User and Engagement Metrics: DAU, MAU, and Monetization

Introduction

For internet companies that monetize through advertising, user and engagement metrics are the fundamental drivers of valuation. Unlike SaaS companies where revenue is contractually committed through subscriptions, ad-supported internet companies generate revenue based on the size and engagement intensity of their user bases. More users spending more time on the platform generates more ad inventory, which generates more revenue. For TMT analysts, understanding how to measure, benchmark, and project user and engagement metrics is essential because these metrics form the top of the analytical framework that flows through to revenue, profitability, and ultimately enterprise value.

DAU, MAU, and the Stickiness Ratio

Daily Active Users (DAU) measures the number of unique users who engage with the platform on a given day. Monthly Active Users (MAU) measures unique users over a 30-day period. The stickiness ratio (DAU/MAU) reveals what percentage of monthly users engage on any given day. A stickiness ratio of 50% means that half of monthly users are active every day, indicating strong habitual engagement. The definition of "active" varies by platform (opening the app, performing a specific action, making a transaction), so TMT analysts must verify each company's definition before comparing metrics across peers. Inconsistent definitions are a common source of analytical error.

These metrics serve different analytical purposes. DAU captures daily engagement intensity and is the primary metric for platforms that monetize through frequent interactions (social media, messaging, content consumption). MAU measures the breadth of the user base and is more relevant for platforms where users engage less frequently but with higher intent (e-commerce, travel booking, financial services). The stickiness ratio connects the two, revealing whether a large MAU base translates into habitual daily usage or represents a large but lightly engaged audience.

Industry benchmarks for stickiness vary dramatically by platform type:

Meta reported 3.35 billion daily active people across its family of apps (Facebook, Instagram, WhatsApp, Messenger) against 3.98 billion monthly active people in early 2025, translating to an 84% stickiness ratio, one of the highest in the industry and a reflection of how deeply embedded Meta's products are in daily communication and content consumption. TikTok's estimated 875-954 million DAU against approximately 1.59 billion MAU (roughly 55-60% stickiness) demonstrates strong engagement, with average session times exceeding 60 minutes daily.

Average Revenue Per User (ARPU)

Total revenue generated in a period divided by the average number of users (typically MAU or DAU) in that period. ARPU translates user metrics into financial value and is the primary metric for comparing monetization efficiency across internet companies. Higher ARPU indicates either better ad targeting (commanding higher CPMs/CPCs), higher ad load (more ads per user session), additional monetization streams (subscriptions, e-commerce, payments), or some combination of all three.

ARPU analysis reveals two critical dimensions: monetization maturity and geographic variation.

Monetization maturity is visible in ARPU trends over time. A platform increasing ARPU while maintaining or growing its user base is improving monetization efficiency, typically through better ad products, higher ad load, or additional revenue streams. Meta's global ARPU grew from $39.63 in 2022 to $44.60 in 2023 to $49.63 in 2025, reflecting continuous improvement in ad targeting (driven by AI) and the addition of new ad surfaces (Reels, Threads).

Geographic variation in ARPU also creates strategic implications. A platform with strong engagement in high-ARPU markets (US, UK, Western Europe) but limited emerging market penetration has a different growth and valuation profile than one with broad global reach but concentrated revenue in North America. The former has higher near-term monetization potential; the latter has larger user growth potential but faces the challenge of monetizing users in markets with lower advertising demand.

Beyond DAU, MAU, and ARPU, TMT analysts evaluate engagement depth through several additional metrics that reveal the quality of user interaction.

Time spent per user measures how long users engage with the platform in each session or per day. This metric directly determines ad inventory: a platform where users spend 45 minutes daily generates far more ad impression opportunities than one where users visit for 3 minutes. TikTok's dominance of time spent (exceeding 60 minutes per daily session on average) is a key competitive advantage that translates directly into advertising inventory and revenue capacity.

Sessions per day captures how frequently users return to the platform within a single day. Multiple daily sessions indicate that the platform has become embedded in the user's routine (checking social feeds, messaging friends, searching for information), which creates more ad inventory opportunities and stronger user retention over time.

Content creation and interaction rates measure the percentage of users who create content (posts, videos, reviews) versus passively consuming it. Platforms with higher content creation rates benefit from user-generated content that attracts other users and generates engagement without the platform bearing content costs. This is a structural advantage over platforms that must pay for professional content (streaming services, news platforms).

TMT analysts construct user-based valuation frameworks by combining user metrics with monetization analysis. The core framework calculates enterprise value per user (EV/MAU or EV/DAU) and compares this metric across comparable platforms, adjusting for differences in engagement intensity, ARPU, and growth trajectory.

A platform trading at $500 EV/MAU with ARPU of $50 and 25% annual user growth might be valued comparably to a platform at $300 EV/MAU with ARPU of $30 and 15% user growth, because the ratio of value per user to revenue per user is similar. The advantage of user-based valuation is that it captures the value of users who are not yet fully monetized, reflecting the option value of future monetization improvements.