How to identify "whales" in your game

Monetization has been a hot topic in the games industry over the past years, ever since the rise of free to play games. How to optimize monetization, how to define correct pricing buckets or how to better convert players are just a few of the widely discussed questions concerning the topic. In this article, however, we’ll be approaching monetization from a different angle. Rather than discussing how to achieve a high conversion rate, we will dig into the differences in behaviour between converted players and non-monetizers. With this we’re looking to give you some insights into these players’ profiles, based on which you should be able to identify them early in the game.

We will be working with two main categories (non-monetizers and monetizers), but 4 cohorts. For granularity purposes, we have broken down the monetizers category into 3 types: minnows (lowcore), dolphins (midcore) and whales (hardcore).

Here’s a preview of our conclusions:

• Dolphins and whales are more likely to play a single game. Once they convert and spend money, they are more likely to stay loyal to that game.
• Non-monetizers are prone to playing a lot of different games, having any number of weekly sessions from 1 to 6. In contrast, whales play less sessions per week.
• A closer look at the time between install to the first purchase reveals that whales take a longer time to convert, with a median of 10 days since install.

Find out more in the Conclusions section!

Methodology

In order to achieve as much granularity and detail as possible on the behavior patterns, we sampled 175M active users in the past 3 months, divided into 4 cohorts based on their total amount spent.

The first cohort consists of the users who hadn’t made any purchase in any game, while the other 3 are based on the amount spent distribution, and its 50th and 90th percentiles.

• Cohort 1 = Non-monetizers = 114M – 97.91%
• Cohort 2 = Minnows = 1.2M – 1.03%
• Cohort 3 = Dolphins = 1M – 0.86%
• Cohort 4 = Whales = 230K – 0.20%

Having around 2% of paying users is not surprising. As much as 25% of the non-monetizers in the sample were only seen once over the whole time period. 13% of them had only 2 sessions in the last 3 months. That adds up to the 40% of the players that never converted having only 2 sessions or less in 3 months. Most likely, all those users tried the game and churned, being a reflection of a common behavior of the population: installing an app, trying it once, and uninstalling it. 

Results

The first thing we wanted to look at in terms of profile trends among non-monetizers versus paying users, was their games playing patterns. Therefore, we set out to compare the percentage of users playing more than one game in each of the cohorts.

 The graph above shows the distribution of users playing either 2 or 3 games for the respective cohorts. It is easily spotted that the percentage of non-monetizers playing 2 or 3 games is higher than that of monetizers, even when considering all players pertaining to the 3 monetizers cohorts as falling into the same bucket.

For the numbers-loving people like ourselves, here’s how the tabular data looks like when breaking down the 4 cohorts and the percentage in which they play 1 to 5 games. These results point out that whales are usually more loyal to a single game, whereas non-monetizers play more games than paying users do. 

Analyzing the weekly number of sessions played by the four cohorts, we found that 99% of whales play up to 4 weekly sessions, and minnows/dolphins only up to 2. Non-monetizers, however, can play up to 8 sessions per week per game.

The number of weekly sessions per game for the monetizers is considerably lower than the number of sessions of non-monetizers.

An in-depth analysis of monetizers

The number of session results and the observations made above pointed us straight towards the retention of monetizers. 

What you’re looking at is a model adjusted to the key data points of the retention curves of the 3 monetizer cohorts and the non-monetizers. So your whales, though playing a lower number of sessions per week, is the cohort that retains best across time. Non monetizers, however, will retain less than the users who converted. This ties in with another interesting fact our analysis showed: hardcore monetizer (whales) take longer to convert. Take a look below:

While the time needed for minnows to make their first purchase is around 8 days, for whales it gets up to 18 days – more than double. As whales play less often, it could be that they catch up with the difficulty curve rather late; or that they like to take their time before committing to spending.

We’ve also looked into the top genre player preferences. Take a look below:

 The graphs above point out the fact that when analyzing your players, you have to take into consideration which genre your game is in.

