What is average revenue per paying user (ARPPU)?

Average revenue per paying user (ARPPU) is a KPI that measures the average amount spent by each paying user over a defined period. By excluding non-payers, it isolates monetization quality.

ARPPU is calculated by dividing the total revenue from paying users in a given period by the number of unique users who made at least one purchase in that period.

For user acquisition teams, ARPPU helps compare cohorts and campaigns by average spend per payer, not just conversion rate. Channels with higher ARPPU bring in higher‑spending users, which informs budget allocation.

ARPPU is also valuable for pricing and paywall optimization. Shifts in ARPPU reveal whether new bundles, trials, or subscription tiers raise or lower average spend. Because higher ARPPU may come with fewer payers, it should always be read alongside payer rate to judge overall impact.

From a CRM and retention perspective, ARPPU supports segmentation. Marketers can group paying users into new, lapsing, or high‑value “whales” and tailor campaigns for each. Tracking ARPPU across these groups shows whether high‑value users sustain spending, whether new payers return, and whether at‑risk users need re‑engagement.

Finally, ARPPU is a core input for lifetime value (LTV) modeling. Combined with payer rate and retention, it allows teams to forecast long‑term revenue at both cohort and portfolio level.

Period ARPPU calculates average spend per payer in a set timeframe (day, week, month) and shows short‑term changes. It includes all payers active in that period, regardless of when they installed.

Cohort ARPPU calculates average spend per payer within a group defined by a shared attribute—such as install week, campaign, creative, market, or platform—to explain why the metric moves.

It’s critical to use both in practice: monitor shifts with period ARPPU, then use cohort ARPPU to diagnose and act (for example, reallocating budget or adjusting a paywall for a low‑performing cohort).

Here are the key differences between ARPPU, ARPU, and ARPDAU at a glance:

Here are top strategies to improve ARPPU. Evaluate each change with cohort ARPPU alongside payer rate and retention.

1. Optimize paywall timing and messaging: Show the paywall after the user reaches the “aha” moment, and A/B test placement, copy, and value communication.
2. Expand the price ladder: Add a higher-priced product or subscription tier and use price anchoring to shift selection toward mid- and high-tier offers.
3. Localize store pricing: Set country-specific prices to match currency, taxes, and purchasing power.
4. Use post-purchase upsells and cross-sells:. Present one-tap higher-value offers on the confirmation screen, and ensure restore-purchase flows work reliably.
5. Leverage subscription levers: Test trials, introductory offers, grace periods, renewal nudges, and upgrade paths.
6. Segment payers and personalize CRM: Encourage second purchases, re-engage lapsing users, and offer added value for high-spend “whales.”
7. Improve storefront and purchase UX: Highlight best-sellers, reduce friction in checkout, and make paid options visible in in-app storefronts and app-store product pages.
8. Target higher-value cohorts in UA: Bid toward predicted LTV or payer propensity and use creatives that attract paying users.
9. Run seasonal and event-based offers: Use limited-time bundles or live-ops events, and monitor for cannibalization or margin impact.
10. Measure and guardrail every test: Use cohort reporting, assess over multiple billing cycles, and normalize for refunds, taxes, and fees.

In Adjust, ARPPU is available in Datascape as revenue per paying user. Teams can view ARPPU alongside related KPIs (like ARPU and ARPDAU) without reconciling reports from multiple sources. It also ensures accurate comparisons by converting revenue into a single currency and applying adjustments for refunds, chargebacks, taxes, and platform fees. 

Teams can also move from high‑level overviews to detailed breakdowns, using flexible filters and customizable reports to segment by install week, campaign, creative, market, or platform, and export results directly from the dashboard. By centralizing these insights, Datascape makes it easier to identify patterns and quickly act on them with confidence.

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