💡 This note shows some Free Fire user metrics disclosed in Sea’s filings and earnings calls. Combined with financial data in Free Fire – Preparation Revenue Estimation, one monetization metric, average adjusted revenue per daily active user, is also calculated. I fill the note with my murmur.
Since 2018, Sea has disclosed various Free Fire user metrics to help public investors gauge the traction of Free Fire. The disclosure is not very consistent as some metrics were disclosed at milestones only and some were missed (wonder why s-s analysts didn’t ask for peak 2Q19 and 3Q19 DAU numbers. Maybe they rely more on timely data from third party analytics. The reason why numbers were not disclosed in those two quarters is that Free Fire might experience drawdown in peak DAU).
The company doesn’t disclose the number of cumulative downloads which is fine for me. Number of cumulative downloads is noisy and only goes up (vanity metric) while it’s useful to gauge initial performance at launch or on-going game operation / product iteration. Since this metric is top of the funnel, if a new game flops on this metric, it would take lots of efforts to turnaround. We can find the metric used in a time-aligned comparison among games having similar genre, leveraging the same IP or even being under the same developer, publisher or IP stakeholder (basically, any cumulative metrics can be analyzed this way). For example, few days later, the first month performance of Pokemon Unite might be compared to other MOBA, other mobile games from Timi (developer), Tencent (publisher), The Pokemon Company (IP holder) or even Nintendo (IP stakeholder).
Once the game is downloaded and installed on the device, users need to open the game, register and start playing (activation). The game engages user in different ways (engagement) in addition to pure gameplay after an user is activated and before she/he is churned (retention).
Depending on the nature of the product, metrics for user base estimation are different. A marketplace might choose MAU (Monthly Active User), a productivity tool might choose WAU (Weekly Active User), and a messaging app or a mobile game might choose DAU (Daily Active User).
Sometimes user base in a shorter timeframe divided by user base in a longer timeframe such as DAU/MAU can be used a proxy for engagement. If MAU is very close to total active user base, then the ratio means that how many active user uses the product everyday.
Every company has their own way to calculate MAU/WAU/DAU but these metrics generally have average in their calculation because the timeframe of interest for the specific metric is generally longer than unit timeframe in the metric. We might be interested in a DAU on a specific day but it’s more likely we look for DAU of last month or last week. For example, the number series of active user for last seven days looks like: [25, 16 , 13, 20, 13 ,3 ,1]. DAU for last seven days is 13 (average of the number series) and the peak DAU is 25 (max of the number series). The difference between average and max of the number series is significant because the pattern in number series indicates the product might have specific behavior. The number series might come from an office productivity tool so it’s more often used on weekdays rather than weekend. One way to avoid this pattern is to use metrics with longer timeframe. The WAU for this product in this seven days might be 27 (the reason why it might be larger than peak DAU is we’re counting the total unique user during the week). There two observation from the exercise above: 1) peak DAU is generally larger than DAU; 2) the difference between two might be significant especially when there’s an event that drives product usage on specific day. Peak DAU looks better but it’s more aggressive and might be misleading. Maintaining a growing peak DAU is difficult than maintaining a growing DAU even though the overall user base is expanding and the engagement is increasing because peak DAU might be event-driven. What would you do if you find your peak DAU doesn’t grow but you know the underlying is still healthy. Probably you stop disclosing and wait for the overall user base catching up. Sea took a break for two quarters from 2Q19 to 3Q19. This is not a problem for investors as long as they can get third party data.
Sometimes, once the lifetime number of downloads/installed/registered users have reached certain level, it will become more difficult to interpret due to the nature of app user lifecycle (acquisition→activation→churn→reactivation if any→… how do you define those being churned and how do you measure them if you get them reactivated?), on-going update/iteration of software and on-going game operation. The metrics derived from these cumulative numbers get noisier and noisier as time goes by.
Free Fire User Metrics
- Free Fire reached 100M MAU and 40M peak DAU in 4Q18. It had 80M peak DAU in 1Q20. If the engagement relationship hasn’t changed a lot, Free Fire might have ~200M MAU in 1Q20 (probably not reached yet because the company might have reported this milestone if it had reached)
- The company has been reporting peak DAU since 2Q18 (from call transcript 3Q18). The reporting was suspended in 2Q19 and 3Q19 but resumed in 4Q19.
Combining Adjusted Revenue and User Metric
The company generally doesn’t disclose number of paying users for specific game.
In April 2020:
- Free Fire had record high monthly paying user which more than doubled yoy (since the peak DAU only grew 60% yoy, the conversion probably improved)
- In India, monthly paying users/monthly active user > 10%
We use peak DAU to calculate a monetization metric, Average Adjusted Revenue per Peak DAU (AARpPKDAU).
Fire Fire Adj. Rev. has been driven by expanding user base, improving conversion (paying ratio), and increasing monetization. As long as you build, grow and sustain a large user base, you always can find/try various monetization methods. While Free Fire focuses more on the monetization now, keep growing and sustaining a healthy user base is one of the keys to turning a game into a platform (key words: engagement, community, ecosystem…). Sea’s current goal for Free Fire is to make it a social platform so the company might try more monetization initiatives to bring in new revenue streams.
The monetization metric – AARpPKDAU – is not accurate but still provides a snapshot on monetization trajectory. The metric is driven various factors so a jump might not be good and a decline might not be bad. For example, a jump in Average Revenue per User (ARpU) might come from more aggressive initiatives which might sacrifice overall user experience and churn light users who might be converted into a paying users. A decline in ARpU might be due to new market penetration that expands the user base but hasn’t brought in revenue yet. If there’s a jump in user base proxy along with a decline a ARpU, I tend to interpret it more positively.