Snap is leveraging technology — particularly AI and machine learning — to enhance its platform performance, advertising capabilities, and user experience.
In Q1, Snap reported total revenue of $1.363 billion, a 14 percent increase. Advertising revenue rose 9 percent to $1.211 billion, driven mainly by a 14 percent growth in Direct Response (DR) advertising. However, brand advertising revenue declined by 3 percent due to weaker demand and a shift toward performance-based advertising. DR advertising accounted for 75 percent of total ad revenue for the first time. Snap also saw strong growth in its SMB segment, contributing to a 60 percent increase in active advertisers.
“We surpassed an important milestone in Q1, with our community growing to over 900 million monthly active users,” said Evan Spiegel, CEO of Snap Inc.
Snapchat’s users
Snap’s active users reached 460 million in Q1, an increase of 7 million from the previous quarter and 38 million (or 9 percent) year-over-year. The growth in Snap’s active users is largely driven by user adoption in less mature markets where user-based increased from 226 million to 262 million.
In contrast, Snap’s user numbers in North America declined from 100 million to 99 million, and Europe remained flat at 99 million, signaling saturation in those regions. To stimulate growth in mature markets, Snap is focusing on product innovation, AI-driven content personalization, and strengthening its creator ecosystem.
Investment in Artificial Intelligence
Artificial intelligence is playing a central role in Snap’s strategy to boost engagement and subscriber growth. The company continues to invest in machine learning models that improve content ranking and personalization. These enhancements have led to more relevant and timely content for users, as evidenced by the doubling of Spotlight views on posts less than 24-hours old. Additionally, Snap has increased the speed at which its algorithms adapt to new user behavior and emerging trends, with the ultimate goal of achieving near real-time model refreshes.
Conversational AI has seen increased adoption, particularly with Snap’s My AI product, now powered by Google Gemini’s multimodal capabilities. This has improved image understanding and responsiveness, resulting in a 55 percent year-over-year increase in My AI’s daily active users in the U.S.
Snap is using AI-driven insights to test and refine user interface designs, making the app more accessible for new and casual users. For instance, design changes like integrating Spotlight more prominently and adding Stories to the chat experience led to increased content consumption among casual users. These data-informed iterations are shaping a new five-tab layout that blends familiar navigation with improved content discovery.
Creator ecosystem
The creator ecosystem is another key area of growth, supported by AI tools that boost content visibility and engagement. The number of Spotlight posts by Snap Stars grew more than 125 percent year-over-year in North America.
Many of these creators, such as Bridgette Ugarte, have built large followings organically, enabled in part by Snap’s content discovery algorithms.
Augmented reality remains a strong differentiator for Snap, supported by more than 400,000 AR creators and over 4 million Lenses. AI enhancements in Lens Studio — like hand tracking and gesture recognition — are enabling more sophisticated and engaging AR experiences. These innovations, coupled with incentives like Spectacles Community Challenges, continue to foster creativity and platform stickiness.
In Q1, the company advanced its machine learning infrastructure significantly by improving model freshness and scaling up training data. These changes accelerated the learning rate of its models by six times and increased the volume of historical interaction data used in training by more than five times. This enabled deeper personalization for users and better targeting for advertisers.
One of the most notable updates was the consolidation of multiple app-based advertising models into a single, integrated system. This unified model uses a broader spectrum of performance signals, aligning optimization more closely with advertiser goals such as app conversions. As a result, Snap has delivered more relevant ad experiences and driven higher conversion outcomes, including a more than 30 percent year-over-year increase in SKAdNetwork-reported app purchases.
AI-powered optimization
Beyond core machine learning improvements, Snap is using automation and AI-powered optimization to support lower-funnel performance. Enhancements to the automated Target Cost (tCPA) bidding strategy have allowed advertisers to scale efficiently while meeting specific cost-per-action goals.
Advertisers using tCPA have reported strong results, including a 32 percent drop in cost per purchase and a 16 percent lift in return on ad spend (ROAS). Real-world examples like Headspace demonstrate the effectiveness of these improvements, achieving a 2x increase in conversions with a 47 percent more efficient CPA.
Snap also introduced new high-intent Dynamic Product Ads that blend performance with storytelling, allowing advertisers to showcase a curated set of products in visually compelling formats that drive better engagement and conversion metrics.
The company is also expanding its global marketing tech partnerships to deliver automated solutions that enhance ad performance. Collaborations like the one between ROI Hunter and fashion retailer 6thStreet.com have led to a 76 percent increase in ROAS and a 22 percent drop in CPA by leveraging Snap’s Dynamic Product Ads. Signal quality is another area where Snap is applying technical focus.
In Q1, the number of advertisers with robust signal setups increased by 29 percent among large advertisers and 48 percent among mid-sized ones. Adoption of Snap’s Conversions API (CAPI) has also grown significantly, now accounting for over 60 percent of all direct response ad revenue. Advertisers with deeper CAPI integration, such as Foot Locker, have seen transformative outcomes — a 49 percent reduction in CPA and more than a 100 percent increase in ROAS.
To diversify and enrich its ad offerings, Snap launched Sponsored Snaps in Q4 2024, with limited testing as a biddable auction product beginning in Q1. Initial tests, which focused on optimizing for Pixel Purchase, showed promising performance results, even though revenue impact remained modest due to limited scale.
Snap plans to expand Sponsored Snaps to more regions and bidding strategies in the future. Overall, Snap’s use of AI, machine learning, and automation is embedded in its advertising and performance infrastructure, driving measurable improvements in personalization, efficiency, and return on investment for its partners.
Baburajan Kizhakedath
