AppLovin Touts Upside in Gaming Ads, Teases Broader E-Commerce Launch by H1 at Morgan Stanley Conference
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AppLovin reaffirmed its gaming-ads growth benchmark of 20%–30% and said there is “definitely potential upside,” attributing gains to directed model enhancements and recursive learning as scale feeds better performance.
The company is iteratively expanding into web and e-commerce with universal, new-customer and new-visitor campaigns and expects a broader release in the first half of this year, estimating the outside-gaming addressable market at roughly 5x to 10x the size of gaming.
Management highlighted monetization efficiency—serving ads to over 1 billion users a day, a current 1.3% conversion rate (and >5% in some gaming moments), extensive rewarded-video usage—and a lean operating model (~900 employees) with only modest hiring planned for e-commerce expansion.
AppLovin (NASDAQ:APP) executives used a Morgan Stanley conference appearance to reiterate their view that the company’s core gaming advertising business still has meaningful runway, while outlining how a broader push into web and e-commerce advertising could expand the platform over time. CEO Adam Foroughi and CFO Matt Stumpf also discussed investment needs, competition, and how they think investors should measure progress as new products roll out.
Stumpf revisited the company’s frequently cited benchmark of 20%–30% growth for gaming ads, saying there is “definitely potential upside.” He framed the original target as a way to “level set with investors” at a time when the market “didn’t think we could grow at all.”
Stumpf attributed the upside to continued “directed model enhancements” and “recursive learning,” arguing that the ad tech remains “very nascent.” As the platform scales, he said, more data feeds the models, improving performance in a way that can “stack and compound.” He added that expansion into categories such as e-commerce should further improve the technology’s ability to drive advertiser spend at targeted returns.
Foroughi described the company’s approach outside gaming as an iterative product build aimed at delivering measurable, profitable marketing. He said AppLovin’s success in gaming came from deeply understanding developers’ core problem—spending marketing dollars with confidence they would earn more back—and building a recommendation system to automate that. He called the model “one of the world’s most powerful” and said it operates at “larger scale than anyone else in the world” for gaming advertisers.
In e-commerce, Foroughi said the market is more complex because advertisers typically combine discovery, search/bottom-of-funnel tactics, and CRM. He walked through product steps AppLovin has taken over roughly the last year and a half:
Universal campaigns: described as a mix of discovery and retargeting, leveraging AppLovin’s “1 billion+ daily active users” in games.
New customer campaigns (rolled out in late October): model-driven targeting aimed at finding customers who have not purchased before, but may have visited a site.
New visitor campaigns (rolled out the prior week): intended to bring shoppers who have “never seen [the site] before,” which Foroughi argued is the most clearly incremental form of advertising; he said pilot results were strong and adoption was swift.
Foroughi emphasized that the company is still in a “closed state” for the platform but expects to reach a broader release in the first half of this year. He said the addressable market outside gaming could be “5x to 10x the size of the gaming market,” while reiterating that his priority is building “the biggest company possible 10 years from now,” even if that means investors get less near-term detail than they want.
Asked what investors should focus on instead of near-term customer counts or sequential revenue, Foroughi pointed to monetization efficiency across the company’s ad impressions. He said AppLovin serves ads to over 1 billion users a day and described the ads as “attention-grabbing,” noting that ads are watched “over 30 seconds” on average and that roughly half of ads served are rewarded video where users opt in to watch.
Foroughi said the platform currently has a 1.3% conversion rate—meaning it “make[s] money on 1.3% of the ads we serve”—while noting that in certain “high-value moments” for gaming, conversion can be “well over a 5% conversion rate.” He argued that expanding advertiser diversity (and therefore auction “density”) could allow the recommendation system to better match each impression to a relevant product category, pushing overall conversion higher and lifting effective yield.
He also described AppLovin’s business structure as an auction where much of advertiser spend is paid out to publishers for ad space, while the company’s reported revenue reflects the spread. He referenced a previously discussed $11 billion run rate of advertiser spend cited around Q1 of the prior year and said the business is “materially bigger” now, adding that advertiser spend on AppLovin’s platform is larger than “the sum of everything that’s happening on Snap, Pinterest, Twitter, Reddit combined.”
Stumpf said key cost components include data center expenses, which the company has previously said grew at about 10% of overall revenue growth over the long term, and he said AppLovin is currently “beating that.” He attributed efficiency to a lean operating model where engineers monitor the cost impact of model changes in real time and focus on profitable improvements.
On staffing, Stumpf said AppLovin has around 900 employees, with roughly 400 tied to the core ad tech business and back office. He described a business development team of about 100 for core mobile gaming and said e-commerce has around 15 people. While the company expects to add headcount to support initiatives like web-based e-commerce advertising, he said additions would be “tens of people,” not a major cost shift.
Stumpf also pointed to increased focus on performance marketing to bring in new advertisers now that the product is in a “very good” place, describing it as running campaigns to drive advertisers into the platform, with discipline similar to the company’s core operating model.
Foroughi argued against aggressively scaling headcount to chase customers before product-market fit is proven, saying platforms that leaned heavily into sales sometimes “hit a wall” when value wasn’t demonstrable. He said AppLovin scaled gaming by showing advertisers they made more money per dollar spent on its platform, not by “begging for dollars.” He also discussed how large language models have improved employee productivity and referenced a prior headcount reduction of roughly 40%, saying the company focused on retaining high-caliber talent able to leverage new tools.
On competition with larger platforms such as Meta and Google, Foroughi said AppLovin’s niche is defined by a different user context and ad formats within games, and he argued the problem set is often oversimplified. In gaming, he said AppLovin predicts revenue “all the way out to 28 days,” requiring a sequence of accurate predictions (engagement, download, usage, propensity to spend, and amount of spend) to deliver advertiser value. He emphasized the importance of the company’s data and model sophistication in recommendation systems.
Foroughi also addressed how AI could affect game creation, arguing that better tools are more likely to make sophisticated developers more efficient, lowering content costs and increasing lifetime value, which could increase marketing budgets. He added that even if more new games are created, they still need discovery channels, and he positioned AppLovin’s ad experiences—where ads may run 30 seconds or more—as a way to introduce content.
On creative generation for advertisers, Foroughi said gaming advertisers can run campaigns with 50,000 ads, while e-commerce advertisers may have closer to 1,000 at the high end, creating a “50x handicap” in creative volume. He said the company is piloting generative tools for static creative and seeing promising early results, with video generation “on the way.” He tied broader availability of such tools to the platform’s expected broader release in the first half of this year.
Finally, Foroughi defended the company’s MAX mediation product, describing mediation as a tool that provides publishers access to multiple demand sources through a “completely fair, unbiased auction approach.” He said MAX was built to be unbiased, transparent, and audited, and he noted its market share gains after launch, as well as the replacement of MoPub’s technology after AppLovin acquired it. He characterized the platform’s tools as “sticky” and said competition is constant, but new innovations can also improve AppLovin’s own products.
In closing remarks, Foroughi said a key underappreciated opportunity is that recommendation systems will continue to improve alongside broader AI progress, and he suggested a major challenge is the company’s own ability to market itself, calling the name “AppLovin” a “handicap” and saying it is the company’s job to make sure more potential customers learn about the platform.
AppLovin Corporation is a Palo Alto–based mobile technology company that provides software and services to help app developers grow and monetize their businesses. The company operates a data-driven advertising and marketing platform that connects app publishers and advertisers, delivering tools for user acquisition, monetization, analytics and creative optimization. AppLovin’s technology is integrated into a broad set of mobile applications through software development kits (SDKs) and ad products designed to maximize revenue and engagement for developers.
Key components of AppLovin’s offering include an ad mediation and exchange platform that enables publishers to manage and monetize inventory across multiple demand sources, and a user-acquisition platform that helps advertisers target and scale campaigns.
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