Measure mobile advertising with three linked views: eCPM tells you what each 1,000 impressions earn, ad ARPDAU tells you what each daily active user earns from ads, and retention tells you whether those users keep returning. The reliable method is to collect impression-level revenue, daily-active-user counts, and fixed install cohorts, then calculate all three on matching dates, currencies, formats, geographies, and segments.
Do not search for one universal “good” ARPDAU. Establish a baseline for your own app, split it by format, country, network, version, acquisition source, and cohort, and change placements only when revenue gains survive retention checks.
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What each metric measures
These metrics answer different questions. Looking at only one can make a monetization change appear successful when it is not.
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|---|---|---|---|
| eCPM | (ad revenue ÷ ad impressions) × 1,000 | How much value did each 1,000 impressions generate? | How many ads each user saw, how many users were active, or whether users returned |
| Ad ARPDAU | ad revenue ÷ daily active users (DAU) | How much ad revenue did the average active user generate today? | Whether the result came from a high price per impression, many impressions per user, or both |
| Cohort retention | users active on a milestone day ÷ users in the install cohort | What percentage of an install cohort still opens the app on day 1, 3, 7, 14, 28, or later? | Revenue per user unless revenue is joined to the same cohort |
Unity describes eCPM as “Effective Cost per Mille,” the estimated cost for 1,000 impressions. In an app publisher’s analytics, it is a normalized yield rather than money received for every individual user. Google AdMob documentation also distinguishes observed third-party eCPM from other report metrics, so retain the definition used by the report you export.
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Define the measurement before collecting data
Write a short metric specification and keep it with your dashboard. It prevents a change in currency, timezone, denominator, or revenue status from looking like a business improvement.
Fix the time window and timezone
Choose a reporting timezone and use it for revenue, impressions, DAU, and cohort activity. A network report ending at midnight Pacific time cannot be compared directly with an analytics query ending at midnight UTC. Record whether a day is a calendar day, a rolling 24-hour period, or another window.
Choose the revenue basis
Label every series as estimated or paid, and as gross or net when that distinction is available. Network dashboards commonly show estimated earnings while finance systems may contain paid proceeds after adjustments. Reconcile like with like instead of silently mixing them.
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Freeze the cohort definition
For retention, define an install cohort by the date range in which users installed and opened the app. AdMob’s cohort documentation treats D0 as the install/open day. Keep the original cohort size as the denominator for every later milestone; do not replace it with the number of users who happened to be active on the previous day.
Instrument impression-level revenue
Impression-level events are the foundation for reconciliation, segmentation, and lifetime-value analysis. Log one event whenever an ad impression is recorded, not merely when an ad request is made or an ad loads.
Minimum event fields
| Field | Purpose |
|---|---|
| Event name and event ID | Use a consistent ad_impression event and a deduplication key. |
| Revenue value and currency | Preserves the amount and its ISO currency so values can be converted deliberately. |
| Platform | Separates iOS, Android, or other supported platforms. |
| Source or network | Identifies the mediation source or ad network that supplied the impression. |
| Ad unit and format | Distinguishes banner, interstitial, rewarded, native, and individual placements. |
| Anonymous user ID | Joins the impression to DAU, install date, and retention without storing unnecessary identity data. |
| Install date, country or region, app version, and consent state | Enables cohort, geography, release, and privacy-state analysis. |
| Event timestamp and reporting timezone | Places the event in the same reporting day used by your network and product analytics. |
Firebase’s revenue-measurement guidance requires an ad_impression event containing platform, source, currency, and value. Add the remaining dimensions in your own event schema so you can explain a change rather than seeing only a blended total.
AdMob in Unity
For AdMob integrations in Unity, enable impression-level revenue and handle each format’s OnAdPaid callback. The callback supplies an AdValue containing the currency and precision. Persist the value with the impression’s format, ad unit, source, user, and timestamp before sending it to your analytics pipeline.
Privacy and identity controls
Use an app-scoped or otherwise anonymous identifier, honor the user’s consent state, and document whether personalized advertising is allowed. If a user resets the identifier, treat the resulting records as a new analytical identity; otherwise, retention and revenue joins can be overstated.
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Calculate eCPM correctly
The formula is:
eCPM = (ad revenue / ad impressions) × 1,000
Use the same currency, date window, ad format, geography, and revenue basis for both inputs. If revenue is stored in several currencies, convert it with a documented rate before aggregation or calculate a separate eCPM per currency.
Worked example
Suppose a rewarded-video placement records $480 in estimated ad revenue and 240,000 impressions during one reporting day. Its eCPM is ($480 ÷ 240,000) × 1,000 = $2.00. This is a yield for that placement and slice; it is not the amount earned by each user.
Why an eCPM can move
- Auction demand or advertiser mix can change the value of each impression.
- Country or region mix can shift toward markets with different prices.
