Original research · 2026-07 edition

AI SEO Statistics: Saas Company (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in saas company.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

My team is drowning in spreadsheets for inventory tracking, what kind of software should I look for to automate this?
Is it cheaper to build a custom internal portal or just pay for a monthly subscription to a pre-made platform?
What security questions should I ask a cloud provider if we handle sensitive HIPAA-compliant data?
How does per-user pricing usually work as a company scales from 10 to 100 employees?
Compare the best project management tools specifically for architectural firms with under 15 people.
Which payroll software handles European tax compliance and multi-currency payments the best for a US company?
What are some red flags in a SaaS company's terms of service regarding data ownership and exit clauses?
I need an email marketing tool that I can set up and send a blast with today without a long verification process.
Show all 15 questions
How can I tell if a new CRM will actually integrate with my existing tech stack without hiring a developer?
What are the typical implementation fees for enterprise-level ERP software vs small business versions?
Is it worth paying for a dedicated account manager or is the standard support ticket system usually enough?
Which customer feedback platforms have the easiest interface for staff who aren't tech-savvy?
If we stop our subscription, what happens to our historical data and is it easy to export into a CSV?
How do I justify the cost of a high-end automation tool to my CFO based on time saved?
What are the pros and cons of choosing a niche industry-specific software versus a general-purpose platform?

Model by model

16-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about saas company buyers.

Behavior rates across 15 saas company buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional20%20%13%67%
Suggests DIY first20%13%7%80%
Names specific providers40%53%67%60%
Gives price or cost info33%27%40%60%
Tells to check reviews0%7%0%93%
Tells to verify credentials13%7%0%87%
Mentions case studies / portfolio20%0%0%80%
Mentions local proximity0%0%0%100%
Gives selection criteria47%60%53%60%
Warns about red flags13%20%7%87%
Asks a clarifying question40%40%7%47%
Recommends multiple quotes13%0%0%87%

By model

How each assistant handled Saas Company questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same saas company questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 20% (ChatGPT) down to 13.3% (Gemini), a 7-point gap on an identical question set.

Across the 15 saas company answers it produced, ChatGPT recommended hiring a professional in 20% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 40% of answers (about 1.6 distinct providers per answer) and included price or cost information 33.3% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 13.3%, averaging 709 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 saas company answers it produced, Claude recommended hiring a professional in 20% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 53.3% of answers (about 2.5 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 328 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 saas company answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 66.7% of answers (about 2.1 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 241 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a saas company buyer to a professional (20%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 709 words on average. Specific providers were named most often by Gemini (66.7%) — even there, roughly one answer in 1 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 16.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a saas company buyer happens to ask matters most:

  • Asks a clarifying question: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Names a specific provider: from 40% (ChatGPT) to 66.7% (Gemini) — a 27-point spread.
  • Mentions case studies or portfolio: from 0% (Claude) to 20% (ChatGPT) — a 20-point spread.
  • Suggests a DIY approach first: from 6.7% (Gemini) to 20% (ChatGPT) — a 13-point spread.
  • Gives price or cost information: from 26.7% (Claude) to 40% (Gemini) — a 13-point spread.

The widest single gap — asks a clarifying question, 33 points — means a saas company buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the saas company market.

Where they agree

The points of near-consensus in Saas Company.

On other behaviors the three models move almost in lockstep — the points of near-consensus for saas company, where all three landed within a few points of each other:

  • Mentions local proximity: 0% across all three models.
  • Recommends hiring a professional: 13.3%–20% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).
  • Suggests a DIY approach first: 6.7%–20% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions local proximity" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (46.7%).

Every behavior, measured

All twelve coded behaviors for Saas Company, averaged across the three models.

The behaviors AI models reproduce most often for saas company are names a specific provider (53.3% on average), gives selection criteria (53.3%) and gives price or cost information (33.3%); the rarest are mentions local proximity (0%), tells the buyer to check reviews (2.2%) and recommends multiple quotes (4.4%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Names a specific provider: 53.3% on average (ChatGPT 40%, Claude 53.3%, Gemini 66.7%) — a 27-point spread.
  • Gives selection criteria: 53.3% on average (ChatGPT 46.7%, Claude 60%, Gemini 53.3%) — a 13-point spread.
  • Gives price or cost information: 33.3% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 40%) — a 13-point spread.
  • Asks a clarifying question: 28.9% on average (ChatGPT 40%, Claude 40%, Gemini 6.7%) — a 33-point spread.
  • Recommends hiring a professional: 17.8% on average (ChatGPT 20%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Suggests a DIY approach first: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 13.3%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Tells the buyer to verify credentials: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 6.7% on average (ChatGPT 20%, Claude 0%, Gemini 0%) — a 20-point spread.
  • Recommends multiple quotes: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.
  • Tells the buyer to check reviews: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Mentions local proximity: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the saas company buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the saas company buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.2% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 13.3%.

On structuring the decision, a selection-criteria checklist showed up in 53.3% of answers on average and a recommendation to gather multiple quotes in 4.4%. The single least-reproduced protective signal for saas company is "tells the buyer to check reviews" at 2.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Saas Company providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 saas company answers, a specific provider was named in 53.3% of responses on average — roughly 2.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for saas company: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Saas Company questions cover.

The 15 questions behind every percentage on this page were drawn from real saas company (technology / SaaS; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact saas company question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific saas company question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →