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Measure mobile ad monetization by collecting impression-level revenue and daily-active-user data, assigning every user to a fixed install cohort, and calculating eCPM, ad ARPDAU, and retention over the same date range and segment definitions. Read the metrics together: eCPM measures yield per 1,000 impressions, ad ARPDAU measures ad revenue per active user, and retention shows whether those users keep returning.
A reliable measurement system therefore needs more than a dashboard headline. It must preserve the ad format, geography, network, app version, consent state, currency, timezone, and cohort for every observation so that a change in revenue can be separated from a change in traffic or user behavior.
What each metric tells you
The three metrics answer different questions and should not be substituted for one another. eCPM is an impression-yield measure; ad ARPDAU is a user-level monetization measure; retention is a cohort-health measure.
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|---|---|---|---|
| eCPM | (ad revenue ÷ ad impressions) × 1,000 |
Revenue normalized to 1,000 impressions | How many ads each active user saw or whether users returned |
| Ad ARPDAU | ad revenue ÷ daily active users |
Average ad revenue generated per active user on a date or over a defined period | Whether revenue came from high-paying impressions, heavy ad exposure, or both |
| Retention | users active on day N ÷ users in the install cohort |
The share of an install cohort that returns on a specified milestone | Revenue per user or the number of ads shown to returning users |
Unity describes eCPM as effective cost per mille, the estimated cost for 1,000 impressions. Google AdMob’s glossary distinguishes observed third-party eCPM from other report metrics, so record the metric definition and source whenever you import a value into your warehouse.
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Build trustworthy revenue and activity data
Calculations are only as good as the event stream behind them. Start with a consistent schema and keep raw observations available for reconciliation.
Capture every paid impression
Log one event for each impression that includes the revenue value, currency, platform, mediation or network source, ad unit, and format. Firebase’s ad_impression event requires platform, source, currency, and value fields. Preserve the event timestamp and an anonymous user identifier as well.
For the AdMob Unity integration, enable impression-level revenue and handle each format’s OnAdPaid callback. The callback supplies an AdValue containing the currency and precision. Store the precision rather than silently treating an estimated value as a cash settlement.
Attach the dimensions needed for analysis
Join each impression and activity event to an anonymous user ID, install date, country or region, app version, platform, and consent state. Add acquisition channel when it is available. These fields let you distinguish a monetization change from a country-mix change, a release effect, or a consent-rate shift.
Define the accounting basis
Document whether revenue is estimated, paid, gross, or net of fees and taxes. Use one currency for a calculation, and specify the timezone and inclusive dates. A network’s estimated earnings may not align with an in-app callback on the same calendar day because of reporting latency or timezone boundaries.
Reconcile against network totals
For every reporting period, compare the sum of impression-level events with the network’s estimated earnings for the identical date window and timezone. Investigate a persistent gap before changing ad placements. Common causes include delayed callbacks, missing events while the app is offline, duplicate event delivery, currency conversion, and a network report that uses a different revenue basis.
Freeze the cohort definition
Use a fixed install/open denominator for every retention calculation. AdMob defines a cohort as users who installed and opened during the same period, with D0 as the install or open day. Do not change the inclusion rule between releases or compare a cohort based on installs with another based on first session unless the distinction is explicit.
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How to calculate eCPM
Use the same currency, time window, ad format, and geography for both inputs:
eCPM = (ad revenue ÷ ad impressions) × 1,000
Worked example
Suppose a rewarded-video segment generated $186.00 from 62,000 impressions during one UTC day. Its eCPM is ($186 ÷ 62,000) × 1,000 = $3.00. This is a yield figure, not a prediction that every user will generate $3.
Keep the denominator comparable
- Do not divide revenue from one date by impressions from another date.
- Do not mix banner, interstitial, rewarded, and native impressions into a single number when the format mix changed materially.
- Do not compare a blended global eCPM with a single-country rewarded eCPM as though they were equivalent.
- Label whether the value is observed from impression-level revenue or reported by a mediation dashboard.
A rising eCPM can result from stronger auction demand, a richer country mix, a format change, improved viewability, or a reporting correction. It does not, by itself, prove that total ad revenue or user value increased.
How to calculate ad ARPDAU
Ad ARPDAU divides ad revenue by the number of daily active users in the same period:
Ad ARPDAU = ad revenue ÷ DAU
Worked example
If the app earns $420 in ad revenue on a day with 28,000 DAU, ad ARPDAU is $420 ÷ 28,000 = $0.015, or 1.5 cents per active user for that day.
Ad ARPDAU combines two forces: the yield of each impression and the number of impressions generated by active users. It can increase while eCPM is flat if users see more ads, or remain flat while eCPM rises if impression volume per user falls.
