In-app ad segmentation 101: How to split and analyse your players

User segmentation is nothing new to the gaming industry. As app developers continue to seek any competitive edge in a complex COVID-19 world, and as segmentation tools become more advanced by the day, the need for deeper ad segmentation undoubtedly becomes all the more important in the gaming ecosystem.

When applying user segmentation to your ad strategy -- no matter how basic or complex -- the idea is essentially the same: alter a player's ad experience in the game based on previous actions, potential actions, or specific characteristics, in order to delicately balance user experience and retention with revenues from both in-app purchases and ads. Then, apply a different ad monetization strategy to each group.

Here are some ad segmentation practices that should be carried out and tested, to add value to your game and generate incremental revenue gain.

Payers vs non-payers segmentation practices

Splitting users into paying and non-paying groups is one of the most common and basic forms of segmenting players. This approach looks at users who have or haven't invested money into a game, and adjusts their engagement or potential engagement with ads. This could result in removing all ads for the paying group (both user-initiated, rewarded ads and system-initiated, non-rewarded ads), removing some ads (typically non rewarded only) and/or adjusting the frequency of ad placement or the reward amount.

Splitting users into paying and non-paying groups is one of the most common and basic forms of segmenting players

There's no one-size-fits-all solution when deciding how to treat these two distinct groups, but it's important to consider the impact of removing a valuable rewarded ad unit from a paying player, that is 100% opt-in. It could be perceived as 'punishment' to the player, and ultimately, cause them to churn.

What's more, a player who made an in-app purchase at one point may later decide to watch a rewarded video in order to push through a tough part of the game, while coming back at a later stage to make another IAP. These are the ultimate users -- the ones who find value in the different game mechanics designed to progress them through the game and they tend to retain better than IAP users alone.

With this type of segmentation, take it a step further to understand two important distinctions:

1. From when is a new player considered a non-payer (e.g. seven days after installing the game, or after reaching a certain level in the game without making a purchase).
2. How many days post-purchase does a paying player get treated as a non-payer again, if at all (e.g. after 14 days).

How these segments are defined can have a meaningful impact on your game metric evaluations like average revenue per daily active user, retention, lifetime value and even store rankings.

Additionally, consider taking users with a high probability to become payers, based on their game progression and level of engagement, as a different group. Balance this with the knowledge that players who engage with rewarded video also have a higher likelihood of making an in-app purchase, so capping the rewarded ad frequency for this group and/or delaying their ability to watch them, may not be advisable for overall game revenue and performance.

Players who are neither monetizing through IAP nor through rewarded ads, should be tackled separately. You may still engage these players with rewarded ads as a way to potentially monetize and retain them, while also showing them non-rewarded ads such as interstitials and banners.

Once this group of users is established, it's a good idea to test and further break them down by increasing or decreasing the frequency at which they see interstitials in order to find the right balance and maximize ARPU/LTV (average revenue per user/lifetime value). Too many interstitials and users will lose interest and churn. Not enough and you run the risk of gaining little to zero value from them.

Finally, keep in mind that players can and do change groups, which is a good thing, so outline how to treat players in each group to yield the best possible returns.

Advanced segmentation methods

Either done in parallel with payer and non-payer groups, or done independently, there are many other directions that segmentation can take. For example, there's a strategy of adjusting the reward amount or even the reward item based on a particular player characteristic, such as country.

A dynamic value exchange changing from geo to geo is not a new concept. It is commonly practised with IAPs, where the reward given for a particular purchase varies based on the player's geography, in order to entice more purchases while staying realistic to their purchasing power.

Just as players can be segmented per geo, players can also be rewarded based on their progression in a game

Similarly, this idea can be applied when looking at players across different geos, with regards to the value players receive in return for engaging with a rewarded video. For example, players in the US may need a larger reward amount in order to be tempted to watch a video, while players in other countries like Brazil or Russia may not need the same amount and therefore may be willing to watch more videos in exchange. Adapting the reward in these cases could be valuable.

Dynamically rewarding players shouldn't stop there. Just as players can be segmented per geo, and rewarded accordingly, players can also be rewarded based on their level or progression in a game. As players become more invested in a game and as the game mechanics naturally improve with progression, their desire or need for more virtual goods will also grow.

As more valuable items are offered for a higher price, a large majority of players will not convert, you're left with choosing between losing the player altogether or providing a different, larger reward for the player's engagement with rewarded video. This type of segmentation should be in addition to your payer versus non-payer segmentation so as not to diminish the need and value of the game's IAP.

This granular level of data allows developers to predict the likelihood of a user becoming an ad whale

Taking segmentation even further is the idea of segmenting players based on where or how they were acquired in the first place. Did the player download the application from a cross promotion campaign? Which specific campaign were they acquired from? Did the player come from another game and if so then what game genre -- hyper-casual or hardcore? All these acquisition sources have the potential to turn into different user segments and have their ad experiences be treated very differently.

Utilising ad revenue measurement

Gaming is such a rich and expansive industry that the tech that supports it is as equally intricate and deep. One of the tools offered by the industry is ARM (ad revenue measurement), which analyzes the ad revenue generated by each device for all ad units across all ad networks. Since ARM gives insights into monetization behavior, it can be used to better refine segmentation activities.

For example, you could use the data from ARM to identify groups of users, such as those who engage more with a specific type of ad unit -- rewarded video, interstitial, banner or offerwall -- and then tailor your ad strategy accordingly.

ARM tools also aggregate the total value of a player and calculate its ad lifetime value. This granular level of data allows developers to predict the likelihood of a user becoming an ad whale, which will then put that user into a new segment with a different ad strategy.

The prediction element

The value of sophisticated segmentation lies not only in the segmentation strategy chosen but also in the ability to utilize the insights to generate revenue. If there is no marginal gain from the next segmentation level that's created, then the effort is pointless.

For this to be worthwhile, you must be able to directly connect your segmentation strategy with the likelihood of a user engaging with your IAP and/or ads, otherwise known as the predictability factor of engagement. If you can identify patterns that lead to higher retention, in-app purchases and engagement with ads, then you will have achieved a positive marginal gain.