HIGH SALIENCE / RESEARCH / SCHEDULING TEARDOWN
TEARDOWN 16 · PUBLISHED SEPTEMBER 26, 2026 · DATASET INCLUDED
100 Questions, Sixteenth Category: Scheduling Software, Where Calendly Is the Default and the Trades Get Their Own Picks
Sixteenth category, same method: 100 scheduling software queries, ChatGPT with web search on, every answer coded by two independent AI readers from a published protocol, Google's top ten as a control. Calendly leads alone. It is named in 57 answers, recommended in 56 and picked as the answer's own choice in 51. The second brand, Acuity Scheduling, is named in 35, the thinnest second place in the series so far. Behind them the category splits by trade: in 11 of 20 questions about a kind of service business, the answer picks none of Calendly, Acuity Scheduling or Square Appointments, and Vagaro is picked in 8 of those 20, twice as often as Calendly. And three small roundup sites that rank for none of the questions are cited in 17 answers.
01Method
Same query structure, same coding, one new category.
Queries: 100 scheduling software 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 50-brand dictionary fixed before collection, under codebook v2.0. "Recommended" in the tables includes picked brands. The two readers agreed on 98.9% of brand codes in this teardown (99.5% at the recommended level). Cited URLs were deduplicated to their domain and classed as first-party or third-party. The third reader decided 4 of the 366 brand codes in this teardown. A rules-based first pass (the codebook v1.6 rules) agreed with the final codes on 95.1 percent at the recommended level; only the reader codes are published. The readers also coded 11 brand mentions 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, with raw responses saved exactly as returned; the raw files carry UTC timestamps, early September 27. No task failed and none was re-run. Square Appointments also matches bare "Square", so answers that pick "Square" for a salon count; where Square appears only as a payment processor that Acuity connects to, the readers code it as a passing mention, not a recommendation. HubSpot Meetings and Google Calendar Appointment Schedules match only their scheduling feature names, not bare "HubSpot" or "Google Calendar", which most answers mention as integrations, so a pick of plain "HubSpot" is not credited. google.com, microsoft.com, zoho.com, hubspot.com, squareup.com and wix.com each count as the own site of the one dictionary brand its parent company makes. Ten aliases that are also ordinary words or names (Square, Motion, Reclaim, Doodle, Boulevard and others) match only when capitalized.
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
One default, a category that splits by trade, and a reading list of roundup sites.
| Brand | Appears in | Recommended in | Picked in | Picked share of appearances |
|---|---|---|---|---|
| Calendly | 57 | 56 | 51 | 89% |
| Acuity Scheduling | 35 | 35 | 30 | 86% |
| Cal.com | 27 | 27 | 22 | 81% |
| Square Appointments | 25 | 25 | 23 | 92% |
| Setmore | 18 | 17 | 16 | 89% |
| Vagaro | 17 | 16 | 15 | 88% |
| Fresha | 12 | 11 | 6 | 50% |
| SavvyCal | 10 | 10 | 7 | 70% |
| SimplyBook.me | 10 | 10 | 10 | 100% |
| Microsoft Bookings | 10 | 9 | 7 | 70% |
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.
One brand, then a long gap
Calendly is named in 57 of 100 answers, recommended in 56 and picked as the answer's own choice in 51. In the 75 questions that do not mention it, it is still named in 40 and picked in 34. On the 20 direct advice questions, 19 produced a pick and Calendly was picked in 13; Acuity Scheduling was picked in 6, Vagaro and Square Appointments in 4 each. Acuity Scheduling is second, named in 35 answers and picked in 30. In the fifteen earlier teardowns no second-place brand was named in fewer than 46. Cal.com (named in 27, picked in 22) and Square Appointments (25 and 23) follow. Almost every recommendation here is a pick: 276 of 325 recommended mentions, 85 percent. Calendly, Acuity Scheduling and Cal.com are each described for a case but not picked in 5 answers.
Calendly is the word for the category
In 10 answers the model uses Calendly to describe a type of product: "Calendly-style", "Calendly-like", "like Calendly". In three of them it is the thing to avoid: two dental offices and a home-service business are told to buy practice or field-service software "rather than a generic scheduler like Calendly", and those answers pick NexHealth, or Jobber and Housecall Pro. Calendly was picked in 17 of the 18 head-to-heads that named it; the exception, Chili Piper or Calendly for inbound sales, only announced a comparison and picked neither.
