# Radar Engage: how AI models rank it, November 2026

Gane Index, November 2026 Edition. Named in 1 judge label across 1 category by 1 of 14 models. Page: https://www.gane.ai/index/gtm/vendors/radar-engage

## Standing by category

| Category | Share | Rank | Negative rate | Labels |
|---|---|---|---|---|
| [Retail, proximity and IoT marketing](https://www.gane.ai/index/gtm/marketing/retail-proximity-and-iot-marketing/index.md) | 0% | 96 | 0% | 1 |

## What the models said for it

- "If by in-store marketing you mean messages or experiences inside your own locations, look at Radar Engage instead." (GPT-6 Luna, Proximity)

## 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 |

## Sources returned with these answers

The sites and pages cited by the answers that named Radar Engage, from 1 of the 1 answers that named it (only the models that return a source list).

### Domains cited

- propensity.com: 1
- radar.com: 1 (its own site)
- support.propensity.com: 1

### Pages cited

- https://propensity.com/: 1
- https://radar.com/solutions/engage: 1
- https://support.propensity.com/hc/en-us/articles/45709987595291-How-to-Build-a-Geofencing-Campaign-in-Propensity: 1

## The company

Radar; https://radar.com/; New York City, United States; founded 2016. From Wikidata, fetched 2026-09-14.

## What it publishes

Every page radar.com exposes, read 2026-09-29: 212 addresses. By kind, pages and the last 90 days:

- blog: 93 pages, 7 in the last 90 days, latest 2026-09-25
- case study: 15 pages, 0 in the last 90 days, latest undated
- comparison: 10 pages, 0 in the last 90 days, latest 2026-06-02
- glossary or explainer: 5 pages, 0 in the last 90 days, latest 2026-06-11
- Recent: 2026-09-25 blog: How precise location uncovers fraud signals traditional signals miss https://radar.com/blog/how-precise-location-uncovers-fraud-signals-traditional-signals-miss
- Recent: 2026-09-22 blog: Three ways to use precise location to detect and prevent fraud https://radar.com/blog/three-ways-to-use-precise-location-to-detect-and-prevent-fraud
- Recent: 2026-09-21 blog: Location signals in fraud prevention why precise location reveals the connections ip misses https://radar.com/blog/location-signals-in-fraud-prevention-why-precise-location-reveals-the-connections-ip-misses
- Recent: 2026-09-17 blog: How does location intelligence detect coordinated fraud in financial services https://radar.com/blog/how-does-location-intelligence-detect-coordinated-fraud-in-financial-services
- Recent: 2026-08-18 blog: From location infrastructure to location intelligence radars latest brand refresh https://radar.com/blog/from-location-infrastructure-to-location-intelligence-radars-latest-brand-refresh
- Recent: 2026-07-23 blog: Campaign scheduling and user insights https://radar.com/blog/campaign-scheduling-and-user-insights

## Record

- Method: https://www.gane.ai/index/gtm/methodology
- License: CC BY 4.0. Cite as Gane Index, November 2026 Edition, www.gane.ai/index/gtm.