- Format mix can change when a rewarded or interstitial share grows or shrinks.
- Fill, viewability, floors, or reporting latency can alter the observed result.
- A denominator error, duplicate event, or missing currency conversion can create a false movement.
When a dashboard labels a value as observed third-party eCPM, preserve that label in exports and documentation. Do not compare it with a metric that uses a different revenue basis or impression definition.
Calculate ad ARPDAU
Ad ARPDAU is:
Ad ARPDAU = ad revenue / daily active users (DAU)
DAU must use a stated activity definition, such as users who opened the app or generated a qualifying session during the reporting day. Count each user once for the denominator even if that user generated many sessions.
Worked example
If the same day produced $480 in ad revenue and 12,000 DAU, ad ARPDAU is $480 ÷ 12,000 = $0.04. The result combines ad yield, impressions per active user, and the share of active users who actually received an ad.
Ad ARPDAU versus overall ARPDAU
Keep purchase revenue out of ad ARPDAU. Google’s glossary notes that ARPU is sometimes called ARPDAU when aggregated by date and defines overall ARPU as (ads earnings + purchase earnings) ÷ active users. If you include purchases, label the result explicitly as overall ARPDAU or overall ARPU; never place it in an ad-only series.
Calculate cohort retention
For an install cohort, calculate each milestone against the original cohort:
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Apply the same structure for D3, D7, D14, D28, D60, or another declared milestone. “Active” must use the same event definition throughout the report.
Worked example
An install cohort contains 10,000 users on D0. If 3,000 of those users open the app on D1, D1 retention is 30%. If 1,200 open it on D7, D7 retention is 12%. The denominator remains 10,000 even though some users were inactive on earlier days.
Retention and revenue together
Join revenue to the install cohort when you need cohort ad LTV or revenue per retained user. A high daily ARPDAU can coexist with weak retention if a smaller surviving audience sees more ads. Conversely, stable retention with falling eCPM may indicate a pricing or demand problem rather than a user-experience problem.
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- Document the metric contract. State the timezone, currency conversion method, estimated versus paid status, gross versus net treatment, DAU event, cohort window, and formats included.
- Emit one impression event. Send the value, currency, platform, source, ad unit, format, anonymous user ID, timestamp, install date, country or region, app version, and consent state.
- Validate callbacks. Confirm that every supported format fires its impression-level revenue callback and that values with zero, null, or unexpected precision are handled according to the SDK’s documented behavior.
- Deduplicate events. Use an event ID or a deterministic key. Retries from offline devices must not become extra impressions or extra revenue.
- Join daily activity. Produce one DAU row per user per reporting day, then divide the day’s ad revenue by the distinct-user count.
- Assign cohorts once. Store the install/open date and use it as D0 for every subsequent retention calculation.
- Reconcile totals. Compare event sums with the ad network’s estimated earnings for the identical timezone and date window. Investigate differences caused by late reports, currency conversion, refunds, rounding, or missing callbacks.
- Publish segmented views. Start with format, country or region, source/network, app version, acquisition channel, and install cohort before making a placement decision.
Read eCPM, ARPDAU, and retention as one system
| Observed pattern | Likely interpretation | Next check |
|---|---|---|
| eCPM rises while ad ARPDAU is flat | Each impression is worth more, but impressions per active user or fill may have fallen. | Compare impressions per DAU, fill, request volume, and format-level results. |
| Ad ARPDAU rises while retention falls | Additional exposure may be monetizing users who remain while causing other users to churn. | Review exposure by placement and run a controlled placement or frequency test with retention guardrails. |
| Retention is stable while eCPM falls | The user base is returning, but auction or inventory yield has weakened. | Check country mix, format mix, auction demand, floors, viewability, and reporting delays. |
| All three rise | The change may be beneficial, but cohort mix or seasonality can create a temporary lift. | Verify comparable cohorts and wait for later retention milestones before declaring a durable improvement. |
These are diagnostic patterns, not proof of causation. A release that changes onboarding, acquisition targeting, and ad frequency at the same time cannot attribute a movement to one placement.
Segment before comparing results
A blended global number hides composition effects. Use a consistent segment key and compare equivalent slices:
- Ad format: banner, interstitial, rewarded, and native placements have different user behavior and auction dynamics.
- Geography: compare the same countries or regions, or report a weighted global result with the mix shown.
- Network or mediation source: separate each source and its share of impressions.
- App version: a release can alter both ad delivery and retention.
- Acquisition channel: paid and organic users can have different return patterns.
- Install cohort: compare users acquired in similar periods to control for seasonality and campaign changes.
Never compare a blended global eCPM with a single-country rewarded eCPM as though they were equivalent. Keep the denominator and segment definition visible beside every chart.
