Why Do AI Answers Change Based on User Context, Chat History, and Location?

What makes the same question produce different answers?

Most of these can be controlled in a test. The last one cannot, which is why repeated runs matter.

Why do ChatGPT recommendations change by location, account or query?

Location. When ChatGPT searches the web, results depend on the user's approximate location. For local questions, OpenAI says ChatGPT may share the user's location with trusted third-party providers to give more relevant local information, such as nearby business listings. Recommendations for "near me" and city-specific questions can therefore differ completely between two towns.

Account. ChatGPT can remember details you have saved and refer to past chats, depending on your memory settings. Custom instructions add standing preferences. A marketer who has spent weeks discussing their own company with ChatGPT may see that company mentioned more readily than a stranger would. Temporary Chat never creates or updates memories, and in its default Unpersonalized mode it also ignores saved memories, custom instructions and plugins. Choose Unpersonalized before the first message when testing; the Personalized option uses your memories.

The conversation so far. Within one chat, every earlier message is context. Asking "what are the best CRMs?" after discussing a small agency's needs gives a different answer from asking it cold.

Model and plan. Different ChatGPT models and settings can produce different recommendations, and whether the answer searches the web or relies on training data makes a big difference.

Query. Wording and conditions often change results more than anything above: "best CRM" and "best CRM for a five-person recruitment agency" retrieve different sources and name different tools. How much wording matters compared with the platform is covered in whether buyer phrasing changes AI recommendations more than the platform.

Do Google AI Overviews change based on location, language or user context?

Yes. AI Overviews are part of Google Search and are shaped by the same inputs as the rest of the results page:

• Location changes local businesses, prices, regulations and which sources rank. Local questions can show different businesses a few miles apart.
• Language and country change the sources available and which pages rank for the underlying searches.
• Whether an Overview appears at all varies by query and over time.
• Signed-in state and search settings can add some personalisation for signed-in users, which can be turned off in Google's settings.
• Time matters, because Overviews are generated from current results and change as those results change.

Because Overviews often use several related searches to build a response, a small difference in location or language can change several of the underlying results at once. To check Overviews reliably, see how to measure brand visibility in Google AI Overviews.

Which factors matter most for a business?

It depends on what you sell:

• Local businesses: location dominates. Test from the neighbourhoods you serve, not from your office.
• International brands: country and language dominate. Test each market separately; see how to track AI recommendations across countries.
• B2B and SaaS: wording and conditions dominate. Company size, budget and integrations change shortlists more than location.
• Everyone: randomness and model differences mean a single answer is never enough.

Personal memory mainly matters for testing. Real buyers each have their own history, which you cannot see or control, but you can avoid letting your own history distort your tests.

What does a context test look like in practice?

A simple experiment shows how much each factor matters for your own questions. Take five to ten important questions and run each three times under each of four set-ups:

1. a clean session with no location in the question;
2. a clean session with your main city named in the question;
3. a clean session with a second city named;
4. your normal account, with its memory and history.

Compare the businesses named in each set. If set-ups 2 and 3 name different businesses on most of the questions, location drives your category and you need per-area tracking. If set-up 4 names you far more often than set-up 1, your own history has been flattering your checks, and every past result gathered that way should be re-run cleanly.

How should you test so personalisation does not mislead you?

1. Use clean sessions: ChatGPT's Temporary Chat in Unpersonalized mode, a logged-out session, or an account used only for testing, with memory off and no custom instructions.
2. Start a new chat for every question, so earlier turns do not carry over.
3. Set and record the location, by testing from the right place or with the tool's location setting, and include the place name in local questions.
4. Fix the language and the exact wording of each question.
5. Record the model and whether search was used.
6. Run each question several times and report counts, such as "named in 3 of 5".
7. Compare like with like: the same settings each month, so changes reflect the engines rather than your setup.

When you want to see what a particular type of buyer sees, add their context to the question itself ("I run a 10-person accounting firm in Leeds...") rather than relying on your own account history.

What does personalisation mean for your GEO strategy?

You cannot optimise for each user's memory and history, and you do not need to. The inputs you can influence are the ones every user's answer draws on:

• Location-specific pages and listings, so local answers in each area have accurate sources about you.
• Content for each buyer condition, such as size, budget, sector and integrations, so personalised questions still find a matching page.
• Broad, consistent third-party evidence, so different retrieval paths lead to the same picture of you.
• Accurate facts everywhere, so whichever source a personalised answer draws on, it is right.

What do people ask about personalised AI answers?

Does ChatGPT remember my previous chats?

It can, depending on your memory settings, which include saved memories and referencing past chats. You can turn these off, delete memories, or use an unpersonalized Temporary Chat.

Why do my colleague and I get different answers to the same question?

Different locations, accounts, memories, models and plain randomness. Compare in clean sessions with the same settings.

Can I see what AI tells customers in another city?

Yes, approximately. Include the city in the question and test with a location set to that area, and record the method so results are comparable.

Do AI answers personalise as much as social media feeds?

Generally less. Most variation comes from location, wording and randomness rather than a detailed profile of the user, though memory features are increasing the role of personal context.