three-year-old review responses all leak into answers. Correcting the sources assistants cite is the fastest visible win in automotive GEO.
Move 3: Publish answers, not ads
The questions buyers ask assistants are concrete: lease versus buy at these rates, what a fair doc fee looks like, how EV incentives work in your state. Dealers who publish plain, specific answers become the pages assistants quote, and quoted dealers become recommended dealers.
Move 4: Win the local trust stack
Automotive answers weight local proof heavily: review volume and recency, service-department reputation, community presence. A steady stream of detailed reviews naming specific vehicles and staff teaches assistants exactly when to recommend you over the group store across town.
Move 5: Measure the answers monthly
AI answers shift with model updates and competitor moves. Track your mention rate across your moment map, watch who gains, and treat drops as incidents. Aethon runs this continuously across ChatGPT, Gemini, Claude, and Perplexity, then ships the fixes that move the answers.
