New Users

Active Users

Calendar Date Monetisation

Revenue

Marketing Spend

Useful when you need a quick sense-check of your key metrics.

The Actuals values for New Users, Active Users and Revenue are exactly as supplied to the platform, so this is the best report for identifying any issues with source data.

New Users: A summary of daily new users (DNU) by calendar date (in the example below you can see the effect of applying weekday seasonality to the scenario, in a product with a strong variance in user acquisition by day of the week).

New Users (combined with actuals): As above, but taking into account any required mid-period summation (shown here using the bar-chart/quarterly preset).

The “combined with actuals” chart is best used when reviewing new users at a non-day granularity (e.g. as above), as it will show the sum of new users (actuals + forecast) in the period where the transition occurs. It does this by creating a single data series for each scenario, including both the actuals and forecast.

This view will match the regular New Users chart when daily granularity is selected, but with each scenario as its own series. This can be useful to compare scenarios that have different forecast start dates (e.g. budget vs baseline).

This chart can highlight any shifts in the retention curve over time, should scenarios be fixed at historic points.

It can also allow you to compare your current forecast retention curve on one scenario against a theoretical (override) curve on another, to determine how closely user behaviour is tracking against expectation.

The rougher part of the curve at the start represents the portion of the curve that has been fit to the actuals within the Retention Lookback period (the number of days prior to the start of the forecast selected for fitting), with the smoother part being the future extrapolation of that curve. Scenarios with different retention lookback periods will therefore smooth out at different dx points.

Day 0 is the day on which the app was installed/the user registered (whichever activity you’ve chosen to interpret as the start of the cohort in your data). This is the day on which New Users = Active Users. Day 1 retention is therefore the number of users returning on the first day after installation or registration.

If a non-daily granularity is selected, the chart will show the average daily active users over that period, and not the average weekly/monthly/quarterly/annual users. Because the Ramp model works at an aggregated user level, we do not have a view on unique users and therefore do not attempt to produce metrics like MAU.

If a non-daily granularity is selected, the chart will show the average daily revenue-generating users over that period, and not the average weekly/monthly/quarterly/annual users. Because the Ramp model works at an aggregated user level, we do not have a view on unique users and therefore do not attempt to produce metrics like ARPMAU.

When rolled up, the average user revenue is weighted by segment. .

Note that the x-axis of this report is cohort date, not calendar date. For cohorts in the Actuals range that are younger than the given window, the result shown will be a sum of the actuals to date plus the forecast CLV up to the given dx point. This is why the series will begin to deviate from a point before the end of the current calendar date.

Revenue: A summary of Revenue before marketing costs aka Gross Revenue. (Once again you can see the strong effect of in-game events in this example).

Revenue (combined with actuals): As above, but taking into account any required mid-period summation (here shown using the bar-chart/yearly preset).

Note that this is Revenue before any margins are applied in the tool settings, and is therefore assumed to be Gross Revenue. If custom margins have been applied in your feed, or if you are sending only Net Revenue, then this will show the net revenue.

The “combined with actuals” chart is best used when reviewing revenue at a non-day granularity (e.g. as above), as it will show the sum of revenue (actuals + forecast) in the period where the transition occurs. It does this by creating a single data series for each scenario, including both the actuals and forecast.

Marketing Spend: A summary of marketing spend by calendar date. The Actuals represent real UA spend provided to the platform, while the forecast range shows the future spend required to meet forecast install rates given a specified target payback period.

Marketing Spend (combined with actuals): As above, but taking into account any required mid-period summation (here shown using the bar-chart/quarterly preset).

Marketing Spend as % of Net Revenue: This chart shows marketing spend expressed as a percentage of net revenue.

Net Revenue Post Marketing Spend: This chart shows Net Revenue after deducting marketing spend.

Forecast marketing spend is not extrapolated from spend-to-date. Rather, it is calculated by taking the forecast daily installs (at a segment level) multiplied by the Customer Lifetime Value (CLV) forecast at the specified payback date provided under Settings > New Users > Marketing.

As such, it’s not uncommon to see a jump-off point between actuals and forecast, representing the disjoint between current spend and future required spend. A large jump-off typically indicates that the target payback provided in the settings is a significant departure from that of recent cohorts. To identify the current payback, see the ROAS report for the scenario in question.

This is also why the same strong weekday seasonality signal visible in the New Users example is seen here. This does not indicate that actual marketing spend is expected to adjust on a day-to-day timeframe; instead it is an artifact of how required spend is linked to acquired users on a given day.

The forecast CPA for each scenario equates to the forecast Customer Lifetime Value as at the target payback period set in Settings > New Users > Marketing.

Similar to marketing spend, a large jump-off from actuals to forecast in this report indicates the target payback period is significantly different to the current payback. To identify the current payback, see the ROAS report for the scenario in question.

Note that if non-daily granularity is selected, the chart will show the average cost per single acqusition over that period.

Gross Revenue: Global annual gross revenue before any margins are applied.

Net Revenue: Global annual net revenue after margin is applied.

Marketing Spend: Global annual marketing spend.

Net Revenue - Marketing Spend: Global annual revenue after margins and marketing spend have been subtracted

Financial years are determined by the Financial Year End specified under Product > Settings.

Gross Revenue is revenue as supplied directly from your data feed, prior to applying any margins in the tool.

Net Revenue = Gross Revenue * Gross Margin %. In the actuals range, this gross margin % is set by going to Product > Settings. In the forecast range, it will be as per each scenario’s settings under Scenario > Settings > General.

If custom margins have been applied in your feed, or if you are sending only Net Revenue, then Gross Revenue in this report will be the same as Net Revenue (assuming your gross margin settings have been left as 100% in this case).

By default this shows all config settings, but you can choose to show only key settings by changing the option in the dropdown at the top.

Scenarios that are hidden from reports or archived will not appear here. If you need to compare two scenarios that are hidden, go to the scenario list and make them visible by clicking on the eye icon with the strike-through. Just remember to hide them again when done!