Separate ad ARPDAU from overall ARPU
Include purchase revenue only when you explicitly label the result as overall ARPDAU or overall ARPU. Google’s definition of overall ARPU is (ads earnings + purchase earnings) ÷ active users. Mixing purchases into an ad-only metric makes ad tests appear more successful or weaker than they really are.
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How to calculate retention by install cohort
For a cohort installed on day 0, calculate each milestone as:
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D1 retention = users active on day 1 ÷ users in the install cohort
Repeat the calculation for D3, D7, D14, D28, D60, or later milestones. The denominator remains the original cohort size; it is not the number of users who survived to the previous milestone.
Example cohort
A cohort contains 10,000 users who installed and opened on June 1. If 3,200 of those same users open the app on June 2, D1 retention is 32%. If 1,100 return on June 8, D7 retention is 11%. A user can count once on a milestone day even if they opened several times that day.
Google defines retention as the percentage of users in a cohort who continue to open the app after install. Unity’s cohort reporting uses the equivalent daily-active-user ratio: active users since install divided by active users on the install day. State whether your implementation uses calendar-day activity, a rolling 24-hour window, or another rule.
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A repeatable measurement workflow
- Choose the accounting window. Set a timezone, currency, start date, end date, and revenue basis before exporting data.
- Ingest impression events. Store value, currency, precision, platform, source, ad unit, format, timestamp, and anonymous user ID.
- Ingest activity events. Record daily activity for the same users and retain install date, country, app version, consent state, and acquisition channel.
- Normalize revenue. Convert currencies using a documented rate or keep separate currency partitions. Never add values with different currencies.
- Assign cohorts. Use the frozen install/open rule and record the cohort date on every user record.
- Aggregate by identical keys. For each date, format, country, source, app version, and cohort, sum revenue and impressions and count distinct DAU.
- Calculate the three metrics. Compute eCPM from revenue and impressions, ad ARPDAU from revenue and DAU, and retention from milestone activity and the cohort denominator.
- Reconcile. Compare event totals with network estimates and flag differences before publishing a dashboard.
- Annotate changes. Mark releases, mediation changes, floor updates, consent changes, and major acquisition campaigns so later movements have an explanation.
Segment before interpreting a change
Use the same segment dimensions for numerator and denominator. Start with the following cuts:
| Segment | Why it matters | Example question |
|---|---|---|
| Ad format | Formats have different demand, completion behavior, and user friction | Did rewarded eCPM rise while interstitial impressions fell? |
| Country or region | A geographic mix shift can change auction prices and retention | Did growth come from a lower-yield market? |
| Network or mediation source | Sources have different bidding and reporting behavior | Did one source contribute the revenue increase or only the impression increase? |
| App version | Placement code, SDK changes, and bugs are version-specific | Did the drop begin only after a release? |
| Acquisition channel | Traffic quality affects both monetization and return behavior | Is a paid campaign bringing users with lower D7 retention? |
| Install cohort | Seasonality and lifecycle effects can mask a real change | Does the lift persist across comparable cohorts? |
| Consent state | Ad eligibility and demand can differ for consented and non-consented users | Did a consent-flow change alter fill or yield? |
Only compare values with matching definitions. A global blended eCPM, for example, is not a valid control for a US-only rewarded experiment.
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Read eCPM, ad ARPDAU, and retention together
eCPM rises while ad ARPDAU stays flat
Demand price improved, but impressions per active user or fill may have fallen. Check impression count per DAU, request and fill rates, and format mix before concluding that monetization improved.
Ad ARPDAU rises while retention falls
The remaining users may be generating more revenue while additional exposure causes churn. Break the result down by placement and cohort, then validate any change with a controlled frequency or placement test.
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Investigate country mix, format mix, auction demand, floors, viewability, and reporting delays before adding more ads. Stable retention does not prove that a lower-yield setup is harmless if long-term revenue per user is declining.
All three rise
Check cohort composition and seasonality first. Confirm that the lift persists across comparable install cohorts and is not caused by a temporary event, an acquisition burst, or a change in consent coverage.
What AdMob, AppLovin MAX, and Unity LevelPlay reports can show
Platform dashboards use different names and windows, so export definitions alongside values.
| Platform report | Documented capabilities | How to use it safely |
|---|---|---|
| Google AdMob cohort report | Ads LTV, overall LTV, and retention milestones including D1, D3, D7, D14, D28, and D60 | Keep ads LTV separate from overall LTV and preserve the install/open cohort definition |
| Firebase revenue measurement | Revenue measurement for AdMob, AppLovin, and ironSource; other platforms can send impression-level values through ad_impression |
Use one event schema and reconcile Firebase totals with each network’s reporting window |
| Unity LevelPlay cohort reports | Retention and revenue per user over time; revenue per user is average daily revenue generated by an active user | Confirm whether the report’s active-user denominator matches your DAU definition |
| AppLovin MAX reporting | eCPM, ad-revenue windows from 0d through 1y, and retention windows including 1d, 3d, 7d, 14d, and 28d | Compare matching windows and cohort dates rather than mixing a same-day eCPM with a lifetime revenue window |
The presence of a metric in a platform report does not make its denominator universal. Check the report’s timezone, attribution window, estimated-versus-paid status, and treatment of late events before joining it to product analytics.
