Meta and TikTok are good at finding more users who behave like the users they are told to optimize for. The catch is that the conversion signal you give them determines what the algorithm learns to look for.
If you optimize a mobile game campaign for installs, the network has a straightforward job: find people likely to install the game. That does not necessarily mean finding people who will retain, make purchases or generate ad revenue later.
For mobile games, the most useful conversion signal often happens after the install. A player reaching level 10, returning on D7, watching rewarded ads, or making an IAP can tell you much more about their potential value than the install itself. The challenge is turning those downstream behaviors into signals that your networks’ algorithms can actually learn from.
What makes a good conversion signal?
A useful conversion signal should tell you something meaningful about user value while happening early enough to influence acquisition.
For example, a tutorial completion event is easy to measure, but it may not tell you much about long-term value. A D30 purchase might be a much stronger indicator, but waiting 30 days to generate the signal makes it less practical for campaign optimization. The key is to find a signal in between that has been identified as an early purchase signal.
A strong signal should be meaningful, measurable, frequent enough and available early enough to support optimization. It should also reflect the behaviors that matter to your particular game rather than relying on a generic definition of a valuable user.
For an ad-monetized game, rewarded ad engagement and retention might be strong signals. For an IAP-heavy RPG, progression combined with purchase behavior could be more useful. A match-3 game might find that reaching a certain level within the first few days strongly correlates with long-term retention. The right signal therefore comes from your own player data.
Why installs are often a weak optimization signal
Installs are useful because they happen quickly and at scale. They are also a very shallow indicator of value.
For example, you might have two campaigns that both generate 10,000 installs. One campaign brings in users who open the game once and disappear. The other campaign brings in users who complete the tutorial, reach level 10, and continue playing for several weeks.
If both campaigns optimize toward installs, the network has little reason to distinguish between those two groups.
The same problem applies to many early funnel events. Optimizing toward tutorial completion might produce users who are slightly more engaged, but it does not necessarily tell the algorithm which users will become your best players. Downstream signals give the network more context.
Instead of looking for people who install, look for people likely to behave like the users who generate value for this game.
How downstream events improve algorithmic learning
The basic principle is simple: the network learns from the outcomes you send back to it. Imagine a game where players who reach level 10 within three days have significantly better D30 retention and revenue. You can treat that behavior as a meaningful conversion signal rather than waiting for the final revenue outcome.
As more users generate the signal, Meta or TikTok receives more information about the characteristics associated with that outcome. The network can then use those signals to adjust delivery and look for users who are more likely to generate similar behavior.
This does not mean the algorithm suddenly knows exactly who will become a high-value player. It means you are giving it a more useful optimization target than the install alone.
How to send better conversion signals to Meta and TikTok
Start with the events you already collect inside the game. Look at progression, retention, monetization and engagement, then identify which behaviors correlate with long-term value.
- For example, you might find that your strongest early indicator is: Level 10 reached + D7 retention
- For an ad-supported game, it could instead be: Level 10 reached + 10 rewarded ad views
- Or for an IAP-driven game: Level 5 reached + first purchase
You can then turn that logic into a custom signal and send it back to the relevant network.
The important part is not creating a complicated signal. In fact, combining every available event into one giant definition can make the signal harder to interpret and less useful for optimization. Start with a behavior that has a demonstrated relationship with value and test from there.
The role of creative-level attribution
Conversion signals become significantly more useful when you can connect them back to the creative that acquired the user.
Suppose one ad generates 20% more installs than another but only 10% of its users reach your high-value milestone. Another creative generates fewer installs but twice as many high-value users. Without creative-level attribution, you might simply see the difference in install volume. With creative-level attribution, you can identify which creatives are attracting users who generate the signals you care about.
It also gives creative teams better information about what to produce next. If certain creatives, concepts or messages consistently attract users with stronger retention or monetization, that is how you can optimize your campaigns.
How Audiencelab helps connect the signals
Audiencelab provides the layer between signal engineering for in-app behavior, creative-level attribution and network optimization.
You can customize and define signals based on the behaviors that matter to your game, connect those behaviors to the creatives that acquired the users, and then send the resulting signals back to networks such as Meta and TikTok.
For example, if you identify that players who reach level 10 and retain on D7 have significantly higher long-term value, Audiencelab can use that logic to create a signal, attribute the resulting behavior back to the acquisition creative, and feed the signal into the relevant network setup. By doing so it turns behavioral data into something the algorithm can actually learn from.
It also means the process does not stop at reporting. Instead of simply discovering that one creative attracted better users, the signal can flow back into acquisition and influence future targeting and optimization.
