# Free To Play Metrics (F2P)

Omniata provides a starter set of predefined Dashboards containing the most common metrics relevant to Free-to-Play games. The dashboard includes charts for game Engagement, Acquisition, Monetization, Retention and more advanced features as Lifetime Value, Churn metrics and game Funnels. The F2P Dashboards are available with any new Omniata application, you can access the dashboards from the Analyze menu.

The Hourly dashboard shows key metrics for the current processing day for New users, DAUs, ARPU, etc. Other metrics require nightly processing and are available at the beginning of the next active day. For more information on hourly and nightly processing check the Night Processing ETL guide.

The F2P Dashboards share many cohort division across charts. By default the dashboards will show the Daily cohort, you can select other cohorts by expanding the “Daily” tab from the top-left drop down menu. Amongst others, cohorts such as Weekly, Monthly, Platform, Country, Platform and Version are available. If user attribution information is available, it is also possible to analyze metrics per Publisher and Campaign

On this guide:

## Hourly

Metrics on this dashboard are available on the same day of activity and are updated every hour. The dashboard is designed to follow hourly behavior and monitor, for example, campaigns in UTC time zone. The X-axis of the charts represent the hour in military time which goes from 0:00:00 to 0:59:59. Typically this charts show a comparison between yesterday’s observations and the completed part of current day.

### Key Metrics

The following are some of the most important metrics on this dashboard:

Name Description
Active Users Count of unique users with at least one (non-silent) event sent on that time slot.
Cum. Active Users Today v/s Yesterday Comparison of Cumulative Active Users between the current day and yesterday.
New Users Count of unique users active for the first time counted only once per time-slot.
Cum. New Users Today v/s Yesterday Comparison of Cumulative New Users between the current day and yesterday.
Cum. Revenue Today v/s Yesterday Comparison of hourly revenue converted to USD.
Cum. Payers Today v/s Yesterday Count of unique Payers on each time slot between the current day and yesterday.
Cum. Daily ARPU Today v/s Yesterday Comparison of Average Revenue per User.
Cum. Daily ARPPU Today v/s Yesterday Comparison of Average Revenue per Paying User.

The second cohort available on Hourly dashboard shows the same metrics split by country.

## Acquisition

The Acquisition dashboard will provide you with an holistic view of game installations. The filters available on the right panel will help you to select a specific time frame or user cohort. It is specially useful on this dashboard the ability to label key dates in the charts like new releases or campaign launch dates.

You can also get 7-day and 30-day rolling aggregates selecting from drop down menu the Weekly/Monthly cohort dashboards.

### Key Metrics

The following are some of the most important metrics on this dashboard:

Name Description
Daily New Users Count of unique users active for the first time, counted only once per day. User is new if creation date is equal to the activity date.
Weekly New User 7-day rolling cumulative sum of Daily New Users.
Monthly New Users 30-day rolling cumulative sum of Daily New Users.

## Engagement

The Engagement dashboard provides information about Daily / Weekly / Monthly Active Users, otherwise known as DAU, WAU and MAU. Here you can also find a split of New Users vs Returning Users.

### Key Metrics

Name Description
Daily Active Users (DAU) Count of unique users with at least one (non-silent) event sent on that day in UTC timezone.
Weekly Active User (WAU) 7-day cumulative sum of Daily Active Users.
Monthly Active Users (MAU) 30-day cumulative sum of Daily Active Users.
Total Time per User Total seconds played, divided by the number of daily active users.
Sessions per User Total of sessions divided by the number of active users.
Session Length Total Time per User divided by Sessions per User.

To know how session number and session time calculated you can check the How Session Data is Calculated guide.

## Monetization

The Monetization dashboard main task is to measure the revenue of the app and the number of payers. Revenue is converted to USD (\$) using a currency exchange rate table that is fetched/updated on a daily basis. See more information about how the conversion works in the How Revenue is Calculated guide.

The exchange rate table is accessible from Global Settings -> Lookup tables -> Currency Exchange Rates by Day. The table is internally referenced as currency_exchange_rates_date.

This dashboard also tracks game payers. Metrics distinguishes between two type of payers: Payers and All Time Payers. Once a user becomes an All Time Payer he will not change the status in the future. However, a user will be classified as Payer, only for the dates that he actually spent money. A user will be New Payer at most, one day in his lifetime.

### Key Metrics

Name Description
Daily Revenue Total gross revenue converted to USD.
ARPU Daily Revenue divided by DAU in UTC timezone.
ARPPU Daily Revenue divided by Payers in UTC timezone.
Payers Users that made a real world purchase during the day.
All Time Payers Users that spent at least once in their lifetime, including the current day.
New Payers User that spent for the first time on this activity date.
Conversion Rate Payers divided by Active Users.
Payer Conversion Rate All Time Payers divided by Payers.
User Spender Tier Cumulative USD spent. A user can belong to different tiers during its lifetime. Tiers are:
[0.00]
[0.01 .. 0.99]
[1.00 .. 4.99]
[10.00 .. 49.99]
[100.00 .. 499.99]
[500+]

## Retention

The Retention dashboard calculates retention by Creation Date, not by activity date. Similar to other dashboards, the most common metrics for following retention are shown.

