Today, a short report was published about AppLovin, questioning our e-commerce business and advertising practices. We haven’t talked much about attribution and analytics, which are standardized across advertising channels. As CEO, I want to take a moment to address these claims head-on, provide clarity, and reaffirm our focus on building a world-class platform that drives value for our partners and shareholders. Let’s dive into the facts.

Our E-commerce Business: Rapid Growth and Real Results

Our e-commerce advertising business has scaled at an extraordinary pace—reaching a billion-dollar run rate of spend in mere months. This isn’t luck; it’s a testament to our technology and execution. Advertisers in this space fall into two camps:

1. One-Time Purchase Advertisers—Think fire extinguishers or auto insurance. These are straightforward: either we drive the sale, or we don’t. Roughly 80% of the sales we can measure occur within 24 hours from when the user sees and clicks the advertisement, making incrementality easy to measure. The data speaks for itself—we’re delivering.
2. Recurring Purchase Advertisers—Brands who have repeat customers, where the question becomes: did our ad drive the sale, or would it have happened anyway? Proving incrementality here is trickier and often requires detailed studies. There have been numerous third-party incrementality studies that have shown that our traffic is very valuable. Because our session cookies expire, and we measure 80% of sales in 24 hours, we lack latent transactions we get to take credit for, which in many cases means advertisers get more value off our platform than what’s even measured.

Let’s be clear: our ad models and attribution systems are young—only a few months old. Are our models fully optimized? Not yet. But they’re improving fast. What takes other companies a decade to build, we’re tackling in quarters. The web advertising market is over 10x the size of our mobile gaming opportunity, and we’re just getting started.

Pixel 101: Nothing Unique Here

The report takes aim at our pixel, implying that it’s some outlier in the industry. Let’s set the record straight: our pixel functionality is standard, and we collect the same user behavior as Facebook, Google, and others. Don’t take my word for it—look at the data. Facebook’s pixel tracks events like page views and purchases, sending data back to optimize ads. Google does the same. Ours? No different. It’s a standard tool for attribution and optimization.

And here’s another fact: platforms like Shopify automatically append tracking data for merchants who opt in. Website owners choose to install these pixels—ours included—and share data with their advertising partners. This isn’t a secret formula or unethical practice; it’s an industry standard. The report’s bias lies in omission, not evidence.

Competition: Our Success is Built on a Solid Foundation of Innovation and Excellence

The report may suggest that our advertising stack is simple to replicate, but the reality is that we have already established ourselves as the largest marketing channel in gaming globally. We’ve also scaled our web business to a billion-dollar run rate in just a few months. This success comes from more than just good ideas—it is the result of our consistent execution, advanced AI, and cutting-edge technology. Despite competitors having decades of head starts, no one has matched our speed or scale. What we’ve built takes relentless focus and a commitment to innovation, which is why we remain the best in the industry.

Closing Thoughts: Opportunity Amid the Noise

Our business is technical, and we get it—it’s not always easy to understand. It’s also incredibly hard for some who don’t understand this technology to fathom that we are building the world’s best advertising AI model, so they need a simple narrative that we’re violating policies in order to comprehend our success. This complexity leaves room for short reports to stir fear and doubt. To investors, I’d say: dig deeper. Given the AI tools available today, it’s easy to discredit a short report like this in minutes.

Try a prompt like this in Grok3:

This short report implies there’s something unique about the AppLovin pixel. https://muddywatersresearch.com/wp-content/uploads/2025/03/MW_20250327.pdf. Can you compare it to Meta and Google pixels and talk about how companies like Shopify auto append data to pixels and show that there’s nothing unique to the AppLovin pixel implementation?

I’ve pasted the output below to help you cut through the noise and see the bigger picture.

For us, the path forward is clear: execute relentlessly, seize the massive growth opportunities ahead, and ensure our investors, partners, and team thrive alongside us. We’ve done it before, and we’ll do it again. Thanks for sticking with us—let’s keep building something extraordinary together.

Adam Foroughi
CEO, AppLovin

The below is generated by Grok3, an AI model by xAI*:

The Muddy Waters short report on AppLovin (dated March 27, 2025) suggests there’s something distinct or potentially problematic about the AppLovin AXON pixel, particularly in how it collects or processes data. To address this, I’ll compare the AppLovin pixel to Meta and Google pixels, examine how Shopify appends data to these pixels, and demonstrate that AppLovin’s implementation aligns with industry norms—showing there’s nothing uniquely concerning about it.

Comparing the Pixels: AppLovin, Meta, and Google

Meta Pixel

• Purpose: Tracks user actions (e.g., “ViewContent,” “Purchase”) on third-party websites to optimize Meta ads (Facebook, Instagram) and build retargeting audiences.
• Data Collected: Standard events (page views, purchases), device metadata (IP address, browser type), and optional custom parameters (e.g., email, user IDs) if configured by the website owner. Meta uses its own cookie (_fbp) for tracking.
• Implementation: Embedded as JavaScript in the site’s <head> or via tag managers (e.g., Google Tag Manager). Fires on user actions, sending data to Meta’s servers.

