Mobile_Game_Analysis.pdf

mobile game data analysis white paper content c 2 3 7 10 13 14 acquisition activation revenue retention monthly engagement is calculated in a similar fashion. average daily usage is defined to be the average number of times a user played a game in a day. it is the number of times a game was played in a given day / the total number of users who played the game on that day. varying engagement levels over time can be used to track how updates affect player behavior, and to gauge how well users respond to activity in promotion channels. ? the total time a game was played in a given day / the number of active users on that day. the number of times that a user plays a mobile game in one day is defined to be the users total daily engagement count (dec). every time the user plays a mobile game, it is recorded as one use. how “sticky” is the game to our users? average daily engagement count which promotion channel results in the highest frequency of engagement? how frequently are users using our products? notes efinition efinitionquestions anwsered ? ? ? ? ? ? ? ? ? ? ? activation 6 dau/mau dau/mau abbreviation dau/mau how can we characterize our user engagement levels? are our games gaining in popularity, losing popularity, or remaining stable? how many consecutive days does an average user stay active? efinition in practice, dau/mau should not drop below 0.2. that is, user sign-ins should not drop below 6 days (0.2*30=6 days). notes the average session duration is the average amount of time a user plays per session in a given period of time, which is the total amount of time a user has played in a given period of time / the number of active sessions during that time. at can also be used to analyze cheating behavior, game stickiness and game performance. game stickiness can be found by tracking users average time spent on a game every week, every two weeks or every month, depending on your needs. notes what are our current player engagement levels? what are the target quality levels of our products? what is the quality of our promotion channels, and are they cheating? can we cross-analyze retention and churn rates with single-use online time? how much retention do our games have? questions anwsered questions anwsered ? ? ? ? revenue 7 ? ? ? ? ? ? ? ? ? ? ? monthly payment ratio mpr the following three methods are currently available in mobile games for generating revenue: download to pay in-game advertising in-game purchases the most important of these is in-game purchases. here, we will define an internal payment quota and describe spending and charging, both of which are related to payment. abbreviation the ratio of paying users to total active users within a given period. the period of measurement is usually one month. mpr is calculated as apa / mau. apa stands for active payment account (see below). are payment guidelines within our games acceptable? what are user payment habits and payment intention (e.g. combine ? goods, levels)? are we converting normal users to paying users at the expected rate? efinition revenue3 mpr includes first-time paying users, recurrent paying users, and previously paying users within the given measurement period. note that changes in mpr do not necessarily indicate a change in the number of paying users. also, mpr will vary depending on the type of game in question. different kinds of games have different expectation levels for mpr. notesquestions anwsered ? ? ? ? ? ? ? ? ? ? ? revenue 8 active payment account apa average revenue per user arpu abbreviation abbreviation the number of users that have made a payment within a given amount of time, typically one month. monthly apa can be calculated as mau*mpr. mau is monthly active users and mpr is monthly payment ratio. the amount of revenue generated by a single player within a given period of time, typically one month. how large is our base of paying users? what is the makeup of our apas: “whales” (big spenders), “dolphins” (mid-level accounts), or “fish” (small-timers)? how stable is our base of paying users? efinition efinition apa includes first- time paying users, recurrent paying users, and previously paying users within the given measurement period. if needed, apa can be further split into active charging accounts and active spending accounts. notes arpu is calculated by taking the total revenue from a game in a given month and dividing it by the total number of active users in that month. arpu = revenue players monthly arpu = revenue mau arpu=monthly total revenue / monthly active users questions anwsered ? ? ? ? revenue 9 ? ? ? ? ? ? ? ? ? ? ? the average revenue generated per user in a given time, usually a month. what kinds of users do we acquire from different promotion channels? w h a t a r e t h e e a r n i n g s contributions from our games? what is the relationship between active users and per-capita contribution? what is a games per-capita earnings performance? a strict definition of arpu differs slightly from the traditional definition, which is arpu=total revenue / number of paying users. arpu can be used to estimate initial earnings across different modalities. notes average revenue per paying user arppu abbreviation what are the apa payment levels? what trends are contributing to apa? what conclusions can we make by analyzing big-spending users? efinition arppu is often distorted by the influence of “whales” (big ? these factors must be accounted for in any analysis of arppu. a cross-analysis of arppu with apa and mpr can provide a perspective on retention rates and ? of paying users. this type of analysis can be helpful in assuring payment quality and scope. notes arpu is calculated by taking the total amount of revenue for a game and dividing it by the number of paying users in one month. arppu = revenue paying users monthly arppu = revenue apa arppu= total monthly game revenue / active paying accounts (apa) questions anwsered questions anwsered retention day 30 churn rate: the ratio of the number of users who logged in last month but did not log in this month to the number of active users last month; questions anwsered ? ? ? ? addendum 13 ? ? ? ? ? ? ? ? ? ? ? addendum5 clicks install register login the preceding metrics are reference measures intended to be used by developers, analysts, operations specialists and management in the analysis of game performance. they are only valid in reference to analysis of mobile games. additional finer- grained