New Users

Retention Curve

Active Users

Calendar Date Monetisation

Customer Lifetime Value

Revenue

Retention Cohort

Short Term Retention

Long Term Retention

Date Range: all reports with a calendar date x-axis, and Retention Cohort.

Ramp Segment: all reports

Retention Lines: Choose to view the Short and Long Term Retention Trends reports on a Calendar or Cohort basis.

Retention Lines Window: number of rolling days over which to average the Short and Long Term Retention Trends reports.

Scenario: current scenario, not advised to edit (to view this same report for another scenario, change scenarios in the breadcrumb dropdown instead).

Granularity: applies to New users, Active users, Calendar date monetisation, Revenue, and the cohort granularity of the Retention Cohort table.

New Users: A summary of new users, rolled up to a global level, by calendar date.

New Users by Segments: As per the first chart, split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

New Users by Category: As per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Channel” has been selected. The chart will then summarise results for each unique channel value, in this case Organic and Paid.

If non-daily granularity is chosen the displayed figure will be the sum over the period.

To stop filtering by Category, type ‘None’ in the category filter field.

By default this report shows only the retention of cohorts within the Actuals range. To include the forecast, change the “Include Forecast” filter to Yes.

To view the forecast for existing cohorts only, change “Include Forecast” to Yes and “Include New Cohorts” to No.

Retention Curve: Displays the retention curve on day 1 of the forecast, rolled up to a global level.

Retention Curve by Segment: as per the first chart, split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Retention Curve by Category: as per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Channel” has been selected. The chart will then summarise results for each unique channel value, in this case Organic and Paid.

To stop filtering by Category, type ‘None’ in the category filter field.

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. Where Automatic retention lookback has been selected, different segments will 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.

Use the “Date Metric” filter to switch between Calendar and Cohort date basis. The “Date Range” filter will apply to whichever metric you’ve selected.

Use the “Window size” filter to apply a rolling average over the selected number of days, to smooth out daily variances and make trends easier to spot.

To view the retention trends of historical actuals only, change the “Include Retention Forecast” filter to No.

Active Users: A summary of Daily Active Users (DAU), rolled up to a global level, by calendar date.

Active Users by Segments: As per the first chart, split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Active Users by Category: As per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Channel” has been selected. The chart will then summarise results for each unique channel value, in this case Organic and Paid.

If non-daily granularity is chosen the displayed figure will be the average daily active users over the period e.g. monthly granularity will show average DAU per month, not Monthly Active Users (MAU).

To stop filtering by Category, type ‘None’ in the category filter field.

Calendar Date - the period included in the x-axis of the report

Cohort Date - the cohorts included in the contribution (the chart series)

We recommend viewing this report for a filtered period, and with a cohort granularity of Monthly, as the report can only display the contribution from a maximum of 50 cohorts.

Calendar Date Monetisation: A summary of average daily revenue per user (ARPDAU), rolled up to a global level, by calendar date.

Calendar Date Monetisation by Segments: As per the first chart, split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Calendar Date Monetisation by Category: As per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Channel” has been selected. The chart will then summarise results for each unique channel value, in this case Organic and Paid.

If a non-daily granularity is selected, the report 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.

To stop filtering by Category, type ‘None’ in the category filter field.

Monetisation per Cohort: A summary of how ARPDAU evolves by days since install, for cohorts over time.

Monetisation per Dx: A pivot of the above, summarising how ARPDAU evolves for cohorts, by days since install.

This chart is best used when interrogating specific time periods, cohorts or Dx points; make use of the filters provided to zoom in on the timeframe in question.

As the Y-axis of the second chart states, this chart includes ARPDAU only for cohorts that exceed the Minimum DAU Contribution size as set in Settings > Monetisation. This ensures any outlier effects illustrated by the report are true signals, and not an artefact of partial cohorts or dying segments.

Cumulative CLV: A summary of how cumulative CLV evolves over days since install, rolled up to a global level, by monthly cohort. This chart will show the first 50 monthly cohorts by default. You can adjust the cohorts shown by entering a cohort date range, or adjusting granularity, but a maximum of 50 can be shown at one time.