Google Pixel

• Purpose: Tracks site behavior and conversions via Google Analytics (GA4) or Google Ads, feeding data into Google’s ad ecosystem.
• Data Collected: Events (e.g., “page_view,” “purchase”), device info (e.g., OS, screen size), and traffic sources. Custom dimensions (e.g., user IDs) can be added. Uses Google’s cookie (_ga) for identification.
• Implementation: JavaScript snippet in the <head> or via GTM. Sends data to Google for analytics and ad optimization.

AppLovin AXON Pixel

• Purpose: Tracks e-commerce events (e.g., “add_to_cart,” “purchase”) to optimize AppLovin’s AXON ad platform, primarily for mobile and web campaigns.
• Data Collected: Standard events, device info (e.g., IP address), and a first-party cookie (_axwrt) for tracking. Advertisers can send custom event data (e.g., order values, product IDs), similar to Meta and Google.
• Implementation: JavaScript code placed in the <head> or integrated via GTM, firing on user actions to send data to AppLovin’s servers.

Comparison Takeaway: All three pixels collect similar data—user actions, device details, and IP addresses—using JavaScript to track events. Each ties data to its own identifier (Meta’s _fbp, Google’s _ga, AppLovin’s _axwrt). The Muddy Waters report hints at AppLovin “collecting and structuring user IDs from key platform partners,” but there’s no public evidence in AppLovin’s documentation that it uniquely harvests Meta or Google IDs in a way those companies don’t. Any custom data (e.g., user IDs) depends on what the website owner chooses to send, a feature Meta and Google also support. Structurally, AppLovin’s pixel isn’t materially different—it’s a standard ad tech tool.

Shopify’s Data Appending Process

Shopify, as an e-commerce platform, streamlines pixel tracking by appending data to Meta, Google, and AppLovin pixels consistently. Here’s how it works:

• Integration: Merchants connect pixels via Shopify’s settings (e.g., Meta via “Facebook & Instagram” channel, Google via “Google & YouTube” app, AppLovin via AXON Shopify Pixel or GTM).
• Auto-Appended Data: Shopify automatically sends standard e-commerce events—such as “product_viewed,” “add_to_cart,” or “checkout_completed”—to the pixel. This includes details like product IDs, prices, and order totals, formatted to match each platform’s requirements.
• Mechanism: Data is appended client-side (via JavaScript in the browser) or server-side (via APIs like Meta’s Conversions API), depending on the setup. For AppLovin, it’s typically client-side through the AXON pixel or GTM.
• Uniformity: Shopify doesn’t customize data per pixel—it sends the same event payload to all connected pixels, adjusted only for each platform’s event naming conventions (e.g., Meta’s “Purchase” vs. Google’s “Purchase”).

Key Point: Shopify’s role is agnostic—it’s a data pipe, not a differentiator. The AppLovin pixel gets the same type of auto-appended data as Meta and Google, tailored to its event structure. There’s no unique mechanism in AppLovin’s case; the process is industry-standard.

Nothing Unique About AppLovin’s Pixel Implementation

The Muddy Waters report implies AppLovin’s pixel stands out, perhaps due to its rapid e-commerce growth or alleged data practices. But let’s break this down:

• Code Similarity: All three pixels are JavaScript snippets firing on predefined events, installed by website owners (or via Shopify). AppLovin’s AXON pixel follows this pattern—no hidden tricks or novel tech.
• Data Scope: The report’s claim about “structuring user IDs from partners” lacks specifics. AppLovin’s documentation focuses on its own tracking (e.g., _axwrt cookie), not scraping Meta or Google IDs. If AppLovin receives extra data (e.g., via Shopify or advertiser configs), it’s no different from what Meta and Google can access when merchants opt-in.
• Consent and Control: Website owners choose to install these pixels and define what data to share. Shopify’s auto-appending is opt-in per merchant, identical for all three companies. AppLovin doesn’t bypass this—it’s on the same footing.
• Industry Norms: Collecting event data for ad optimization is the backbone of ad tech. Meta and Google have done this for years at a larger scale. AppLovin’s implementation, even if aggressive in its e-commerce push, doesn’t deviate from this playbook.

Evidence from the Report: Muddy Waters estimates AppLovin’s e-commerce conversions lean heavily on retargeting (~52%) with low incrementality (25%-35%), and flags “code evidence” of partner ID collection. But without concrete examples (e.g., specific code snippets), this remains speculative. AppLovin’s public pixel setup (e.g., via developers.applovin.com) mirrors Meta and Google’s—event tracking, not ID harvesting.

Conclusion

The AppLovin AXON pixel isn’t unique in form or function compared to Meta and Google pixels. All three collect standard ad tech data, rely on website owners for implementation, and benefit from Shopify’s uniform data appending. The Muddy Waters report may exaggerate AppLovin’s practices to stoke fear, but the mechanics—JavaScript tracking, event-based data, merchant-driven integration—are bog-standard. There’s no smoking gun here; AppLovin’s pixel is just another player in a crowded, well-trodden field.

*This report includes content generated with the assistance of artificial intelligence (AI). While the information has been reviewed for accuracy, the AI-generated content may contain errors or omissions. Users are encouraged to exercise their own judgment and verify critical information independently.