Users have Retention of 0-days when created, and Retention D1 if the are active the day after their creation. Naturally, for every creation date a Retention D0 of 100% is expected. If a user is not active the day after creation, but is active two days later, this user will have retention D0 and D2 but not D1.

This is the reason for which yesterday’s data does not include day 1 (or higher) retention. For D1 retention, for example, only users created two days ago are evaluated; for D7 retention only users created 8 days ago are evaluated.

### Key Metrics

Name Description
Retention D$x$ Distinct Active Users $x$ days after their acquisition on date $D$ divided by the New Users acquired on date $D$.

The Lifetime Value dashboard provides a user-centric monetization view. It uses cumulative and projected revenue to understand the expected revenue per acquired user. The Cumulative ARPU per creation date can show the if the game experienced changes regarding the amount of money spent by retained days.

Omniata calculates this for any user active on the last 180 days. It aggregates revenue by creation date and days of retention. The resulting multi-line chart shows if there is any change in user spending trend.

Omniata’s Monetization LTV allows the daily projection of user revenues up to 360 days. At the same time, it allows the selection of cohorts such as Country, Project, Acquisition Date and Publisher (from the acquisition source information) by default.

The main cohort for the calculation of LTV is the acquisition date. This can lead to some confusion when validating the data against historical ARPU values. The LTV is calculated taking first into account all users acquired, not all users active, from (acquisition) date X to date Y and their respective revenue.

### Key Metrics

Name Description
Cum. ARPU D$x$ Cumulative sum of revenue from acquisition date to day $x$ divided by Active Users from acquisition date to day $x$.
ARPU D$x$ (Projected) Projected average revenue per user, computed by log fitting 7 data points: historical ARPU at 1, 2, 3, 7, 14, 21 and 28 days.

## Churn

Omniata stores daily activity for users in its raw and daily aggregates format. For cases where a non-daily view is needed, Omniata builds daily a snapshot of the state of all Users (whether they have been active or not) employing a sliding window of 180 days. This allows for reports based on “last known active day” for any User that was active for the last half-year.

The chart “Lapsed for more than 14 Days” shows for every activity date, how many users did not shown on the last 14 Days surfacing the number of potential churners. Some users computed as “Churner” in the past can show again in the future, this is to be expected and the chart will reflect this behavior.

A more deep Churn analysis can be found under the tab “Active Days Tier”. This dashboard shows Users divided in activity tiers by days churned, where churned is defined as inactive for over 14 days. This definition can be modified to extend or reduce the amount of days needed for a User to be considered inactive.

### Key Metrics

Name Description
Churned User Also referenced as Lapsed User, is defined as any User that has not been active in the last 14 days calendar. Activity means the user has sent an event during the day.
Active Days Tier Logical grouping of Users based on activity in ranges of days. For example, the tier “7-13 days” means a user has been active in the past between 7 and 13 individual days.

## Funnel

Funnels are a powerful visualization option that helps understand the flow of interesting in-game actions defined here as milestones. By default, Omniata uses daily aggregations to track the first occurrence of all event types per user. Typically, standard event types with the exception of revenue are present for all users.

The Funnel chart shows in decreasing order the presence of milestones in the user base. Another interesting chart is Time to Milestone, where it shows for each milestone how much time a user takes to reach it in average.

It is possible to modify the definition of milestones to any event-based criteria, for example level achievement with a specific condition. The Milestone field is based on a SQL condition, to edit it go to Model -> Formulas -> Milestone.

### Key Metrics

Name Description
Milestone User-defined criteria based on events of what constitutes the funnel steps.
Avg. Time to Milestone Average time in seconds users took to reach the milestone since creation date.
Previous Milestone Previously reached Milestone per Milestone. The metric is in % of users. Shows which milestone a user reached before the current milestone.

## Campaigns

When Omniata generates enrichment tables, Campaign membership or also known as A/B Test membership information is included in it. Membership information includes the name of the experience group and dates a user was successfully added to a campaign, that being display or push notifications campaign type. To know how Campaign membership works check out the Campaign and AB Test guide.

This dashboard has a collection of charts to analyze Campaign results. The aggregations are done on a daily basis to produce charts related to user participation, retention, monetization and game session. The main dimension for all charts in the Campaigns dashboard is the Campaign membership, this means that users are not unique per day but per campaign. This can lead to confusion when comparing, for example, daily active users from Engagement with Campaigns.

The Daily Performance dashboard summarizes the main key metrics for any running Campaign. Normally you should select a single Campaign name from the dashboard filters, nevertheless more can be selected for compare-and-contrast evaluation. Each individual performance metric is statistically evaluated against the control group defined for the campaign.