Customer Lifetime Value by Segments: as per the first chart, but split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Customer Lifetime Value by Category: as per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Channel” has been selected. The chart will then summarise results for each unique channel value, in this case Organic and Paid.

By default the report does not include forecast CLV, so on the top chart, you’ll see that younger cohorts have shorter CLV lines (as they have fewer days since install). You can include forecast values by setting the “Include Forecast*”* filter to Yes.

To stop filtering by Category, type ‘None’ in the category filter field.

To reduce the impact of volatile cohorts, this report excludes cohorts that are smaller than the minimum cohort contribution size, as specified in Settings > Retention.

Customer Lifetime Value: A summary of the cumulative CLV at a specified Dx point (the “CLV Window*”,* default 365), by cohort date.

Customer Lifetime Value by Segments: as per the first chart, but for all cohorts split by segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Customer Lifetime Value by Category: as per the first chart, but for all cohorts split by category. This chart will only show results once a category is selected from the filters. In the example below, the category “Channel” has been selected. The chart will then summarise results for each unique channel value, in this case Organic and Paid.

To stop filtering by Category, type ‘None’ in the category filter field.

To reduce the impact of volatile cohorts, this report only includes cohorts that are greater than the minimum cohort contribution size, as specified in Settings > Retention. This can be seen most clearly in the By Segments chart above, where some diminishing segments show no CLV after a certain cohort date.

Revenue: A summary of revenue before margin or marketing spend is subtracted, rolled up to a global level, by calendar date.

Revenue by Segments: as per the first chart, split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Revenue by Category: as per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Channel” has been selected. The chart will then summarise results for each unique channel value, in this case Organic and Paid.

Note that if non-daily granularity is chosen the displayed figure will be the sum over the period.

To stop filtering by Category, type ‘None’ in the category filter field.

If you’re only sending us your net revenue in the source data, this report will show net revenue, not gross.

Cost Per Acquisition: The average cost per individual user acquired, rolled up at a global level.

Cost Per Acquisition by Segments: as per the first chart, split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Cost per Acquisition by Category: as per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Platform” has been selected.

The forecast CPA 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 may need adjusting.

Where a blended payback has been set, PAID segments will seem to show a dip from actuals to forecast, while ORGANIC segments will show an uplift from zero actuals. Together these will result in the blended CPA when rolled up.

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

To stop filtering by Category, type ‘None’ in the category filter field.

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 by Segments: as per the first chart, split by individual segment. A maximum of 50 segments may be displayed on this chart; by default the first 50 are shown. If you have more than 50 segments, it’s advised to use this chart only when comparing specific, filtered segments.

Marketing Spend by Category: as per the first chart, split by predefined segment category. This chart will only show results once a category is selected from the filters. In the example below, the category “Platform” has been selected. The chart will then summarise results for each unique channel value, in this case Android and IOS.

Marketing Spend as % of Net Revenue: Marketing Spend as a percentage of (Gross Revenue * Gross Margin %). The downward spikes in this example are due to regular events spiking up the revenue in the denominator.

Net Revenue Post Marketing Spend: (Gross Revenue * Gross Margin %) - Marketing Spend.

Note that if non-daily granularity is chosen the displayed figure will be the sum over the period.

To stop filtering by Category, type ‘None’ in the category filter field.

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, you may 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.

Return on Ad Spend (ROAS): A summary of the percentage of marketing spend paid back by a cohort at a specific Dx point post install.

Payback: The estimated period of time that it takes a cohort to reach 100% payback with respect to marketing spend.

Cohorts or segments that do not show payback within the forecast range will have their payback displayed as “38.7+ months” for example. The figure used is the time to forecast end, indicating that the payback is sometime after the end of the forecast.

The Payback Period by Segment table defaults to including cohorts within the last 90 days. This can be adjusted using the By Segment Last X Days filter.

The by-segment breakdown will include only segments with actual Marketing Spend, while the by-cohort and key metric sections will represent a blended payback across all segments.