HIGH SALIENCE / RESEARCH / EMAIL MARKETING TEARDOWN

TEARDOWN 03 · PUBLISHED SEPTEMBER 17, 2026 · DATASET INCLUDED

100 Questions, Third Category: Seven Email Brands and No Default

Third category, same method: 100 email marketing queries, ChatGPT with web search on, every answer coded under the rulebook the first two teardowns used, Google's top ten as a control. Project management had a favorite and CRM had a big four. Email marketing has seven brands nobody leads, and a model that recommends both sides of every single head-to-head.

01Method

Same query structure, same coding, one new category.

Queries: 100 email marketing 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 17, 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 brand named was coded as recommended (selected for a stated case), listed (in a table or list without being chosen), passing, or anchor (the brand being replaced in an "alternatives" query), against a 52-brand dictionary fixed before collection, under codebook v1.5. Cited URLs were deduplicated to their domain and classed as first-party or third-party. Five answers drawn with a fixed seed were hand-checked. Under the rulebook as it stood, 19 of 22 coded mentions agreed with the reader, below the 90 percent floor the codebook sets, which triggered amendment v1.5; under v1.5 all 22 agree.

Codebook amendment v1.5: the three hand-check misses were lines of the form "B2B startup with a sales team: HubSpot" and "product-led startup: Customer.io", where the answer assigns a brand to a stated case without a selecting verb. That is a selection for a stated case, which is what recommended has meant since the rulebook was written, so the rule was clarified, a random sample of 40 lines caught by the clarification was read (all 40 were genuine assignments), and all three datasets were recoded. The figures on this page and on the CRM page are v1.5; the project management page keeps its original figures with an update note.

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

Seven brands, no default.

Bar chart of 11 email marketing software brands showing how many of 100 ChatGPT answers each appears in versus how many recommend it. Mailchimp 61 and 40, Brevo 49 and 34, MailerLite 46 and 36, ActiveCampaign 43 and 32, Klaviyo 39 and 28, HubSpot 38 and 25, Kit 35 and 26, Omnisend 20 and 16, Constant Contact 12 and 10, Customer.io 11 and 9, beehiiv 11 and 9.
BrandAppears inRecommended inRecommended share of appearances
Mailchimp614066%
Brevo493469%
MailerLite463678%
ActiveCampaign433274%
Klaviyo392872%
HubSpot382566%
Kit352674%
Omnisend201680%
Constant Contact121083%
Customer.io11982%
beehiiv11982%

Out of 100 answers, codebook v1.5. "Recommended" means the answer selected the brand for a stated case. "Appears" adds brands listed in a table or bullet list without being chosen. The brand being replaced in an "alternatives" query is excluded from both columns.

Nobody owns this category in the model's mouth

Mailchimp is the most-named brand and appears in 61 of 100 answers. In project management the leader appeared in 76, in CRM 77. Behind Mailchimp sit Brevo at 49, MailerLite 46, ActiveCampaign 43, Klaviyo 39, HubSpot 38 and Kit 35: seven brands inside a 26-answer band. Recommendation share is high for all of them, between 66 and 78 percent of appearances, which means the model is not withholding picks. It is spreading them. Fifty-one answers recommend three or more brands; twenty recommend five or more; one recommends nine.

When the buyer asks outright, the spread holds. Seventeen of 20 recommendation-intent queries produced a dictionary pick, and the picks went to Mailchimp 11 times, ActiveCampaign 8, Brevo 7, HubSpot 7, Customer.io 5, Klaviyo 5, MailerLite 4, Kit 3. No brand is the default. Each has a lane: Klaviyo and Omnisend for stores, Kit and beehiiv for creators, Customer.io for product-led software, ActiveCampaign for automation, Brevo and MailerLite for price.

Every head-to-head is a fork

Thirty of 30 comparison queries recommended every brand named in the query. Not one produced a single winner. Mailchimp's 14 comparison recommendations are all on queries where Mailchimp was one of the names being compared: it is chosen as one side of a fork, never as the answer to somebody else's comparison. If your category page strategy is "beat Mailchimp", the model has already decided the match is a draw and is handing the buyer the conditions.

