We detected 767 companies using Magellan AI, 61 companies that churned, and 45 customers with upcoming renewal in the next 3 months. The most common industry is Financial Services (7%) and the most common company size is 51-200 employees (31%). We find new customers by detecting JavaScript snippets or configurations on customer websites.
Note: We can't detect companies using Magellan AI in server-side only implementations or headless storefronts (edge cases)
The count of new companies shown here may differ from the total in the table above. This is intentional. We apply a consistent baseline to ensure month-over-month comparisons are apples-to-apples rather than affected by when data was first collected.
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Market Insights
🏢 Top Industries
Financial Services47 (7%)
Retail43 (7%)
Consumer Services32 (5%)
Banking29 (4%)
Non-profit Organizations27 (4%)
📏 Company Size Distribution
51-200 employees220 (31%)
11-50 employees132 (19%)
201-500 employees114 (16%)
1,001-5,000 employees77 (11%)
501-1,000 employees72 (10%)
👥 What types of companies use Magellan AI?
Source: Analysis of Linkedin bios of 767 companies that use Magellan AI
Company Characteristics
i
Shows how much more likely Magellan AI customers are to have each trait compared to all companies. For example, 2.0x means customers are twice as likely to have that characteristic.
Trait
Likelihood
Funding Stage: Private equity
35.9x
Funding Stage: Series A
30.6x
Funding Stage: Grant
18.5x
Company Size: 5,001-10,000
17.6x
Industry: Manufacturing
13.4x
Company Size: 1,001-5,000
12.7x
I noticed that Magellan AI's customers span an incredibly diverse range of industries, but they share a common thread: they're all consumer-facing businesses that need to reach and acquire customers at scale. These companies sell everything from car washes and collision repair to fitness memberships, fertility monitors, dog food, and addiction treatment services. What unites them is that they're competing in crowded consumer markets where brand awareness and customer acquisition are critical challenges.
These companies range dramatically in maturity. Some are venture-backed startups like Wispr Flow (Series A, $30M) and ThreatLocker (Series E, $60M) in rapid growth mode. Others are established enterprises like Lenovo and IBM with tens of thousands of employees. However, the majority sit in that middle zone: 50-500 employees, often privately held or family-owned, with proven business models now focused on scaling their customer base and geographic reach.
🔧 What other technologies do Magellan AI customers also use?
Source: Analysis of tech stacks from 767 companies that use Magellan AI
Commonly Paired Technologies
i
Shows how much more likely Magellan AI customers are to use each tool compared to the general population. For example, 287x means customers are 287 times more likely to use that tool.
I noticed that companies using Magellan AI are heavily invested in digital advertising measurement and attribution, particularly across audio and connected TV channels. The presence of Spotify Ads alongside multiple programmatic advertising platforms like TheTradeDesk and MNTN tells me these are sophisticated performance marketers running multi-channel campaigns who need to track effectiveness across newer, harder-to-measure media formats.
The pairing of Magellan AI with Spotify Ads makes perfect sense since Magellan specializes in podcast advertising intelligence and measurement. Companies using both are clearly investing in audio advertising and want competitive intelligence plus performance tracking. The strong correlation with MNTN and TVSquared is equally revealing. These companies aren't just doing traditional digital ads, they're running connected TV campaigns and need attribution tools that can connect streaming video exposure to downstream conversions. Axon by AppLovin appearing so frequently suggests many of these companies are in mobile app growth mode, needing to understand how their podcast and audio investments drive app installs.
The full stack reveals marketing-led organizations that are beyond early stage experimentation. These companies have dedicated media buying teams and enough budget to justify specialized measurement tools for each channel. They're likely Series B or later, possibly even public companies, given the investment in premium analytics infrastructure. The emphasis on attribution and measurement over pure acquisition tools suggests they're optimizing existing channels rather than just testing new ones.
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