HIGH SALIENCE / RESEARCH / WEBSITE BUILDER TEARDOWN
TEARDOWN 15 · PUBLISHED SEPTEMBER 26, 2026 · DATASET INCLUDED
100 Questions, Fifteenth Category: Website Builders, Where Wix Is Named Most and Squarespace Wins the Industry Questions
Fifteenth category, same method: 100 website builder queries, ChatGPT with web search on, Google's top ten as a control. Every answer was coded by two independent AI readers, who separated the builders an answer picks as its own verdict from the ones it only describes. Wix is named most and picked most, in 62 of 100 answers, but Squarespace is the pick in 15 of the 18 questions that name a kind of business. The model reads mostly the builders' own websites, while the forums and review sites Google ranks most for these questions go uncited.
01Method
Same query structure, same coding, one new category.
Queries: 100 website builders queries built from the same templates as the earlier teardowns: 30 category, 30 comparison, 20 alternative and 20 recommendation. The full list is in the dataset.
Surface: ChatGPT with web search forced on, logged out, United States, English, via the DataForSEO scraper, one run per query, collected September 26, 2026.
Control: Google's top ten organic results for the same queries in the same window, via the DataForSEO SERP API at depth 20 and truncated to the first ten organic results.
Coding: every answer was coded by two independent AI readers (Claude agents working blind from a written protocol), with a third settling disagreements. They were not people. Each brand named was coded picked (the answer's own verdict: "my pick", "choose X if", a shortlist it tells you to act on, the #1 of its ranking), recommended (assigned to a stated case or fit, such as "best for small teams", without being the answer's verdict), listed (named as an option, no fit given), passing (named but not offered as an option) or anchor (the brand being replaced in an "alternatives" query), against a 52-brand dictionary fixed after collection, before the final coding run (every change is listed in DICTIONARY_CHANGES.md), under codebook v2.0. "Recommended" in the tables includes picked brands. The two readers agreed on 99.5% of brand codes in this teardown (100.0% at the recommended level). Cited URLs were deduplicated to their domain and classed as first-party or third-party. The third reader decided 2 of the 409 brand codes in this teardown. A rules-based first pass (the codebook v1.6 rules) agreed with the final codes on 95.6 percent at the recommended level; only the reader codes are published. The readers also coded one brand mention that no dictionary alias matched (named by meaning or only by a web address); those rows are in the dataset.
Collection note: the ChatGPT answers and the Google controls were collected on September 26, 2026 through the DataForSEO standard queue, between about 8:15 and 8:30 PM US Mountain time, with raw responses saved exactly as returned; the raw files carry UTC timestamps, early September 27. No task failed and none was re-run. One answer (Duda versus Wix) came back in Spanish and is coded as returned. After collection, two aliases were added: Framer and Ghost now also match their addresses, framer.com and ghost.org, because the model sometimes names them only that way. Divi and HubSpot are never named, and where an answer names WordPress only as the platform Elementor runs on, the readers coded it as a passing mention. Every google.com citation counts as Google Sites' own site, although two of the three are other Google pages. Eight aliases that are also ordinary words or names (Framer, Ghost, Square, Hugo and others) match only when capitalized. Both dictionaries and the list of changes ship with the dataset.
One run per query, so this is a teardown, not the benchmark. Frequencies describe this window only. Absence means not observed in this sample, never zero visibility.
02Findings
Three builders lead, Squarespace takes the industry questions, and two reading lists barely overlap.
| Brand | Appears in | Recommended in | Picked in | Picked share of appearances |
|---|---|---|---|---|
| Wix | 70 | 70 | 62 | 89% |
| Squarespace | 65 | 64 | 56 | 86% |
| WordPress | 57 | 56 | 47 | 82% |
| Webflow | 43 | 42 | 36 | 84% |
| Shopify | 31 | 31 | 24 | 77% |
| Framer | 25 | 25 | 20 | 80% |
| Carrd | 14 | 14 | 9 | 64% |
| Hostinger | 12 | 12 | 10 | 83% |
Each linked brand has its own page: how ChatGPT recommends every brand in this dataset, with positioning labels, head-to-head results and sources.
