Original research · 2026-07 edition

AI SEO Statistics: Home Furnishing (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

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The question bank

The questions we tested — sampled from real buyer journeys in home furnishing.

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

What is the average cost to hire an interior designer for a single room redesign?
Is it cheaper to reupholster a high-quality vintage sofa or buy a brand new one?
How do I find a local woodworker who can build a custom walnut dining table?
What are the red flags I should look for when hiring a professional furniture assembly service?
Do I need an interior designer or an interior decorator for a kitchen layout change?
How much should I expect to pay for white-glove delivery and installation of a bedroom set?
What questions should I ask a custom cabinet maker before signing a contract?
I have a $10,000 budget to furnish a 2-bedroom apartment, can a professional designer work with that?
Show all 40 questions
How do I vet a furniture restoration expert for an antique heirloom piece?
Is it worth paying for a professional floor plan consultation before buying large furniture?
What is the typical lead time for custom-made upholstered sectionals right now?
Can I hire someone to just help me pick out paint colors and window treatments?
What kind of insurance should a home staging company have if I'm hiring them to sell my house?
Why do some interior designers charge hourly while others charge a flat fee?
How can I tell if a custom furniture shop is actually using solid wood or just veneers?
I need to mount a very heavy mirror and a gallery wall, what kind of professional service do I search for?
What are the pros and cons of hiring an online e-design service versus an in-person designer?
Should I hire a professional organizer before or after I buy new closet systems?
How do I coordinate multiple furniture deliveries to arrive on the same day for a new house?
What is a reasonable deposit percentage for a custom-built entertainment center?
Are there designers who specialize in kid-friendly and pet-proof luxury living rooms?
How do I know if a furniture repair tech is qualified to fix a reclining mechanism?
What should be included in a detailed quote for custom built-in bookshelves?
Can a professional stager help me use my existing furniture to make my home look better for photos?
Is it better to hire a general contractor or a specialized installer for a walk-in closet?
What happens if the custom furniture I ordered doesn't fit through my apartment hallway?
How do I check the portfolio and references of a freelance furniture designer?
What are the hidden costs of ordering furniture from overseas that requires local assembly?
I'm looking for a sustainable and non-toxic interior designer, how do I find one in my area?
How much does it cost to have a professional measure my house for a full furniture overhaul?
What is the etiquette for tipping furniture delivery and installation teams?
Can an interior designer get me better trade pricing on high-end furniture brands?
What's the difference between a custom furniture maker and a furniture refinisher?
I need a nursery designed and furnished in under a month, who can handle a quick turnaround?
How do I resolve a dispute if my custom table arrives with a different finish than I ordered?
Is it standard for designers to take a commission on the furniture they recommend?
What kind of professional can help me maximize a very small studio apartment with multi-functional furniture?
How do I find a craftsman who specializes in mid-century modern furniture restoration?
What are the benefits of hiring a local lighting consultant for a dark living room?
Should I buy my furniture first or hire a designer to create a 3D rendering of the space?

Model by model

24-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 home furnishing buyers.

Behavior rates across 40 home furnishing buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional80%60%63%73%
Suggests DIY first15%15%3%80%
Names specific providers10%20%35%60%
Gives price or cost info23%28%38%60%
Tells to check reviews10%15%8%78%
Tells to verify credentials20%13%5%83%
Mentions case studies / portfolio30%13%5%55%
Mentions local proximity30%30%23%60%
Gives selection criteria43%45%28%43%
Warns about red flags18%8%8%78%
Asks a clarifying question50%58%0%28%
Recommends multiple quotes13%13%0%78%

By model

How each assistant handled Home Furnishing questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same home furnishing questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 60% (Claude), a 20-point gap on an identical question set.

