Track Business Recommendations Across AI Tools

When a potential customer asks ChatGPT for a local recommendation, does your business make the list? A useful answer takes more than searching for your own name once.

To track business recommendations across AI tools, save a small set of realistic customer questions, repeat them under consistent conditions, and keep the answers. Record whether your business is recommended, which competitors appear, and which sources are shown. Compare those observations over time.

This guide gives local business owners and marketing teams a method they can start in a spreadsheet. It measures the answers you actually capture; it does not measure every customer's experience or promise enquiries, bookings, or sales.

1. Start with questions a customer would ask

Choose one service and one market first. Write questions that describe the customer's need without giving the assistant your business name. Otherwise, you are testing recognition rather than whether the business gets discovered.

For example, a real estate agent serving first-time condo buyers could save these questions:

• “Which real estate agents help first-time condo buyers in Toronto?”
• “Who can help me compare condos in Liberty Village?”
• “Which Toronto buyer's agents explain condo fees and the buying process?”

These are suggested test prompts, not verified popular searches. Replace the place and service with areas you genuinely cover. Use questions from enquiries and sales conversations when you have them.

The same approach works for other local services:

Business	Example discovery question
Dental practice	“Which dentists offer Saturday appointments in North York?”
Law firm	“Which Toronto law firms handle employment disputes for employees?”
Med spa	“Which Toronto clinics offer consultations for acne scar treatments?”
Roofing business	“Who repairs leaking flat roofs in Scarborough?”

Keep branded checks in a separate group. “What does my business offer?” can uncover incorrect facts, but it should not be counted alongside questions where the assistant has to choose a provider without a name cue.

For a real estate business, also decide whether you are tracking an individual agent, a team, or the brokerage. Record a brokerage mention separately when the individual agent you are measuring is not named.

2. Keep each platform and test condition visible

Choose the platforms relevant to your customers and practical for your team to check. You can begin with a small set rather than checking every available assistant. The important part is repeating the same method.

For each run, record:

• The exact question and stated city or neighbourhood.
• The platform, date, time, and model or mode when visible.
• Whether the check was manual or made through a monitoring provider.
• Whether web search was used, if you can tell.
• Sign-in state, location settings, and any known personalization.
• The complete answer, visible sources, and named competitors.

Use a fresh conversation for manual checks to reduce the influence of earlier messages. Keep other settings consistent and note anything you cannot control. A question containing “Toronto” specifies the requested market; it does not prove the test was run from Toronto.

OpenAI's ChatGPT search documentation explains that search can use approximate location and relevant saved memories. That makes location and account context useful information to keep beside a local recommendation result.

Treat Google AI Overviews as a separate test

For Google, save the exact search query and record whether an AI Overview appeared. If it did, capture its answer and sources separately from ads, Maps results, and ordinary search listings.

Google's documentation says AI Overviews do not appear for every query. “No AI Overview shown” is therefore a distinct result, not evidence that an Overview recommended a competitor instead.

An API or monitoring-provider result also should not be presented as a screenshot of what every person sees in the consumer app. Label the collection method and compare like with like.

3. Separate recommendations, mentions, and citations

Record whether the business is named, then classify the wording around it. Track citations separately: a response can recommend a business and cite its website in the same answer.

What the answer does	How to record it
Suggests the business as a suitable option	Recommendation
Includes it among suitable providers in a shortlist	Recommendation, with shortlist context
Names it without suggesting the reader consider it	Mention only
Warns against choosing it	Negative mention; preserve the warning
Does not name it	Absent from this captured answer

Separately record any visible link to the business's website or profile, even if the business is not recommended. If the wording is ambiguous, mark it “unclear” and retain the answer for review instead of forcing a positive score.

Do not call a business “ranked first” just because it appears at the top of an unordered or alphabetical list. Preserve explicit ranking language when it exists; otherwise, describe the result as a mention, recommendation, or shortlist.

An example tracking record

The record below is fictional and only demonstrates the logging method. It is not a RankSimply scan or a customer result. Agent A, Agent B, and Agent C are example labels.

Field	Illustrative entry
Prompt	“Which real estate agents help first-time condo buyers in Toronto?”
Platform and method	ChatGPT; manual check with web search
Test date	12 October 2026; illustrative date
Context	Fresh chat; signed in; Toronto stated in prompt; account location not controlled
Business being tracked	Agent A
Captured answer	“Consider Agent B or Agent C for help with your condo search.”
Classification	Agent A absent; Agent B and Agent C recommended
Visible sources	None shown in this fictional answer
Next step	Check whether Agent A's public pages clearly describe buyer services and areas served

For real checks, retain the full response and copy the exact source URLs. Open those sources before relying on them: a citation does not establish that a page supports every claim in the answer.

