Original research · 2026-07 edition

AI SEO Statistics: Moving 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 moving company.

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

Is it actually cheaper to rent a truck and move myself vs hiring a full-service moving company for a 2-bedroom apartment?
What kind of insurance should a reputable moving company have to cover my expensive electronics if they get broken?
How much does a local move typically cost for a 3-bedroom house if I am only moving 15 miles away?
Do movers charge extra for things like stairs, long hallways, or packing materials, or is it usually a flat rate?
I need to move out by this weekend because my lease is up, are there any last-minute movers that won't charge double?
What are some red flags I should look out for when reading reviews for a moving company online?
What is the difference between a binding estimate and a non-binding estimate when getting moving quotes?
Who should I hire to move a baby grand piano and a heavy gun safe without scratching my hardwood floors?
Show all 15 questions
How can I verify if a moving company is officially licensed for interstate travel across state lines?
If I hire movers, do they expect me to have everything in boxes already, or can they pack the kitchen for me?
Do I need to tip my movers, and if so, what is the standard percentage or flat amount per person?
What happens if a moving company loses a box during a long-distance move; how do I file a claim?
I'm downsizing to a senior living facility; are there movers who specialize in helping elderly people sort and pack?
Can a moving company hold my furniture in a warehouse for two weeks while I wait for my new house to close?
Why is one moving quote $500 cheaper than the others for the exact same inventory list?

Model by model

27-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 moving company buyers.

Behavior rates across 15 moving company buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional53%47%40%73%
Suggests DIY first27%13%7%67%
Names specific providers7%20%7%87%
Gives price or cost info33%33%40%60%
Tells to check reviews47%20%7%47%
Tells to verify credentials40%33%7%53%
Mentions case studies / portfolio7%7%0%93%
Mentions local proximity33%13%7%53%
Gives selection criteria53%53%40%33%
Warns about red flags33%27%20%60%
Asks a clarifying question40%47%0%40%
Recommends multiple quotes40%20%0%53%

By model

How each assistant handled Moving Company questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same moving company questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 53.3% (ChatGPT) down to 40% (Gemini), a 13-point gap on an identical question set.

Across the 15 moving company answers it produced, ChatGPT recommended hiring a professional in 53.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.6 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 33.3%, and told the buyer to verify credentials in 40%, averaging 468 words per answer. On the remaining cues it told the buyer to check reviews in 46.7%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 40%.

Across the 15 moving company answers it produced, Claude recommended hiring a professional in 46.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 20% of answers (about 0.1 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 33.3%, averaging 285 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 moving company answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.5 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 281 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 6.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a moving company buyer to a professional (53.3%) and Gemini the least (40%). ChatGPT produced the longest answers, at 468 words on average. Specific providers were named most often by Claude (20%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 26.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a moving company buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 46.7% (Claude) — a 47-point spread.
  • Tells the buyer to check reviews: from 6.7% (Gemini) to 46.7% (ChatGPT) — a 40-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Mentions local proximity: from 6.7% (Gemini) to 33.3% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 47 points — means a moving 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 moving company market.

Where they agree

The points of near-consensus in Moving Company.

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

  • Gives price or cost information: 33.3%–40% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Recommends hiring a professional: 40%–53.3% across all three (a 13-point spread).
  • Names a specific provider: 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 case studies or portfolio" (identical coding in 93.3% of questions) and least consistently on "gives selection criteria" (33.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for moving company are gives selection criteria (48.9% on average), recommends hiring a professional (46.7%) and gives price or cost information (35.5%); the rarest are mentions case studies or portfolio (4.5%), names a specific provider (11.1%) and suggests a DIY approach first (15.6%). 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:

  • Gives selection criteria: 48.9% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 40%) — a 13-point spread.
  • Recommends hiring a professional: 46.7% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 40%) — a 13-point spread.
  • Gives price or cost information: 35.5% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 40%) — a 7-point spread.
  • Asks a clarifying question: 28.9% on average (ChatGPT 40%, Claude 46.7%, Gemini 0%) — a 47-point spread.
  • Tells the buyer to verify credentials: 26.7% on average (ChatGPT 40%, Claude 33.3%, Gemini 6.7%) — a 33-point spread.
  • Warns about red flags or scams: 26.7% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 20%) — a 13-point spread.
  • Tells the buyer to check reviews: 24.5% on average (ChatGPT 46.7%, Claude 20%, Gemini 6.7%) — a 40-point spread.
  • Recommends multiple quotes: 20% on average (ChatGPT 40%, Claude 20%, Gemini 0%) — a 40-point spread.
  • Mentions local proximity: 17.8% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 6.7%) — a 27-point spread.
  • Suggests a DIY approach first: 15.6% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 6.7%) — a 20-point spread.
  • Names a specific provider: 11.1% on average (ChatGPT 6.7%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Mentions case studies or portfolio: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the moving company buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the moving company buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 24.5% of answers on average. Verifying credentials or certifications appeared in 26.7%. Warning about red flags or scams appeared in 26.7%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 20%. The single least-reproduced protective signal for moving company is "recommends multiple quotes" at 20% 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 Moving Company providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 moving company answers, a specific provider was named in 11.1% of responses on average — roughly 0.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for moving company: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Moving Company questions cover.

The 15 questions behind every percentage on this page were drawn from real moving company (home services; 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 moving 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 moving 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 →