measurements of performance may also be obtained if needed. during actual analysis and according to the number of variables, secondary or cross-metrics may also be established. for instance, revenue analysis could be run against returning user contributions, long-term paying user contributions, retained paying user contributions, paying user churn rates, second-time spending analysis, or new user conversion rates. in addition, some commonly- used metrics were not described above. partial explanations of these metrics are given below: pcu peak concurrent users: the highest number of players online at one time. acu average concurrent users: the average number of users online at one time. new users conversion rate: may be grouped according to promotion channel source. k-factor=invitation rateconversion rate conversion rate: number of invitations sent / number of people invited who were converted into new users. invitation rate: number of successfully sent invitations or the number of people invited. if k1, game user population size is increasing. if k1, game user population size is decreasing. appendix 14 ? ? ? ? ? ? ? ? ? ? ? appendix6 daily game data report weekly game data report monthly game date report quarterly game data report annual game data report developers and operations managers must periodically review data analyses to stay abreast of a games current status, identify any problems that might arise and make any necessary adjustments. to this end, weve gathered together some of the most important data metrics below and explained how to use them for data analysis. the first three reports are the most widely used as they are ? due to the increasingly complex nature of data metric design, analysts often have to struggle with tens or even hundreds of metrics. however, the number of metrics we can keep track of in a day, week, or month is limited. for this reason, data metrics should follow “occams razor“, which requires that we not complicate things unnecessarily. often, more can be accomplished with just a few tools than with an entire cumbersome toolbox. thus, in writing data analyses, try to use as few metrics as possible. do not add more metrics just for the sake of having more metrics as this will eventually cost time and energy. due to limited space, this section will only use daily reports and weekly reports as models for how to interpret and analyze core data metrics over different periods of time. ? ? ? ? appendix 15 ? ? ? ? ? ? ? ? ? ? ? daily game data report monitor important data, identify abnormalities. understand promotion channel data, gain real-time understanding of promotion channel performance. daily active users dau look for a big change in user activity. for instance, was activity influenced by holidays, promotions, version upgrades, server problems, or loss of veteran users? daily new users dnu was there a bump in daily new users, perhaps as a result of promotions, server problems, or holidays? day 1 retention rate if new users return to play the next day, this is a good indication that the game met their expectations (a measure of the games “stickiness“). a sudden jump or drop in the churn rate may be ? ? daily average online time at is online time stable? large numbers of new or one-time users pouring in may result in a low, biased at. the analysis here is done over the same period, by comparing results from two consecutive days, or by using average historical levels. metrics monitored user activity objectives of the report note appendix 16 ? ? ? ? ? ? ? ? ? ? ? daily charge total sum of daily charges daily changes in charging totals resulting from charging events, ? daily charging user count total number of users charging per day daily changes in number of charging users may result from charging activities or version upgrades. if necessary, daily spending totals and daily spending user counts may also be monitored to see if user spending is in line with the particular spending items being promoted during that time, such as new items or new spending points. the analysis here is done over the same period, by comparing results from two consecutive days, or by using average historical levels. daily channel charging count monitor total charging activity for each promotion channel and play close attention to channel revenue. the amount of new charging in important channels may also be monitored, as needed. daily channel new user count a comparative analysis of the daily amount of new users brought in from each channel. do abnormalities exist - watch for biases based on version, server issues, and presence or absence of promotions. channel next-day retention rate a daily monitored metric. a good measure of the quality of users from different channels. game revenue channel promotions note note ? ? ? ? appendix 17 ? ? ? ? ? ? ? ? ? ? ? understand periodic trends, make timely alterations to strategy. understand main promotion channels, user tracking, conversion analysis, and quality control. summarize periodic data analysis, devise strategy for next period. weekly active users wau and historical average wau wau is a good metric for quickly determining current in-game activity levels, such as within a matter of weeks. the activity level of new and veteran users over the course of a week constitutes a fairly complete periodic cycle. by comparing data from similar periods (for example, last week, the same week in the previous month, the same period last year, etc.), analysts can effectively evaluate the stability of user activity. weekly new users wnu and historical average wnu wnu is the total number of new users in a given week. the ratio of wnu to wau is a good measure of the current overall activity of users (with emphasis on new users or veteran users). in addition, wnu can also be used to help operations analysts understand the positive or negative effect of promotions, natural growth, (without promotions), version upgrades, and holidays, on the games vitality from a periodic data perspective. weekly engage day wed and historical wed wed is the number of times a user has logged into a game in one week. this data metric measures user activity from week to week and can also measure when users are most active during the week (for instance, for many games friday and saturday are the busiest days). operations can use this metric to effectively select times to organize events. weekly game data report user quality objectives of the report metrics monitored ap