Original Research • June 2026

Do you have to talk to ChatGPT like a search engine?

No. When the same question is typed as a clean keyword ("best CRM tools") or as a messy, conversational ask ("ok i need a good crm for my small business, what do you actually recommend?"), ChatGPT returns almost identical results — it searches the web at nearly the same rate, shows the same product carousels, and cites listicles at the same rate across four of five search intents. The one exception is local: for "find me a good X near here" questions, phrasing meaningfully changes which sources ChatGPT pulls.

By Sebastian Pinzon Duran, Head of Discovery, AIVO · Independent research, no brand paid for inclusion

Companion study: Are listicles the AI-search silver bullet?

Research scope

276
ChatGPT queries
46
Paired prompts
2
Phrasings each
5
Search intents
n=3
Runs per prompt
4 of 5

search intents where keyword and conversational phrasing returned the same sources

56%

of domains cited for a keyword reappear for its conversational version

36%

source overlap for local queries — the lowest of any intent

276

ChatGPT queries (46 paired prompts × 2 phrasings × n=3)

6.5%

of answers showed a product carousel — identical for both phrasings

Executive Summary

Do you have to talk to ChatGPT like a search engine?

The premise was simple and widely assumed: messy, conversational prompts should pull a different, more scattered set of sources than clean SEO keywords. To test it, we wrote 46 questions twice — once as a keyword, once the way a real person types — and ran both three times each (276 ChatGPT queries). Mostly, the wording didn't matter.

The Bottom Line

Typing a keyword or asking ChatGPT in a full, messy sentence returns almost the same sources. ChatGPT searched the web at nearly the same rate (92.8% vs 89.9%), showed product carousels at exactly the same rate (6.5%), cited listicles at the same rate on four of five intents, and reused about 56% of the exact domains across phrasings. The one real exception is local: keyword phrasing pulls directories and best-of lists while conversational phrasing pulls individual business websites (36% source overlap, ~15-point listicle drop). You optimize for the intent behind the question, not the phrasing — and for local, you win twice.

Q1

Do you have to phrase prompts like keywords to show up in ChatGPT?

Finding: No. Asking as a clean keyword ("best CRM tools") or a messy sentence ("ok i need a good crm for my small business, what do you actually recommend?") returned almost the same sources. ChatGPT searched the web at nearly the same rate (92.8% vs 89.9%), showed product carousels at the same rate (6.5%), and cited listicles at the same rate across four of five intents.

Insight: ChatGPT normalizes a messy ask back toward the same set of trusted sources — wording is not the lever most brands think it is.

Implication: Optimize for the intent behind the question, not the exact wording.

Confidence: high
Q2

For the same question, does ChatGPT cite the same sources?

Finding: More than half the time. About 56% of the exact domains cited for a keyword reappear for its conversational twin, and overlap is high everywhere except local (68% navigational, 64% informational, 55% commercial-investigation, 55% transactional, 36% local).

Insight: ChatGPT converges on the domains it trusts for a question regardless of wording; local is the consistent outlier.

Implication: Earned placement in trusted sources carries across phrasings — except for local, where you must win twice.

Confidence: high
Q3

Why is local the exception?

Finding: Keyword phrasing ("best dentist in Miami") reads as "rank these for me" and pulls directories and best-of lists; conversational phrasing ("whos a good dentist in miami?") reads as "who's good?" and pulls individual business websites. Local source overlap is just 36% and listicle citations fall about 15 points.

Insight: The more specific and high-stakes the local need ("emergency dentist near downtown Chicago"), the less phrasing matters.

Implication: Local brands need both: presence in trusted directories and a strong, well-structured website ChatGPT can recommend directly.

Confidence: high
Q4

Did phrasing matter as much as expected?

Finding: No. The premise was that messy prompts would pull a different, more scattered set of sources than clean keywords. Mostly they didn't: web-search rate, carousel rate, and listicle-by-intent were near-identical, and the core "phrasing pulls different sources" hypothesis was mostly rejected.

Insight: The intent map from the listicle study holds whether people search like a robot or talk like a human.

Implication: Stop optimizing copy for keyword phrasing; build for the intent and the trusted sources behind it.

Confidence: high
Start here

Two ways to ask the same question

We took 46 questions and wrote each one twice: once as a clean search keyword, once the messy way a real person types into a chat box. Same intent, same category — only the wording changed. Then we ran both, three times each, and compared what ChatGPT cited.

Keyword phrasing

  • best CRM tools
  • emergency dentist near downtown Chicago
  • best noise cancelling headphones
  • HubSpot CRM pricing

Conversational phrasing

  • ok i need a good crm for my small business, what do you actually recommend?
  • i need an emergency dentist near downtown chicago asap
  • which headphones actually block out noise the best?
  • how much is hubspot crm gonna run me?
Finding #1

Web search and product carousels

Two behaviors set the stage for everything else: whether ChatGPT runs a live web search, and whether it renders a product carousel. Both are effectively unchanged by phrasing.

Ran a live web search

92.8% → 89.9%

Keyword vs conversational. A 3-point difference, well within run-to-run noise. ChatGPT decides to search based on the question's intent, not its wording.