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How to increase ad revenue without sacrificing retention
Start with a controlled baseline
Choose a stable cohort and record impressions per DAU, eCPM, ad ARPDAU, and D1, D7, and D28 retention before changing placements. Randomly assign users to a control and one treatment, keep the assignment persistent, and change one major exposure rule at a time.
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Test placement and frequency separately
A placement test asks whether an ad appears at a different moment; a frequency test changes how often it can appear. Keep these experiments separate so a revenue lift can be attributed to one decision. Monitor session length, crashes, level or task completion, and return behavior in addition to the three headline metrics.
Use format-appropriate moments
Rewarded ads should be tied to an optional, clearly described benefit. Interstitials should not interrupt a critical action or a first-use flow. Banners and native units may create steady impressions but can affect layout and engagement. Evaluate each format independently before blending results.
Change floors and mediation carefully
A higher floor can improve price on filled impressions while reducing fill. A new mediation source can alter both auction competition and reporting latency. Compare request, fill, impressions, eCPM, ad ARPDAU, and retention by source and country during the same period.
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Consider a lighter exposure policy during onboarding and for users who show signs of frustration. Separate first-session retention from established-user retention so a change that helps mature users does not hide damage to the install cohort.
Troubleshoot common discrepancies
| Symptom | Likely checks | Corrective action |
|---|---|---|
| Impression events exceed the network report | Duplicate callbacks, retries, or events recorded before a final value was known | Deduplicate with an impression identifier and retain the original event for audit |
| Network revenue exceeds event revenue | Offline delivery, missing callbacks, timezone mismatch, or delayed reporting | Compare a later closed period and document the reconciliation lag |
| eCPM jumps but ad ARPDAU does not | Lower impressions per DAU, weaker fill, or a changed format mix | Inspect impressions per DAU and fill by format and country |
| Ad ARPDAU jumps while retention drops | Higher exposure, an intrusive placement, or a confounded acquisition mix | Review treatment-level retention and roll back or narrow the exposure if the controlled test confirms harm |
| Retention changes without a monetization change | App release, crash rate, acquisition source, seasonality, or cohort-definition drift | Compare app versions and acquisition channels and verify the denominator |
| Dashboard values disagree across tools | Different currencies, timezones, windows, revenue bases, or active-user definitions | Publish the definition beside every number and align the export windows |
Design a dashboard that supports decisions
Show a daily trend for revenue, impressions, DAU, eCPM, ad ARPDAU, and impressions per DAU. Add cohort curves for D1, D3, D7, D14, D28, and D60 retention where those milestones have matured. Every chart should expose filters for format, country, source, app version, acquisition channel, consent state, and install cohort.
Keep raw and derived fields separate. A useful fact table contains event timestamp, user ID, install date, country, platform, app version, consent state, source, ad unit, format, currency, value, precision, and event ID. Derived daily tables can then store revenue, impressions, distinct DAU, eCPM, ad ARPDAU, and retention counts with the exact aggregation keys.
Mark immature cohorts explicitly. A cohort installed three days ago cannot have a final D7 or D28 value; displaying it beside mature cohorts as if it were complete creates a false trend.
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There is no universal “good” ARPDAU or eCPM benchmark in the platform documentation described here. Values vary with geography, format, audience, app category, traffic source, consent rate, seasonality, and the definition of revenue and DAU. Use your own comparable historical cohorts and controlled experiments as the reference point.
A useful target is therefore stated comparatively: increase ad ARPDAU against a matched control while retaining the required D1, D7, and D28 performance, or maintain retention while improving eCPM and impressions per DAU. Record the segment and time window so the target remains reproducible.
Practical decision framework
- Revenue problem: If ad ARPDAU is low, decompose it into eCPM, impressions per DAU, and fill by format and country.
- Yield problem: If eCPM is low with stable impressions, examine demand, floors, viewability, source mix, and reporting lag.
- Exposure problem: If eCPM is healthy but ad ARPDAU is low, examine eligible sessions, placement opportunities, and impressions per active user.
- Retention problem: If revenue improves while cohort retention falls, stop broad rollout and isolate the placement, frequency, or audience segment responsible.
- Data problem: If tools disagree, resolve currency, timezone, cohort, event deduplication, and estimated-versus-paid definitions before optimizing monetization.
The defensible measurement loop is simple: capture every impression, preserve its context, calculate all three metrics on matching populations, segment the result, and test changes against retention rather than treating a higher eCPM as a complete monetization win.
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