4. Calculate a rate you can explain

For each platform and a fixed group of discovery questions, use:

Recommendation rate = usable captured answers recommending the business ÷ all usable captured answers × 100.

Count each captured answer once per business, even if it names that business several times. Count a suitable shortlist appearance as a recommendation under this method. Use the same rule for competitors.

Here is an arithmetic example, not a measured result: if a business is recommended in 3 of 10 usable captured answers, its recommendation rate is 30% for that sample. Another business could also appear in those answers, so competitor rates do not have to add up to 100%.

Keep these cases visible:

• A completed answer names no businesses: include it in the denominator and record no recommendation.
• A failed request or unavailable platform: record it as unmeasured; do not turn it into a zero.
• A refusal or unusable response: label it separately and report how many you excluded.
• No Google AI Overview appears: record that outcome separately. Report recommendations among captured Overviews alongside how often an Overview appeared across successful Google checks.

Always show counts with percentages. Three recommendations out of ten answers is easier to interpret than “30% visibility” without a sample size. If some checks fail, report the missing checks and avoid comparing a smaller, different sample as if nothing changed.

Keep platform results separate. Adding more checks on one platform can move a blended percentage even when the underlying results on each platform remain unchanged.

5. Turn a missing recommendation into something worth fixing

The captured answer tells you what appeared, not why the platform chose it. Use it to identify a question worth investigating.

Start with inaccurate information. If an answer gives the wrong opening hours, service area, or business identity, check the visible source, your website, and relevant business profiles. Correct what you control and request corrections elsewhere where appropriate.

Next, look for gaps that affect a real customer's decision:

• Missing service detail: explain the services you actually provide and how to enquire.
• Unclear location coverage: state the areas you serve accurately.
• Weak evidence for a claimed specialty: add support you can substantiate, such as relevant experience or a permitted customer example.
• Conflicting business details: reconcile the website and important profiles.

For an agent targeting first-time condo buyers, the useful next step might be explaining the buying process and the agent's actual experience with that audience. Publishing dozens of near-identical neighbourhood pages would not resolve missing evidence of expertise.

Google's local-ranking guidance supports keeping Business Profile information complete and accurate. That guidance concerns Google local results; it is not proof that the same factors determine a ChatGPT recommendation.

Do not buy a special schema package on the assumption that Google AI Overviews require it. Google's AI-features documentation says no special schema is required. Clear, accessible information is a sensible improvement without promising that one edit will secure an AI recommendation.

6. Repeat the checks and keep a change log

Choose a cadence you can maintain. Weekly checks can be a starting point for a small prompt set, but they are a working schedule, not a guarantee of when visibility should improve.

Keep the original questions. If your services or target market change, add a new group and label it rather than quietly replacing the baseline.

Beside each recheck, record what changed on the business's website or profiles, when it changed, and the URL affected. A later improvement is an observation to investigate; it does not prove that your edit caused it. Avoid repeatedly rerunning a prompt until you get a favourable answer and reporting only that result.

Measure enquiries and bookings separately. A recommendation rate tells you about the sampled answers. It does not tell you how many customers saw those answers or contacted the business.

Start with one check, then build a baseline

If you want a first observation, run RankSimply's free AI visibility report. It checks one commercial recommendation prompt for your business category and city in ChatGPT, and shows the returned answer and named competitors. It is a starting point, not a complete cross-platform audit.

For ongoing monitoring, RankSimply's features include selected-prompt tracking across AI platforms and competitor visibility reporting. Use the method above to decide which questions to track and what each result means for your business.

Frequently asked questions

Can I do this without paying for a monitoring tool?

Yes. A spreadsheet and saved responses are enough to begin with a small question set. A monitoring tool becomes useful when repeating checks and reviewing evidence across services, locations, and platforms takes too much time.

Can I see what every customer asks ChatGPT?

This method measures responses to the questions you deliberately test. It does not provide access to customers' private conversations or establish the search volume behind each question.

Does being cited mean my business was recommended?

No. A source link and a recommendation are separate observations. Read the surrounding answer before deciding whether the business was suggested as a suitable provider.

What if the result changes on the next check?

Keep both results. Compare the exact questions, collection methods, settings, and dates. A changing answer is a reason to retain a repeatable record, not to discard the less favourable observation.