Retail, proximity and IoT marketing · Named, not rankedRadar Engage
Named, too few times to rank.
A product needs 10 labels in a category before a share is stated. Radar Engage has 1.
Why models recommend it
“If by in-store marketing you mean messages or experiences inside your own locations, look at Radar Engage instead.”GPT-6 Luna · Paraphrase question · alternative
What gives them pause
No model argued against it.
Also on the shortlist
The products named in the same answers as Radar Engage, over the 1 answer that named it, and how often each was the first choice instead.
| Product | Named together | Picked instead | |
|---|---|---|---|
| 1 | 1 | View → |
A side-by-side page exists where both products are among a category's top eight.
At a glance
Stated on Radar Engage's own site, not checked by Gane.
- Headquarters
- New York City, United States
- Founded
- 2016
How this is measured
Fourteen AI models from 12 labs are asked the same buying questions six ways, for three company sizes. Standings refresh every edition.
Gane sells AI visibility software. No company can pay to change a result.
Read the method →Sources returned with these answers
Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted. These counts come from 1 of the 1 answers that named Radar Engage and are not a share of its labels.
Domains cited
Two of the three domain citations in answers naming Radar Engage came from a third party's page.
Pages cited
Pages are listed as the models cited them.
By model
| Model | First choice | Alternative | Mention | Warning | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| 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 | 0 | 0 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 1 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
What it publishes
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 93 | 7 | 2026-09-25 | Mobile apps | |
| Case study | 15 | undated | |||
| Comparison | 10 | 0 | 2026-06-02 | ||
| Glossary or explainer | 5 | 0 | 2026-06-11 |
Most recent
- 2026-09-25How precise location uncovers fraud signals traditional signals miss blog
- 2026-09-22Three ways to use precise location to detect and prevent fraud blog
- 2026-09-21Location signals in fraud prevention why precise location reveals the connections ip misses blog
- 2026-09-17How does location intelligence detect coordinated fraud in financial services blog
- 2026-08-18From location infrastructure to location intelligence radars latest brand refresh blog
- 2026-07-23Campaign scheduling and user insights blog
How it is countedHide how it is counted
Every page radar.com exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. Read September 29, 2026.