Across the 40 home furnishing answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 22.5% of the time. ChatGPT asked a clarifying question before answering in 50% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 20%, averaging 536 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 30%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 home furnishing answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 20% of answers (about 0.7 distinct providers per answer) and included price or cost information 27.5% of the time. Claude asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 12.5%, averaging 279 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 home furnishing answers it produced, Gemini recommended hiring a professional in 62.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 35% of answers (about 1.4 distinct providers per answer) and included price or cost information 37.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 281 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 22.5%; a selection-criteria checklist appeared in 27.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a home furnishing buyer to a professional (80%) and Claude the least (60%). ChatGPT produced the longest answers, at 536 words on average. Specific providers were named most often by Gemini (35%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 23.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a home furnishing buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 57.5% (Claude) — a 58-point spread.
  • Names a specific provider: from 10% (ChatGPT) to 35% (Gemini) — a 25-point spread.
  • Mentions case studies or portfolio: from 5% (Gemini) to 30% (ChatGPT) — a 25-point spread.
  • Recommends hiring a professional: from 60% (Claude) to 80% (ChatGPT) — a 20-point spread.
  • Gives selection criteria: from 27.5% (Gemini) to 45% (Claude) — a 18-point spread.

The widest single gap — asks a clarifying question, 58 points — means a home furnishing 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 home furnishing market.

Where they agree

The points of near-consensus in Home Furnishing.

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

  • Tells the buyer to check reviews: 7.5%–15% across all three (a 8-point spread).
  • Mentions local proximity: 22.5%–30% across all three (a 8-point spread).
  • Warns about red flags or scams: 7.5%–17.5% across all three (a 10-point spread).
  • Suggests a DIY approach first: 2.5%–15% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to verify credentials" (identical coding in 82.5% of questions) and least consistently on "asks a clarifying question" (27.5%).

Every behavior, measured

All twelve coded behaviors for Home Furnishing, averaged across the three models.

The behaviors AI models reproduce most often for home furnishing are recommends hiring a professional (67.5% on average), gives selection criteria (38.3%) and asks a clarifying question (35.8%); the rarest are recommends multiple quotes (8.3%), warns about red flags or scams (10.8%) and tells the buyer to check reviews (10.8%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Recommends hiring a professional: 67.5% on average (ChatGPT 80%, Claude 60%, Gemini 62.5%) — a 20-point spread.
  • Gives selection criteria: 38.3% on average (ChatGPT 42.5%, Claude 45%, Gemini 27.5%) — a 18-point spread.
  • Asks a clarifying question: 35.8% on average (ChatGPT 50%, Claude 57.5%, Gemini 0%) — a 58-point spread.
  • Gives price or cost information: 29.2% on average (ChatGPT 22.5%, Claude 27.5%, Gemini 37.5%) — a 15-point spread.
  • Mentions local proximity: 27.5% on average (ChatGPT 30%, Claude 30%, Gemini 22.5%) — a 8-point spread.
  • Names a specific provider: 21.7% on average (ChatGPT 10%, Claude 20%, Gemini 35%) — a 25-point spread.
  • Mentions case studies or portfolio: 15.8% on average (ChatGPT 30%, Claude 12.5%, Gemini 5%) — a 25-point spread.
  • Tells the buyer to verify credentials: 12.5% on average (ChatGPT 20%, Claude 12.5%, Gemini 5%) — a 15-point spread.
  • Suggests a DIY approach first: 10.8% on average (ChatGPT 15%, Claude 15%, Gemini 2.5%) — a 13-point spread.
  • Tells the buyer to check reviews: 10.8% on average (ChatGPT 10%, Claude 15%, Gemini 7.5%) — a 8-point spread.
  • Warns about red flags or scams: 10.8% on average (ChatGPT 17.5%, Claude 7.5%, Gemini 7.5%) — a 10-point spread.
  • Recommends multiple quotes: 8.3% on average (ChatGPT 12.5%, Claude 12.5%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the home furnishing buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the home furnishing buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 10.8% of answers on average. Verifying credentials or certifications appeared in 12.5%. Warning about red flags or scams appeared in 10.8%.

On structuring the decision, a selection-criteria checklist showed up in 38.3% of answers on average and a recommendation to gather multiple quotes in 8.3%. The single least-reproduced protective signal for home furnishing is "recommends multiple quotes" at 8.3% 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 Home Furnishing providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 home furnishing answers, a specific provider was named in 21.7% of responses on average — roughly 0.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for home furnishing: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Home Furnishing questions cover.

The 40 questions behind every percentage on this page were drawn from real home furnishing (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 home furnishing 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 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific home furnishing 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.

40 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 2026-07-06, 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 →