Engagement

Engagement provides information on how many players are playing and returning to the game. Driving these numbers up is vital in having a successful and profitable game.

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 The Daily Active Users gives the total number of unique users playing the game in the last day. This is the best measure of which players are actively and currently engaged with the game. There may be a small number of returning players who are active but not played the game in the last day but this is one of the best measures of all active players.

The Weekly Active Users gives the total number of unique users playing the game in the last seven days. Some of these players may not return to the game so the WAU does not give an accurate measure of currently engaged players but it does allow the measurement of increases and decreases in the player base.

The Monthly Active Users gives those players who have been active in the game in the last thirty days. Many of these players may not be currently active and this should be borne in mind when presenting this figure.

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This chart shows the percentage of players that are still playing after one, seven, fourteen and thirty days. The retention is calculated by looking at players that played on a day and then exactly one, seven, fourteen and thirty days later.

A steady retention rate like the chart above gives a good indication of a game that is retaining users. The closer the 30 day retention is to the 1 day retention, the better your overall retention rate is in the game.

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 Stickiness is the ratio of DAU: MAU and shows if players are returning and engaging with the game. If the stickiness is low then there are higher numbers of new players active in the game whereas if the stickiness is high then there are higher proportions of returning players currently active. As games mature the stickiness measure will change. It will be low at the launch when there are many new players and lower once the game is mature. By addressing retention issues it is possible to maintain stickiness. A low stickiness points to potentially wasted acquisition budget where new players are leaving the game relatively quickly.

Sessions

The number of sessions played is a very strong indicator of engagement. Understanding how the user base of the game is split by the number of sessions each player has completed will give a very strong understanding of how many committed players are in the user base and how that split is evolving over time.

Sessions can be seen as an indication of retention and engagement which makes these charts very powerful to understand the different segments of engaged players.

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This chart shows the unique players on each day split by the number of sessions they have played. The number of sessions is banded into groups and represents a range of sessions.

The example above shows the large group of players that have between 100-500 sessions, these are the highly engaged players with the next largest group only having 21-50 sessions.

Each of these player groups represents part of a different segment of the playing base. As each group changes over time you can see how the playing base is maturing from a fairly new set of users who mostly had played less than 100 sessions to a highly engaged set of users that have crossed the 100 session barrier.

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Payers by Session Played are the number of daily unique paying players split by the number of sessions they have played.

The majority of the payers are those players that have played for over 100 sessions as you would expect as these are the very engaged players.  The next largest group of spending players is the 51-100 session players although this group is decreasing over time as the highly engaged 500+ session group start to become increasingly active spenders.

The game is becoming less successful at converting early spenders in favour of late spenders over the period of this chart.

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Payments by Session Played are the number of unique payments split by the number of sessions players have played.  This chart shows a similar structure to payers, these points to there being a one to one relationship between payers and payments. If these two charts diverge it points to one group generating a much larger number of payments. This is often seen with early payers generating lots of small payments.

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Revenue by Sessions Played is the total revenue split by the number of sessions players have played. The revenue shows very strongly the impact of the late payers generating much larger payments and therefore driving the majority of the revenue.

Total Revenue by Sessions Played

Total Revenue by Sessions Played is the total revenue split by the number of sessions players have played. This shows how much of the revenue is being generated by three players groups. The group of players that spend 100-500 sessions is absolutely key to delivering the revenue in this example.

All Player Retention Matrix

The All Player Retention Matrix tracks how often players are coming back and playing. It includes players based on the filters at the top of the page and counts relative to the first Day in the matrix. Therefore it may include new players, returning players or a mixture of both, depending on how you filter the matrix. It will show you how many players have played for a certain number of days across the game’s lifespan. These days do not need to be in a row, it could be across a large period (or the whole lifespan of the game).

All Player Retention Matrix

New Player Retention Matrix

The New Player Retention Chart shows how long players keep playing after first installing. So the 1, 7, 14, 30 day chart will track those days from when a player first installs.
New Player Retention Chart
The New Player Retention Matrix shows the number and percentage of players who installed on a specific day have played on the subsequent days. This is a more traditional view of visualizing new player retention. N.B. You can toggle between player counts and percentages by clicking the “Toggle Percentages” button.
New Player Retention Matrix