Summary that DnR=r(n). Each game will have its own values for a and b Game analytics 100: The retention Since it defines the probability that a user plays exactly n days curve after install, expected DAU by day n after game launch is dependent on the retention curve. The recurrence relationship is DAUh=(r[n]*DAU)+DAUn-1, which can be implemented as a Retaining existing users is fundamental to the success of any by Russell Ovans, East Side Games April 2024 recursive function in any programming language. that best fit their retention profile. Retention is measured as the proportion of a cohort that plays daily active users from the cohort who played on the nth day after Player duration (PD) is the number of distinct dates a new use their install date. exactly n days after their install date: DnR= DAUn DAU ...where DAU is the size of the cohort, and DAU, is a count of the daily active users from the cohort who played on the nth day after their install date.

Summary Table of contents Retaining existing users is fundamental to the success of any that DnR=r(n). Each game will have its own values for a and b mobile game since you can only monetize the players you have. Portions of this paper previously appeared in the book Game Retention is measured as the proportion of a cohort that plays Analytics: Retention and Monetization in Free-to-Play Games. Preliminary Reprinted with permission of Thought Pilots. How GameAnalytics displays Summary after install, expected DAU by day n after game launch is DnR= DAUn dependent on the retention curve. The recurrence relationship The retention DAU is DAUh=(r[n]*DAU)+DAUn-1, which can be implemented as a Retaining existing users is fundamental to the success of any Fitting a retention curve in recursive function in any programming language. Constructing a retention curve in mobile game since you can only monetize the players you have. that best fit their retention profile. Retention is measured as the proportion of a cohort that plays Tableau from (more) historical Player duration (PD) is the number of distinct dates a new use their install date. exactly n days after their install date: Predicting DAU with a retention DnR= DAUn Player duration as the summation of DAU the retention ...where DAU is the size of the cohort, and DAU, is a count of the Retention daily active users from the cohort who played on the nth day after their install date. 21 Summary

Summary Introduction Retaining existing users is fundamental to the success of any that DnR=r(n). Each game will have its own values for a and b Retaining customers is the life blood of any business, let alone a daily active users (DAU) from a constant number of daily installs; game studio. Generating new customers is an expensive and, the summation of this curve is the expected number of endeavour as it requires outlays of cash on advertising. As such, distinct days a new user will play your game. it is generally more economical to keep the customers you have after install, expected DAU by day n after game launch is than it is to buy new ones. Or, as my Uncle Bob explained to me dependent on the retention curve. The recurrence relationship years ago, “Once the customer enters your business, all of your is DAUh=(r[n]*DAU)+DAUn-1, which can be implemented as a effort shifts to selling them one thing: a return recursive function in any programming language. ...where DAU is the size of the cohort, and DAU, is a count of the that best fit their retention profile. The topic of this paper is mobile game customer retention: how Player duration (PD) is the number of distinct dates a new use do we quantify and model the rate at which users return to play our games? You can only monetize the users you have, so if your game doesn’t retain its players, you will struggle to generate DAUn revenue. Most game analysts are surely familiar with the notion DnR= DAU of day-n retention; i.e., the proportion of your players who return to play exactly n days after they install and play their first session.

u have, so if your game doesn’t retain its players, you will struggle to generate DAUn revenue. Most game analysts are surely familiar with the notion DnR= DAU of day-n retention; i.e., the proportion of your players who return to play exactly n days after they install and play their first session. For example, if 100 users install your game on July 1st, and 20 of those players return to play on July 8th, then day 7 retention (D7R) is 0.20. In this paper, we generalize the concept of day-n retention with a retention curve, a simple formula to model and predict player retention for any day after install. We describe how a retention curve is derived from a set of historical data, plus two important applications of the curve: predicting

Summary Preliminary ᵈᵉᶠⁱⁿⁱᵗⁱᵒⁿˢ Retaining existing users is fundamental to the success of any that DnR=r(n). Each game will have its own values for a and b Data analysts tend not to think too much about individual published by East Side Games, a good D1R is around 40%. For players. Instead, descriptive statistics are drawn from a group of D7R, we aim for players called a cohort. A cohort is a set of players who have Since it defines the probability that a user plays exactly n days something in common. Normally, this is their install date, but Dn retention as a KPI is measured at a standard set of days additional attributes can be used to determine membership in a since install, typically for n ∈ {1, 7, 30, 90} . But in general, DnR cohort. For example, a cohort might consist of all the Android can be measured for any day since install as players from the US who installed version 1.26.5 on July 1, 2023. DAU Cohorts define the players used in the calculation of averages DnR= n that best fit their retention profile. and other descriptive statistics that make up key performance installs Retention is measured as the proportion of a cohort that plays indicators (KPIs) such as day-n Player duration (PD) is the number of distinct dates a new use their install date. exactly n days after their install date: where installs is the size of the cohort, and DAUₙ is a count of Retention is a KPI that is modified with a “days since install” the daily active users from the cohort who played on the nᵗʰ day DAUn index, which we denote with the variable n. You never talk about DnR= after their install date.

ate: where installs is the size of the cohort, and DAUₙ is a count of Retention is a KPI that is modified with a “days since install” the daily active users from the cohort who played on the nᵗʰ day DAUn index, which we denote with the variable n. You never talk about DnR= after their install date. Note that DAU₀ ≡ retention without specifying a particular day after a cohort’s DAU install date, which is indicated by “Dn.” Dn retention (DnR) is the For a cohort to have a value for any Dn retention metric their proportion – often expressed as a percentage – of a cohort that install date must be more than n days ago. For example, we plays exactly n days after their install date: not the day before, can’t calculate D7R for a cohort of installs until eight days after nor the day after. By definition, D0R is always 1.0 since the their install date, at which point we say the cohort and its users install date is equivalent to the date of a player’s first session. are “seven days fully baked.” If a cohort is not fully baked with D1R is the proportion of installs who played at any time in the respect to a KPI, the value is calendar date immediately following their install date. The A time series is made up of successive measurements – over higher your retention, the better. For the idle-genre titles