The % of whales and the spender distribution is different depending on the genre. As can be seen on the results above, you are more likely to find a whale (and therefore have whales) on Trivia and Role Playing games, while Puzzle and Sport players tend to be mostly mid core spenders. Whether having a higher percentage of hardcore spenders results in a higher revenue depends on other metrics too, conversion being one of them. But understanding players’ profiles is definitely a good start for a successful analysis of your game’s performance.

We’ve explained in the Methodology section the reasoning behind how we divided monetizers into the 3 categories. Here’s a more in depth picture of this split and the percentage of the revenue they generate.

Minnows, represent less than 1% of the total revenue, whereas whales generated 86.6% of the revenue in our games sample. It is important to note at this point, that the revenue is derived both from IAPs and in-app advertising.

For a more precise view, we’ve broken down monetizers by platform. It doesn’t come as a surprise that Android players are in their majority minnows (60%), while iOS ones spend more money: as much as 70% of the iOS users are dolphins, with only 15% being minnows.

 More interesting from this perspective, we have found that iOS monetizers make their first purchase in considerable less time than Android monetizers do. Take a look at the chart below.

 It takes minnows on Android 9 times more days to convert than it would an iOS lowcore monetizer. The difference between whales on the two platforms is considerably lower (of less than a week), while for dolphins the difference is close to none. But overall, it takes iOS users less time to make a purchase than Android ones.

To get even deeper into the behaviour of monetizers, we decided to pull the data on the 3 cohorts and their distribution across our spectrum (minnows to whales) for two of the major markets (and very different at that): US and China.

As expected, the majority of the Chinese and US monetizers are midcore (with US having a slightly bigger percentage than China – 64% against 48%).

The interesting difference though, intervenes when it comes to whales. China has more than twice as big a percentage of whales (37.37%) than the US (14.42%). Not only that, but Chinese whales will also spend, in average, more than the US ones: $347.39 vs $283.9, with a median of $120 vs $67.24 (almost double).

Conclusions

First, let’s take a look at a breakdown of our findings, bullet-points style (we do love a good list!):

Non-monetizers:

• play more games in a given period of time;
• play more often than monetizers (at large);

Monetizers:

• tend to be loyal to one game (especially whales);
• whales are the most engaged of your monetizers, however they also take longer to convert;
• whales play less in terms of number of sessions than any of your players;
• there are more whales on iOS than on Android;
• Android users will also take longer to convert, this goes especially for minnows;
• Chinese monetizers spend more than US ones, especially the hardcore ones.

Though these results can mean different things depending on your context, here are a few of the thoughts related to the results that crossed our minds when researching this.

If your non-monetizers play more games, more often, they most probably are skillful at it. Though they may not be paying, they might be your promoters.

Our results have shown that non-monetizers are not only very engaged players, but they play more than one game at a time. Therefore, their attention is by default divided between multiple games. By these players, ads serving may not be perceived as a nuisance. Done right, you may even struck the right cord, without having to worry about decimating your player base: they will still come back to your game even if they do find another one they’ll start playing.

With whales, the story could be different. Having shown they commit to one game at a time, bombarding them with ads may not be in your best interest, as you might end up losing some of them. So, be judicious with your ads serving strategy, and know your players.

The fact that Chinese monetizers are spending more than US ones, got us thinking how far a good partnership with the right publisher that knows the market may take you.

What are your conclusions?

Read more posts from "The GameAnalytics Data Science Team" here: https://blog.gameanalytics.com/blog

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GameAnalytics - the world's biggest free analytics platform for game developers with more than 22,000+ games signed-up -  was founded by Morten E. Wulff in 2012 with $8M in funding from Jimmy Maymann (CEO of Huffington Post), Rene Rechtman (President Marker Studio), Michael Arrington (Founder @ TechCrunch), Tommy Ahlers (Founder of Podio), and Anil Hansjee (Partner @ Mojo Capital), and Sunstone Capital.

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