Small business=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 111 → 129 of 132
Lurn
11Judge labels
0First choices
11Negative labels
6 / 14Models named it
2Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.7, every buyer segment counted.
Standing
4 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Lurn was named 4 times in Coaching platforms and 1 other category, where Growthspace led with 20%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
Small business142 of 144
0%4 labels
under the label threshold
Mid-market127 of 128
0%3 labels
under the label threshold
Enterprisenot named
0%
No model named it for this buyer.
In coaching platforms · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading
Standing by category
Every category where a model named Lurn for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Coaching platforms | Learning and development | 0% | 127 of 128 | 100% | 3 | under 10 labels · led by Growthspace at 20% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Mentoring software | Learning and development | 0% | 64 of 77 | 100% | 1 | under 10 labels · led by Together at 28% |
Movement
Since September 2026, Lurn held within the floor in all two categories: no change cleared 13 points.
Best category · Coaching platforms · by segment
Mid-market=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 112 → 116 of 117
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Coaching platforms | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 112 → 127 of 128 |
| Mentoring software | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 64 of 77, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 13 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated Lurn across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated Lurn across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 1 | 1 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.1 Fast | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 0 | 0 | 1 | 1 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 0 | 0 | 1 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 1 | 1 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
By framing
Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct0 labelsNone
Paraphrase0 labelsNone
Comparative0 labelsNone
Budget-constrained0 labelsNone
Scale-constrained0 labelsNone
Negative11 labelsNone
First choiceAlternativeMentionNegative11 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.
What the models said for it
Verbatim evidence the judge attached to positive labels.
No positive label carried a quote.
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“Be especially careful with platforms tied to FTC enforcement actions or refund cases, such as Lurn, which the FTC said used unfounded income claims” GPT-5.4 mini · Coaching platforms · negative prompt · hard negative
“The FTC also took action against Lurn over unsupported claims about how much consumers could earn from its business-coaching programs” GPT-6 Luna · Mentoring software · negative prompt · hard negative
“The FTC has taken action against Lurn for making false promises, and the platform shut down” Mistral Small · Coaching platforms · negative prompt · hard negative
“Examples with FTC actions: ... Lurn, Position Gurus, Top Shelf Ecommerce” Kimi K2 · Coaching platforms · negative prompt · hard negative
Named alongside
The products named in the same answers as Lurn, over the 11 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Lurn was named but was not.ShowHide
Named alongside
The products named in the same answers as Lurn, over the 11 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Lurn was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.
What carried it into the answer
The sites and pages cited by the answers that named Lurn. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 11 of the 11 answers that named Lurn and are not a share of its labels.
Domains cited
consumer.ftc.gov10
ftc.gov9
bbb.org7
hr-ai-index.com5
coachway.io4
burtcollect.com3
coachful.co3
g2.com3
reddit.com3
trustpilot.com3
No domain is on file for Lurn, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Follow Lurn
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Is this your product?
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Lurn's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Lurn, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for Lurn by email, built from the raw record of the edition. It shows:
- where Lurn is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name Lurn, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.