Out of 100 answers, codebook v2.0. "Picked" means the answer itself chose the brand as its verdict, overall or for a case. "Recommended" means the answer assigned the brand to a stated case or fit, and includes picked brands. "Appears" adds brands named as an option with no fit. The brand being replaced in an "alternatives" query is excluded from all columns.
Three builders lead, and the pick is what separates them
Wix is named in 70 of 100 answers, recommended for a stated case in 70 and picked as the answer's own choice in 62. Squarespace is named in 65, recommended in 64 and picked in 56; WordPress 57, 56 and 47; Webflow 43, 42 and 36. Shopify (31, 31 and 24) and Framer (25, 25 and 20) follow. Almost every builder named gets a case: of the 389 brand mentions that were not the product a question asked to replace, 376 assign the builder to a kind of buyer, 97 percent. Only 3 list a builder with no case and 10 name one in passing. So the line that matters here is the pick: 309 of the 376 recommendations are the answer's own verdict. Of those recommended but not picked, WordPress has 9, Wix and Squarespace 8 each, Shopify 7. Shopify is picked in 24 of its 31 recommendations, the lowest rate of the six leaders, and nearly always for the same case: a site whose main job is selling products.
Squarespace takes the industry questions
Eighteen questions name a kind of business: restaurants, photographers, therapists, churches, law firms, nonprofits, artists, musicians, real estate agents and contractors. Squarespace is picked in 15 of them, Wix and WordPress in 10 each. Wix is recommended in 15 of the 18, but in five it is a row in the answer's table ("Maximum customization" for photographers, "Budget DIY website" for real estate agents) while the verdict goes to Squarespace or a builder made for that trade. Those five industry answers account for most of the gap between Wix's 70 recommendations and 62 picks. On the 20 direct advice questions, Squarespace is picked in 16 and Wix in 15.
Specialists are picked next to the big three
A builder made for the trade is picked in 8 of the 18 industry questions: SmugMug for photographers, Tithely for churches and Placester for real estate agents in two answers each, TherapySites and Bandzoogle in one. In 7 of the 8, Wix, Squarespace or WordPress is picked in the same answer; only one church answer picks Tithely alone. BentoBox (restaurants) and Brighter Vision (therapists) are described but not picked. The two law firm questions name no legal website vendor from the dictionary, but both name LawLytics, which is not in it. One opens "For most law firms, I'd look at LawLytics first" and so has no pick from the dictionary. It is one of only 2 answers out of 100 without a pick; the other describes seven Webflow alternatives and closes on three "closest substitutes" without choosing one.
Head-to-heads pick both sides
In 29 of the 30 comparison questions the answer picks every builder the question names, each for a kind of buyer. Wix versus GoDaddy, for example, ends with one line for buyers who care most about customization and one for buyers who want a quick, simple site. The one exception is Wix or GoDaddy for a contractor, which ends "I'd choose Wix" for a site meant to generate leads and calls GoDaddy only "reasonable" for a basic online presence.
Old names fade, and Hostinger owns price
Weebly is named in the 2 questions that ask about it and in none of the other 98 answers. GoDaddy, which sells a website builder alongside its domains, is named in 5 of the 96 answers to questions that do not name it and picked in 2. Twenty of the 52 brands in the dictionary are never named, among them IONOS, Jimdo, Strikingly, SITE123 and the AI builders Durable and 10Web. Hostinger found a lane instead: it is picked in 10 answers, in 9 of them the stated reason is price, and the tenth is the answer to "cheapest website builder".
Two reading lists that barely overlap
Across 100 answers there were 331 citation events to 120 domains. The builders' own sites took 189 of them, 57 percent, and 41 answers cited nothing but builder pages. wix.com is cited in 46 answers, 30 of them to questions that never mention Wix; squarespace.com in 43, 29 of them to questions that never mention Squarespace. The most-cited independent sources are TechRadar (9 answers) and itechguides.com (8 answers across four pages); itechguides.com ranked in Google's top ten for none of the 100 questions.