Vertical prompts bend the field here instead of swapping it

In project management and CRM, one vertical word replaced the entire field. In email marketing it mostly adds a specialist beside the general brands. Real estate agents get Follow Up Boss next to ActiveCampaign, Constant Contact and Mailchimp, and the general brands are still recommended. Financial advisors get FMG Suite and Snappy Kraken, plus a compliance archiving layer (Smarsh, Global Relay), and HubSpot, ActiveCampaign and Constant Contact are still chosen. Only three vertical prompts lost the general field entirely: a restaurant was sent to Toast Marketing first, a marketing agency to HighLevel, and a law firm got a table with no pick at all. Seven answers in total recommended no dictionary brand, against 13 in CRM and 5 in project management; the other four were lists with no selecting line.

Ranking in Google is still a different game

Of the 309 recommended brand mentions, 216 were for brands whose own website does not rank in Google's top ten organic results for that query, 70 percent. Google's top ten for these queries is 72 percent third-party pages. The overlap sits between the CRM figure (65 percent) and the project management figure (83 percent).

Vendors are cited more here, and the long tail is stranger

Across 100 answers there were 367 citation events to 140 domains, one per cited domain per answer. Fifty-three percent went to vendors' own sites, the highest share of the three categories after project management, and 37 answers cited nothing but vendor pages. The most-cited domains are mailchimp.com (25), activecampaign.com (23) and hubspot.com (20). Capterra (10) and Zapier (9) lead the third-party half, and behind them are match-vs.com, emailtooltester.com, smbcompare.com and tajo.io at four or five citations each. The ten most-cited domains cover 42 percent of citation events. Reddit and Wikipedia have zero citations in this sample, for the third teardown running.

Ninety-seven of the 367 citation events involved a domain that also sat in Google's top ten for that query, 26 percent, the same overlap as CRM.

033 categories, side by side

Same rulebook, every category so far.

Measure (codebook v1.5)Project management, Sept 8CRM, Sept 17Email marketing, Sept 17
Recommendations per category answer (average)4.83.33.3
Answers with no recommended dictionary brand5 of 10013 of 1007 of 100
Most-recommended brand: appears / recommendedAsana 76 / 68HubSpot 77 / 62Mailchimp 61 / 40
Head-to-head queries recommending every named brand29 of 3026 of 3030 of 30
Recommendation-intent queries with a dictionary pick18 of 2016 of 2017 of 20
Recommended mentions where the brand does not rank in Google's top 10305 of 368 (83%)187 of 287 (65%)216 of 309 (70%)
Google top 10 that is third-party pages746 of 1,000 (75%)710 of 1,000 (71%)720 of 1,000 (72%)
Citation events to vendors' own sites216 of 362 (60%)136 of 320 (42%)193 of 367 (53%)
Answers citing only vendor pages50 of 10040 of 10037 of 100
Share of citations in the 10 most-cited domains54%40%42%
Citation events whose domain is in Google's top 1083 of 362 (23%)83 of 320 (26%)97 of 367 (26%)
Reddit and Wikipedia citations000

All columns are coded under codebook v1.5; 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.

In a category with no default, the lane is the prize. The model gives every one of seven brands a use case and then recommends by use case. The brand whose lane is stated most clearly, in the model's own words, gets the queries that match it. A vague lane gets furniture.

Do not budget to beat the incumbent on comparison queries. Thirty forks in thirty. Write the fork: say plainly when Mailchimp is the better choice and when you are.

Vertical prompts are additive here. A specialist gets named beside you, not instead of you. Being the general brand that survives the vertical prompt is a position worth measuring and holding.

Measure the overlap with Google per category. Seventy percent of recommendations here go to brands not ranking for the query. That number was 65 in CRM and 83 in project management. It is a property of the category, not of AI search.

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 email marketing software market and deliberately excludes vertical tools, so their appearances are described in prose and not counted. The rule-based coder has one known failure: a brand named as a contrast on the same line as a selecting verb is coded as recommended. Sentiment coding is rule-based and not reported. 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. The brand dictionary was written before collection; HighLevel, Toast Marketing, FMG Suite, Snappy Kraken and Follow Up Boss were not in it and are described in prose only.

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: 463 coded brand mentions with position, type and whether the brand's domain was in Google's top ten.
  • citations.csv: 367 citation events with domain class and Google overlap.
  • observations.csv: one row per query with brand, recommendation, citation and control counts.
  • codebook.md: the rulebook, with dated amendments through v1.5.

The earlier teardowns: Teardown 01, project management, Teardown 02, crm. This teardown is also being published on the High Salience Substack.

Three categories, three different mechanisms.

A favorite, a big four, and a seven-way spread. Which one you are fighting decides what you build, and the only way to know is to measure your own category. A Category Salience Brief runs this exact method on it, with a query set you approve first.