AI search tracking for a dealer group with locations in multiple cities
How we track AI search visibility for a dealer group with locations in multiple cities: location-aware prompts, market-level competitors and the tool we use.
A dealer group looks like one brand to its owners and like six different businesses to a shopper. Someone in one city asks about the nearest service department, someone in another asks about used inventory, and the engine answers each from a different set of sources. When a dealer group asks us which tracking approach works, our answer starts with structure, and only then moves to tools.
Structure first: one group, many markets
We set the program up in three layers.
Group layer. Brand-level prompts that apply everywhere: "best [brand] dealer group", "is [group name] trustworthy". These show how AI describes the group overall.
Market layer. One set of prompts per city, with local competitors named. "Best place to buy a used SUV in [city]" and "[brand] service center near [neighborhood]" belong here.
Store layer. Store-specific questions about inventory, service, financing and hours, where the store's own pages and listings are the likely sources.
Each market gets its own competitor list. The dealer across town in one market is irrelevant in another, and blending them produces a meaningless share of voice.
What the tool must support
- Location-aware tracking. A prompt like "dealer near me" needs a place. The tool should ask from the market.
- Segmentation by topic and persona. New vehicles, used, service and parts behave differently.
- Citation visibility. Which listing sites, review pages and local pages feed each market's answers.
- Crawler access checks. Many dealer sites run on third-party platforms with their own robots rules. If bots are blocked from inventory or service pages, content work will not matter.
- Traffic and conversion tracking where the site can capture leads.
What we use
We run client programs on Promptwatch. For multi-location work, the relevant features are state and city tracking, available from the Business plan ($579 a month) and on every agency plan, and Local GEO/SEO targeting, added in October 2026. It pins a monitor to exact coordinates with a radius from 20 m to 5 km, so ChatGPT, Google AI Overviews and Google AI Mode answer as if asked from that spot. Other models fall back to country, state or city. Coordinate targeting is on Business, Agency Scale and Enterprise.
Alongside that, topics, tags and personas let us separate the layers above. Citation analytics shows which pages are cited in each market, crawler logs (from Professional upward) show whether AI bots fetch inventory and service pages, and visitor analytics reports AI-referred sessions and conversions. Agency plans (Kick-off $199, Growth $399, Scale $799) include unlimited projects and city tracking, which suits agencies running many stores.
A note for fairness: Promptwatch does not produce a map grid of rankings per store. If a dealer wants that visual, we pair it with a dedicated local tool, since grid scans answer a slightly different question.
Where the local signal comes from
Local AI answers lean on the same ingredients as local search. Complete and consistent listings, recent reviews that say specific things, store pages that name the city and the services, and third-party mentions. We use citation analytics to see which of those sources show up in each market, then fix the weakest store first. If one store's answers cite a directory with an outdated address, that is a faster win than writing new content.
What we check in crawler logs
Dealer sites often have inventory feeds, dynamic pages and plugins. We look for GPTBot, OAI-SearchBot, ChatGPT-User and the other AI bots hitting the pages we care about, then for error responses and blocked paths. A service page the bots cannot read is a service page the engines cannot cite.
Reporting to the group
Each store manager gets their market's view: visibility, who is cited, and two actions. The group sees a rollup by market. Dashboards in the platform can be shared as PDF reports, and Action Board items can be assigned to named people, so each store owns its list.
Limits worth stating
No tool can guarantee that an engine recommends a particular store. Answers vary by run, by engine and by location. We tell groups to read trends over weeks, to expect noisy individual results, and to avoid promising store managers a rank.
Where to start
Pick two markets rather than all of them. Write 20 prompts for each, in real shopper language, with local competitors. Run for four weeks, review the cited sources and crawler logs, fix what you find, and then roll the same structure out to the rest of the group.