Google's top ten for these questions is 91 percent third-party pages, the highest in the series so far, and the sites it ranks most are ones the model does not cite. Reddit sits in Google's top ten for 99 of the 100 questions, YouTube for 49 and Quora for 45, and the review sites websiteplanet.com and tooltester.com for 19 each. None of them is cited in any answer. Of the 309 picks, 262 went to builders whose own site did not rank for that question, 85 percent. Only 51 of the 331 citation events involved a domain that also sat in Google's top ten for that query, 15 percent, the lowest overlap in the series so far.
0315 categories, side by side
Same rulebook, every category so far.
| Measure (codebook v2.0) | Project management, Sept 8 | CRM, Sept 17 | Email marketing, Sept 17 | Help desk, Sept 17 | Accounting, Sept 17 | Payment processing, Sept 17 | Payroll, Sept 21 | HR software, Sept 26 | Applicant tracking, Sept 26 | Password managers, Sept 26 | Endpoint security, Sept 26 | Business intelligence, Sept 26 | Data warehouse and ETL, Sept 26 | Ecommerce platforms, Sept 26 | Website builders, Sept 26 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Recommendations per category answer (average) | 5.9 | 4.3 | 5.0 | 5.5 | 4.3 | 4.2 | 4.6 | 5.0 | 4.5 | 3.4 | 4.7 | 5.0 | 4.5 | 4.3 | 4.3 |
| Answers with no recommended dictionary brand | 1 of 100 | 5 of 100 | 1 of 100 | 1 of 100 | 0 of 100 | 5 of 100 | 1 of 100 | 6 of 100 | 5 of 100 | 3 of 100 | 3 of 100 | 3 of 100 | 2 of 100 | 3 of 100 | 0 of 100 |
| Answers with no pick (the answer's own verdict) | 2 of 100 | 11 of 100 | 2 of 100 | 4 of 100 | 9 of 100 | 22 of 100 | 19 of 100 | 17 of 100 | 12 of 100 | 7 of 100 | 7 of 100 | 5 of 100 | 3 of 100 | 7 of 100 | 2 of 100 |
| Most-recommended brand: appears / recommended | Asana 73 / 73 | HubSpot 75 / 74 | Mailchimp 58 / 56 | Zendesk 70 / 69 | QuickBooks 75 / 73 | Stripe 70 / 68 | Gusto 73 / 73 | Rippling 58 / 57 | Workable 53 / 52 | Bitwarden 84 / 82 | Microsoft Defender 68 / 67 | Power BI 75 / 73 | Snowflake 50 / 50 | Shopify 75 / 75 | Wix 70 / 70 |
| Most-picked brand: appears / picked | Asana 73 / 70 | HubSpot 75 / 69 | Mailchimp 58 / 44 | Zendesk 70 / 61 | QuickBooks 75 / 65 | Stripe 70 / 54 | Gusto 73 / 61 | Rippling 58 / 50 | Workable 53 / 47 | Bitwarden 84 / 78 | Microsoft Defender 68 / 62 | Power BI 75 / 69 | Snowflake 50 / 46 | Shopify 75 / 69 | Wix 70 / 62 |
| Head-to-head queries recommending every named brand | 30 of 30 | 28 of 30 | 30 of 30 | 29 of 30 | 26 of 30 | 28 of 30 | 30 of 30 | 28 of 30 | 29 of 30 | 28 of 30 | 30 of 30 | 29 of 30 | 28 of 30 | 28 of 30 | 30 of 30 |
| Head-to-head queries picking every named brand | 30 of 30 | 23 of 30 | 29 of 30 | 29 of 30 | 25 of 30 | 19 of 30 | 21 of 30 | 21 of 30 | 29 of 30 | 27 of 30 | 29 of 30 | 29 of 30 | 28 of 30 | 27 of 30 | 29 of 30 |