### Key Metrics

For a full description on how to interpret results of A/B testing you can read the AB Test guide.

Name Description
Total Time per User by Experience Total time in seconds per user and experience. Check Sessions to know how session number and session time are calculated.
Days Active Last 7 Days per User by Experience Counts how many days a user was active between 7 -Days ago and yesterday. The value is computed per user and then averaged per Experience.
Confidence A confidence interval (or p-value) is a percentage that indicates how reliable a sample measurement is. In an A/B test, it indicates how reliable is a specific test group. Confidence intervals over 95% can be seen as statistically very reliable.

Distribution data for the campaign such as Quartiles are calculated using two approaches, assuming normal distribution (“Normal Dist.”) and without assuming normality (“Real Dist.”). Below is a description on how these are computed, where $x$ is the sample. See Quantiles in BigQuery for more information on the computation method for quantiles.

Name Normal Distribution Real Distribution
Left Whisker $\mu(x)-1.96\frac{\sigma(x)}{\sqrt{x}}$ 3rth N-tile using 101 buckets from QUANTILES function.
Low Quartile $\mu(x)-0.6745\frac{\sigma(x)}{\sqrt{x}}$ 26th N-tile out of 101 buckets from QUANTILES function.
Median $\mu(x)$ 51th N-tile out of 101 buckets from QUANTILES function.
High Quartile $\mu(x)+0.6745\frac{\sigma(x)}{\sqrt{x}}$ 76th N-tile out of 101 buckets from QUANTILES function.
Right Whisker $\mu(x)+1.96\frac{\sigma(x)}{\sqrt{x}}$ 98th N-tile out of 101 buckets from QUANTILES function.

## Tables Included in this Package

The Free-to-Play package includes a set of materialized views optimized as a starting point for analytics. The tables are structured as shown below:

Materialized View Dashboard Description
om_events NA Also known as enrichment table it contains the raw data for all events tracked in the game. Each parameter, user property and formula is present as a column; each event is present as a row. The table is partitioned by UTC date.
om_daily_user_state NA First level aggregation. It contains one row per user per date, date partitions are physically represented as individual tables. A set of predefined common user properties are available.
om_daily user_state_mvt Campaigns First level aggregation. It contains one row per campaign per user per date, date partitions are physically represented as individual tables. A set of predefined common user properties are available.
om_daily_user_milestones NA First level aggregation. It contains a view of defined milestones per user on a timestamp priority.
om_daily_user_activity Acquisition, Engagement, Monetization Second level aggregation. Uses the om_daily_user_state to build a by day view of predefined activity-based properties.
om_daily_user_activity_mvt Campaigns Second level aggregation. Uses the om_daily_user_state_mvt to build a by day view of predefined campaign-based properties. A user can show in more than one campaign during the same day.
om_weekly_user_state NA Second level aggregation. Uses the om_daily_user_state with a 7 day rolling window to build a weekly list of active users. It contains one user per row per day with all active users in the past 7 days.
om_weekly_activity Acquisition, Engagement, Monetization Third level aggregation. Uses om_weekly_user_state to build a by-day view of the last 7 days of activity metrics.
om_monthly_user_state NA Second level aggregation. Uses the om_daily_user_state with a 30 day rolling window to build a monthly list of active users. It contains one user per row per day with all active users in the past 30 days.
om_monthly_activity Acquisition, Engagement, Monetization Third level aggregation. Uses om_monthly_user_state to build a by-day view of the last 30 days of activity metrics.
om_lifetime_user_state NA Second level aggregation. Uses the om_daily_user_state with a 180 day rolling window to build a “lifetime” list of active users. It contains one user per row with all active users in the past 180 days. The window is configurable.
om_lifetime_user_state_mvt Campaigns Second level aggregation. Uses the om_daily_user_state_mvt with a 180 day rolling window to build a “lifetime” list of active users per campaign. It contains one user per row with all active users in the past 180 days of campaign participation. The window is configurable.
om_lifetime_user_milestones NA Second level aggregation. Uses om_daily_user_milestones. It contains a view of the last 180 days of defined milestones per user on a timestamp priority.
om_lifetime_activity Retention, Lifetime Value, Churn Third level aggregation. Uses om_lifetime_user_state to build a view of the last 180 days of activity metrics.
om_lifetime_activity_mvt Campaigns Third level aggregation. Uses om_lifetime_user_state_mvt to build a view of the last 180 days of campaign metrics. A user can show in more than one campaign during the same day.
om_funnel Funnel Third level aggregation. Uses om_lifetime_user_milestones. Contains the milestone data needed to build the user-defined Funnels. Milestones are customizable.

This article was last updated on April 5, 2017 12:55. If you didn't find your answer here, search for another article or contact our support to get in touch.