The trades get their own tools
Twenty questions name a kind of service business: salons, barbershops, spas, massage therapists, therapists, medical and dental practices, fitness studios, personal trainers, law firms, photographers, tutors, churches and home services. Every one of them produced a pick. In 11 of them the answer picked none of Calendly, Acuity Scheduling or Square Appointments. Vagaro is picked in 8 of the 20, Calendly, Acuity Scheduling and Square Appointments in 4 each, and Cal.com, third by answers naming it, is not named in any. Salon, barber and spa questions go to Vagaro, GlossGenius, Booksy, Fresha and Boulevard, with Square Appointments alongside. Therapists get SimplePractice, TherapyNotes and Jane App, dentists NexHealth, photographers HoneyBook and Dubsado, churches Planning Center, home-service firms Jobber and Housecall Pro. Law firms get a stack: Clio for the firm, Calendly for consultation booking. Fourteen of Vagaro's 15 picks are beauty, wellness or fitness questions. Sales, consulting and recruiting questions stay with Calendly, named in all 6 and picked in 5.
What does "scheduling" mean?
Eight answers picked nothing, and four of them read the word as a different product. "Best scheduling tool for small business" and "which scheduling tool should I use for a 50 person company" were answered with employee shift schedulers such as Homebase, When I Work and Deputy. "Best open source scheduling software" got data pipeline orchestrators (Apache Airflow, Kestra, Dagster). "Best round robin scheduling tool" got sports tournament generators. Each of the four opens with "If you mean" and then answers that reading. Of the other four, one only announced a comparison and three listed options with a fit for each, then asked the buyer to narrow it down. The head-to-heads were decisive: 29 of 30 picked every brand named in the question.
A reading list of roundup sites
Across 100 answers there were 296 citation events to 139 domains. Vendors' own sites took 137 of them, 46 percent, and other third-party pages 138, 47 percent, the highest share for that group in the series so far. calendly.com is the most-cited domain, in 29 answers, 14 of them to questions that never mention Calendly. The most-cited third parties are small roundup and pricing sites: dupple.com in 8 answers, codersyapps.com in 6 and stackscored.com in 5, 17 answers between them. None of the three ranked in Google's top ten for any question where it was cited. Two codersyapps.com pages, on salon booking software pricing and barber booking apps, were cited in six salon, barbershop and massage answers. Zapier, in 8 answers, is the most-cited review and media source. Reddit and Wikipedia have zero citations.
Of the 276 picks, 200 went to brands whose own website did not rank in Google's top ten for the question, 72 percent. Acuity Scheduling was picked 30 times, and its own site ranked for 3 of those questions. Sixty-three of the 296 citation events involved a domain that also sat in Google's top ten for that query, 21 percent.
0316 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 | Scheduling, 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 | 3.4 |
| 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 | 5 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 | 8 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 | Calendly 57 / 56 |
| 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 | Calendly 57 / 51 |
| 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 | 29 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 | 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 | 19 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 | 19 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%) | 241 of 325 (74%) |
| 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%) | 200 of 276 (72%) |
| 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%) | 780 of 999 (78%) |
| 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%) | 137 of 296 (46%) |
| 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 | 42 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% | 35% |
| 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%) | 63 of 296 (21%) |
| Reddit and Wikipedia citations | 0 | 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.
Being the category's name is not the same as being every buyer's pick. The model uses Calendly to describe the whole category, and in three answers that is exactly why it is ruled out: a dental office or a home-service firm is told to buy software built for its trade instead of a generic scheduler.
Say which trade you serve, in plain words. In 11 of 20 trade questions none of Calendly, Acuity Scheduling or Square Appointments was picked, and Vagaro was picked in 8. SimplePractice, NexHealth, Planning Center and Jobber are picked for the trades they are built for. If you serve a trade, your pages should name it.
Small roundup sites are on the reading list. Three roundup and pricing sites that ranked for none of the questions were cited in 17 answers, and two pages on one of them were cited in six salon, barbershop and massage answers. Know which of these cover your category, and whether they describe you accurately.
Ambiguous words get resolved without you. Four answers read "scheduling" as employee shift scheduling, data pipelines or sports tournaments, and picked nothing from this category. If your category word means several things, your pages have to say which one you are.
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 scheduling software 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. The codes come from AI readers, not human coders; the two readers agreed on 98.9 percent of codes, and the remaining disagreements were decided by a third AI reader. Mangomint, dental practice software such as Dentrix and Curve Dental, recruiting software such as GoodTime, and employee shift schedulers are not in the dictionary, and a pick of plain "HubSpot" is not credited to HubSpot Meetings. Four questions were read as being about a different kind of scheduling, which lowers every brand's counts slightly.
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 50-brand dictionary with aliases and canonical domains.
- mentions.csv: 366 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: 296 citation events with domain class and Google overlap.
- observations.csv: one row per query with brand, recommendation, citation and control counts.
- coder-notes.md: coding notes for codebook v2.0: reader agreement, the third reader's decisions, judgment calls 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, Teardown 15, website builders.
Sixteen categories, and the reading list is still the story.
In scheduling the model's picks track the vendors' own sites and a handful of roundup sites that do not rank. In password managers they track the vendors' own websites. 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.