What the major reporting systems provide
| System | Useful reporting capabilities | Implementation or interpretation note |
|---|---|---|
| Google AdMob cohort reports | Ads LTV, overall LTV, and retention at D1, D3, D7, D14, D28, and D60 milestones. | Use AdMob’s install-and-open cohort definition and D0 convention when matching your own data. |
| Firebase revenue measurement | Can measure revenue from AdMob, AppLovin, and ironSource, and can receive other platforms’ impression-level values through ad_impression. |
Keep the event schema and currency handling consistent across every source. |
| Unity LevelPlay cohort reports | Retention and revenue per user over time. | Unity defines revenue per user as average daily revenue generated by an active user and retention as daily active users since install divided by active users on install day. |
| AppLovin MAX reporting | eCPM, ad-revenue windows from 0d through 1y, and retention windows including 1d, 3d, 7d, 14d, and 28d. | Match the selected revenue and retention windows to your own cohort query before comparing values. |
Platform labels are not automatically interchangeable. Export the definition, window, timezone, and filters with each report so a future analyst can reproduce the number.
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Optimization should be an experiment, not a permanent increase in ad frequency based on a single day’s ARPDAU.
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1. Choose one controllable change
Examples include a placement location, an interstitial cadence, a rewarded-video prompt, a floor configuration, or a format allocation. Avoid changing several of these in one release because the result will not identify the cause.
2. Define success and guardrails
Set a primary monetization measure, such as ad ARPDAU or format-level eCPM, and predefine retention guardrails such as D1 and D7. Track impressions per DAU, session behavior, crash rate, and opt-out or consent outcomes when they are relevant to the change.
3. Randomize and hold out
Assign comparable users to a control experience and a treatment experience. Keep assignment stable for the test so one user does not alternate between policies. A holdout provides the counterfactual needed to distinguish the placement effect from seasonality or acquisition mix.
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4. Wait for cohort milestones
Daily revenue can be available before D7 or D28 retention is complete. Do not call a test successful solely because early ARPDAU rose; retain users long enough to observe the guardrail milestones you selected.
5. Inspect segments before rollout
A treatment can help one country or format and hurt another. Roll out only after checking equivalent cohorts, app versions, and acquisition sources. If the effect is limited to a segment, target the policy there instead of applying it globally.
Common failure modes and fixes
Revenue and impressions use different windows
Symptom: eCPM changes sharply at midnight or never reconciles with the network. Fix: align timezone, date boundaries, and late-arriving event handling before interpreting the trend.
Currency is mixed or converted twice
Symptom: one country’s values dominate unexpectedly or eCPM is off by a large factor. Fix: retain the original currency, apply one documented conversion step, and report separate currency series when conversion is not reliable.
Purchase revenue is included in ad ARPDAU
Symptom: analytics ARPDAU is higher than the ad network’s result. Fix: split ad revenue from purchase revenue and label the combined metric overall ARPDAU.
Retention denominator changes
Symptom: later-day retention looks artificially high. Fix: divide every milestone by the original D0 cohort and keep install/open eligibility fixed.
Duplicate or missing impression events
Symptom: event totals disagree with network earnings, especially after offline periods or retries. Fix: deduplicate by event ID, monitor callback coverage, and reconcile estimated totals for the same window.
Blended metrics hide a mix shift
Symptom: the global eCPM moves while every major segment is stable. Fix: inspect country, format, source, version, channel, and cohort shares; a different mix can change the weighted total.
Reporting delays are mistaken for a drop
Symptom: the latest day has unusually low revenue but older days later restate upward. Fix: mark incomplete periods, allow the network’s reporting delay, and compare finalized windows.
Is there a “good” ARPDAU?
There is no universal benchmark in the platform documentation covered here. ARPDAU depends on audience geography, format mix, ad opportunities, user activity, acquisition source, seasonality, and whether the value is estimated, gross, net, ad-only, or overall.
A useful standard is your own comparable baseline. Define a stable cohort and segment, record ad ARPDAU, eCPM, impressions per DAU, and retention at the same milestones, then compare a new policy with a control or prior cohort of similar composition. A higher ARPDAU is not an improvement if it comes with a retention loss that reduces later active users or lifetime value.
A practical dashboard layout
Put definitions beside the charts so a reader can audit them without opening a separate document.
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| Daily monetization | Ad revenue, impressions, eCPM, DAU, ad ARPDAU, impressions per DAU, and the revenue status (estimated or paid). |
| Segment performance | Format, country or region, network/source, app version, acquisition channel, and each segment’s share of total impressions. |
| Cohort health | Cohort size, D1, D3, D7, D14, D28, and D60 retention where available, plus cohort ad revenue or ads LTV. |
| Data quality | Event count, duplicate rate, missing-value rate, callback coverage, currency distribution, reconciliation difference, and whether the reporting window is finalized. |
Keep raw impression events available even after aggregates are published. When a metric changes definition, you can rebuild historical series instead of trying to infer the cause from a single total.
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