| Recommendation-intent queries with a dictionary recommendation | 19 of 20 | 18 of 20 | 20 of 20 | 20 of 20 | 20 of 20 | 19 of 20 | 20 of 20 | 18 of 20 | 20 of 20 | 19 of 20 | 19 of 20 | 20 of 20 | 20 of 20 | 19 of 20 | 20 of 20 |
| Recommendation-intent queries with a pick | 19 of 20 | 18 of 20 | 20 of 20 | 20 of 20 | 19 of 20 | 18 of 20 | 20 of 20 | 18 of 20 | 20 of 20 | 19 of 20 | 19 of 20 | 20 of 20 | 20 of 20 | 19 of 20 | 20 of 20 |
| Recommended mentions where the brand does not rank in Google's top 10 | 398 of 468 (85%) | 269 of 387 (70%) | 313 of 419 (75%) | 350 of 445 (79%) | 206 of 368 (56%) | 294 of 360 (82%) | 202 of 383 (53%) | 310 of 405 (77%) | 333 of 388 (86%) | 218 of 304 (72%) | 273 of 369 (74%) | 355 of 401 (89%) | 360 of 426 (85%) | 313 of 376 (83%) | 324 of 376 (86%) |
| Picked mentions where the brand does not rank in Google's top 10 | 349 of 416 (84%) | 226 of 330 (68%) | 270 of 370 (73%) | 291 of 380 (77%) | 141 of 279 (51%) | 170 of 222 (77%) | 113 of 246 (46%) | 232 of 311 (75%) | 261 of 311 (84%) | 163 of 241 (68%) | 231 of 319 (72%) | 292 of 336 (87%) | 303 of 365 (83%) | 227 of 285 (80%) | 262 of 309 (85%) |
| Google top 10 that is third-party pages | 746 of 1,000 (75%) | 710 of 1,000 (71%) | 720 of 1,000 (72%) | 714 of 1,000 (71%) | 743 of 1,000 (74%) | 787 of 1,000 (79%) | 653 of 1,000 (65%) | 820 of 1,000 (82%) | 871 of 1,000 (87%) | 866 of 1,000 (87%) | 778 of 1,000 (78%) | 823 of 999 (82%) | 777 of 1,000 (78%) | 868 of 1,000 (87%) | 906 of 1,000 (91%) |
| Citation events to vendors' own sites | 216 of 362 (60%) | 136 of 320 (42%) | 193 of 367 (53%) | 152 of 345 (44%) | 125 of 302 (41%) | 161 of 301 (53%) | 192 of 314 (61%) | 167 of 325 (51%) | 139 of 311 (45%) | 201 of 292 (69%) | 149 of 266 (56%) | 190 of 274 (69%) | 197 of 267 (74%) | 194 of 277 (70%) | 189 of 331 (57%) |
| Answers citing only vendor pages | 50 of 100 | 40 of 100 | 37 of 100 | 27 of 100 | 34 of 100 | 47 of 100 | 49 of 100 | 34 of 100 | 30 of 100 | 65 of 100 | 44 of 100 | 60 of 100 | 60 of 100 | 55 of 100 | 41 of 100 |
| Share of citations in the 10 most-cited domains | 54% | 40% | 42% | 45% | 55% | 58% | 55% | 46% | 44% | 72% | 58% | 62% | 60% | 61% | 54% |
| Citation events whose domain is in Google's top 10 | 83 of 362 (23%) | 83 of 320 (26%) | 97 of 367 (26%) | 80 of 345 (23%) | 99 of 302 (33%) | 98 of 301 (33%) | 134 of 314 (43%) | 85 of 325 (26%) | 59 of 311 (19%) | 78 of 292 (27%) | 72 of 266 (27%) | 44 of 274 (16%) | 54 of 267 (20%) | 56 of 277 (20%) | 51 of 331 (15%) |
| Reddit and Wikipedia citations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
All columns are coded under codebook v2.0 by two independent AI readers per answer; the project management column is the September 8 dataset recoded under it. Each teardown is one run per query in its own window, so differences between columns mix category with collection date.
04What this changes
Four things, in order.