Showed a product carousel

6.5% = 6.5%

Identical for both phrasings. The shopping carousel appears for the same physical-product questions whether you type keywords or a sentence.

The baseline

Before looking at which sources get cited, the mechanics are the same. ChatGPT does not treat a conversational user differently from a keyword user in whether it searches or shops — it normalizes the messy ask back to the same behavior.
Finding #2

Listicle citations by intent and phrasing

For each intent we measured how often the answer cited at least one listicle, best-of, comparison, or directory source, using one classifier applied identically to both phrasings (n=3 per prompt). Keyword and conversational track each other almost exactly — except local.

KeywordConversational

Commercial-investigation

"best X", "X vs Y"

Keyword
100%
Conversational
100%

Transactional

"buy X", "X price"

Keyword
50%
Conversational
50%

Informational

"how does X work"

Keyword
46%
Conversational
46%

Local

"best X in [city]" — the exception

Keyword
76%
Conversational
61%

Navigational

"X login", "X official site"

Keyword
0%
Conversational
0%

What it means

The intent map from our listicle study holds whether people search like a robot or talk like a human. A "best X" buyer gets a roundup either way; a navigational user gets none either way. You optimize for the intent, not the phrasing. Local is the single place where wording shifts the result — and it drops about 15 points when the ask turns conversational.
Finding #3

Source overlap across phrasings

For each paired question we measured how many of the domains ChatGPT cited for the keyword version also appeared for the conversational version. The higher the bar, the more interchangeable the two phrasings are.

Navigational
"X login", "official site"
68%
Informational
"how does X work"
64%
Commercial-investigation
"best X", "X vs Y"
55%
Transactional
"buy X", "X price"
55%
Local
"best X in [city]"
36%

The pattern

Source overlap is the third measure pointing the same way. For most intents, the two phrasings land on a majority-shared set of sources — ChatGPT converges on the domains it trusts for that question regardless of wording. Local is the consistent outlier at 36%, the same intent that moved on the listicle measure. Everything else is largely phrasing-proof.
Finding #4

Local: where phrasing matters

Keyword phrasing reads as "rank these for me" and pulls directories and best-of lists. Conversational phrasing reads as "who's good?" and pulls individual business websites instead. Two paired examples from the study show the split clearly — same place, same need, different wording, different sources.

Keyword: "best dentist in Miami"

ranking framing → directories & lists

  • reddit.com
  • bestdentist.reviews
  • ratemds.com

Conversational: "whos a good dentist in miami?"

advice framing → individual practices

  • feelgooddentistry.com
  • drpattymiami.com
  • galgueradental.com

Keyword: "best gyms in Austin"

ranking framing → city directories

  • austinot.com
  • atly.com
  • discovergyms.com

Conversational: "what are the best gyms in austin?"

mixed → individual gyms surface

  • trustanalytica.org
  • goldsgym.com
  • worldgym.com

The local takeaway

For local, you have to win twice. The keyword ask rewards directory and best-of presence; the conversational ask rewards your own well-structured website that ChatGPT can recommend directly. One urgent, specific pair in the study ("emergency dentist near downtown Chicago") returned nearly identical sources both ways — so the more specific and high-stakes the local need, the less phrasing matters.
Hypothesis Scorecard

Going in, we thought phrasing would matter more

Going in, we thought phrasing would matter more. The premise was that messy, conversational prompts would pull a different, more scattered set of sources than clean keywords. Mostly, they didn't.

Conversational phrasing pulls materially different sources than keywords

the core premise of the follow-up

Mostly rejected

ChatGPT searches the web at the same rate regardless of phrasing

92.8% keyword vs 89.9% conversational

Confirmed

Product / shopping behavior is unaffected by phrasing

6.5% carousel rate, identical both ways

Confirmed

Listicle citation by intent holds across both phrasings

identical on 4 of 5 intents

Confirmed except local

Local is the most phrasing-sensitive intent

36% source overlap, ~15-point listicle drop

Confirmed
FAQ

Keyword vs conversational: Common Questions

Methodology

How We Built This Study

A paired design — every question written twice, keyword and conversational — built to isolate the effect of phrasing alone.

Research design

  • •46 questions, each written two ways: keyword and conversational
  • •Paired 1:1 — same intent, category, and place; only wording differs
  • •n=3 runs per prompt = 276 ChatGPT queries
  • •US market, English

What we measured

  • •ChatGPT only — one engine, fewer variables
  • •Whether the answer ran a web search and showed a product carousel
  • •Listicle citation rate by intent
  • •How many cited domains the two phrasings share

Classification

  • •The same listicle definition (best-of, comparison, directory) used in our prior study
  • •One classifier applied identically to both phrasings, so the comparison is like-for-like
  • •Conservative throughout — reported rates are floor estimates
  • •Keyword figures align with the published listicle study

Independence

  • •Independent AIVO research
  • •No brand paid for inclusion or was notified before publication
  • •Reflects ChatGPT behavior at the time of collection
  • •Results are subject to change as models update

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