Own a stated case. The model answers website builder questions by matching a builder to a kind of buyer. Squarespace is picked in 15 of 18 industry questions and Hostinger in 9 answers on price, and in both the answer names the case in a few words. The pick goes to the builder whose case matches the question.
A row in the table is not the pick. Wix is recommended in 15 of the 18 industry questions and picked in 10. In the other five it is described as the flexible or budget option while the verdict goes elsewhere. Pages that say who you are for, industry by industry, give the model a reason to choose you rather than list you.
An industry builder needs to explain itself next to the big three. Specialists were picked in 8 of the 18 industry questions, and in all but one of those answers next to Wix, Squarespace or WordPress. If you sell to one industry, your pages have to say why you instead of, or alongside, the general builders the buyer already knows.
Your own site is read; the sites Google ranks are not. wix.com was cited in 30 answers to questions that do not mention Wix, while Reddit and the review sites Google ranks most often for these questions were cited in none. Clear comparison, pricing and use-case pages are the material the model reaches for first. Know which list your category's buyers are reading from.
05Limitations
What this teardown cannot tell you.
One run per query means answer variance is unmeasured; Benchmark 01 runs each query three times across three surfaces. The teardowns are collected on different days, so differences between categories mix the category with the date. The brand dictionary covers the general website builders market and deliberately excludes vertical tools, so their appearances are described in prose and not counted. The readers are AI models, not people: they follow a written protocol and agree with each other closely, but a shared blind spot would not show up as disagreement. The line between picked and recommended is a judgment, documented in the protocol with examples. Sentiment is not coded. No vendor-tool cross-check was read for this category. Google's control counts a brand as ranking only when its own domain is in the top ten; a listicle that features the brand does not count. One run per question, one day, United States, English, logged out. Answers vary between runs, so these are frequencies for this sample; one answer came back in Spanish. Some products the answers name are outside the dictionary and are not counted: Webstudio (named in 6 answers, the stated pick in one) and Format (5) for portfolio and open-source questions, LawLytics in both law firm answers, and five enterprise platforms (Adobe Experience Manager, Sitecore, Optimizely, Contentful and Contentstack) that make up the main shortlist in the enterprise answer. Two aliases were added after collection; both dictionaries ship with the dataset.
06Dataset
Check it, don't believe it.
Every number above can be recomputed from these files. CC BY 4.0: use them, cite the page.
- queries.csv: the 100 queries with intent labels.
- brands.csv: the 52-brand dictionary with aliases and canonical domains.
- mentions.csv: 409 coded brand mentions with position, type, a 0/1 picked column and whether the brand's domain was in Google's top ten.
- citations.csv: 331 citation events with domain class and Google overlap.
- observations.csv: one row per query with brand, recommendation, citation and control counts.
- dictionary-changes.md: the two alias additions made after collection, before the final coding run, with their effect
- brands-original.csv: the dictionary as fixed before collection, before those additions
- coder-notes.md: coding notes: reader agreement, the judgment calls the readers and the third reader made for this category, reader-added mentions and dictionary notes
- codebook.md: the rulebook, with dated amendments through v2.0.
- reader-protocol.md: the written protocol both readers coded against (codebook v2.0).
- picked_stats.json: the picked-level figures.
The earlier teardowns: Teardown 01, project management, Teardown 02, crm, Teardown 03, email marketing, Teardown 04, help desk, Teardown 05, accounting, Teardown 06, payment processing, Teardown 07, payroll, Teardown 08, hr software, Teardown 09, applicant tracking, Teardown 10, password managers, Teardown 11, endpoint security, Teardown 12, business intelligence, Teardown 13, data warehouse and etl, Teardown 14, ecommerce platforms.
Fifteen categories, and the reading list is still the story.
In website builders the model's picks track the builders' own websites and a few review sites that do not rank, while the pages Google ranks most go uncited. In password managers they track vendor sites, and in applicant tracking a comparison library that does not rank. Finding out what the model is reading for your category is the first thing a Category Salience Brief does, with a query set you approve first.