The Growth Loop Explained
4 min read
By
Justin Stolzenberg
Co-founder
Feb 19, 2025
Unlocking the Power of Attribution Data: Personalizing Early-Game Experiences for Growth
In the incredibly competitive mobile games world, User Acquisition (UA) attribution data is an invaluable tool that many developers either underestimate or under-utilise. It holds the potential to bridge the gap between what you know about players as they enter your game and what they expect from their early experience. But are you leveraging this data to its fullest?
At Metica, we see firsthand how install source data from attribution or deeplinks - when used dynamically - can transform early-game experiences, improve monetization, and drive scalability.
Before diving in: yes, attribution information isn’t available for every user, but depending on your channel mix we see as high as 80% direct attribution rate - so it’s worth considering strategies around it!
This article first looks at three powerful ways to utilize attribution information, and then discusses how to pair that with user behavioral data to truly personalize the experience.
1. Tailored Initial Offers: Boost Conversions Right Out of the Gate
When a player downloads your game, they bring a wealth of context with them. Their UA attribution data tells you about the campaign they came from, the creative they clicked, and the audience profile they fit into. This information can be a game-changer for crafting your initial in-app purchase (IAP) offers.
For example:
Players from campaigns optimized for high-value spenders can have a 10x higher conversion likelihood than your organic user baseline.
By tailoring their initial offers to reflect this profile—think premium bundles or exclusive discounts—you can see up to a 50% uplift in early-game revenue.
A real-life success story: In one case, a game targeting high-value IAP users through a themed campaign found significant results by introducing high-stakes, premium bundles early in the player journey. This approach dramatically improved conversion rates without alienating less frequent spenders.
2. Dynamic Ad Placements: Monetize Without Sacrificing Retention
Getting the timing of ads right can be a balancing act. Show ads too early, and you risk losing player engagement and retention. Show them too late, and you miss out on valuable monetization opportunities for non-spending players.
Here’s how UA attribution data helps you strike the right balance:
Players from IAP-focused campaigns might benefit from a delay in ads, prioritizing their onboarding experience and increasing their likelihood to spend.
Conversely, players who are less likely to make purchases (based on campaign data) can be introduced to rewarded ads when their engagement is high.
This tailored approach ensures a better balance between player experience and monetization, paving the way for sustainable growth.
3. Optimized Game Difficulty: Deliver the Right Challenge
Not all players are the same—some are looking for a relaxing escape, while others want a competitive, challenging experience. UA attribution data, combined with campaign insights, offers a powerful way to tailor early-game difficulty to match player expectations.
For instance:
A player entering your game through a campaign featuring high-stakes action might appreciate more intense challenges upfront.
Another player, drawn in by a relaxing creative, might prefer additional lives or a simpler tutorial to ease into the game.
Adjusting early-game difficulty improves retention and ensures a more enjoyable, personalized experience for your audience.
Why Static Rules and A/B Testing Don’t Keep Up
While these examples might seem obvious, the static rules and A/B testing often used to implement them often fall short in a dynamic UA landscape.
Traffic dynamics change constantly. The players you acquire during a soft launch can be very different from those you attract at scale. Without revisiting assumptions, you risk creating mismatched experiences for your audience.
For example:
During a soft launch, one game increased difficulty to improve retention, only to find that this same adjustment a year later harmed engagement. The player base had shifted, and the same strategy no longer applied.
This is where AI-driven solutions like contextual multi-armed bandits excel. These systems continuously learn and adapt in real time, delivering personalized experiences tailored to evolving traffic dynamics. We have written a lot on what contextual multi-armed bandits are and how to implement them here and here.
Building a Holistic Approach
While UA attribution data is an incredible starting point, it’s not the whole story. Pair it with:
Behavioral Data: Early signals like session length or tutorial completion provide crucial context that refines UA source data.
Device & Country Insights: Players in different regions or on different devices often have unique behaviors and preferences.
When used together, these data points create a clearer, more actionable picture of your players and their needs.
For Developers Getting Started
For developers ready to make the leap, here’s how to begin:
Access Attribution Data: Tools like Adjust or Appsflyer are great starting points for collecting UA source data.
Combine Signals: Integrate UA data with early in-game behaviors to refine decisions.
Leverage AI: Platforms like Metica simplify the complexity of dynamically tailoring player experiences, helping you scale beyond what static systems can achieve.
Avoid the DIY trap: I admit my bias, but I don’t believe developers should build this kind of platform themselves anymore. While it’s relatively straightforward to create a solution that handles early use cases—like tailoring initial offer journeys based on UA source—teams will inevitably want to go further. As the game gains traction and scales, optimizing more deeply across the player journey becomes essential. At that point, homegrown solutions often become unwieldy, introducing significant process inefficiencies and technical debt.
This is what Metica is made for - handling these complexities effectively.
If you were to develop a personalization platform from scratch, it would require several major components:
A real-time event tracking and ingestion system.
A mechanism to manage or automatically generate treatment variants.
A robust system to learn and deliver the right treatments to individual users.
For most teams, investing in an established platform is a far more scalable and efficient approach.
Future-Proofing with AI
With the advent of privacy regulations like Apple’s ATT and Google’s Privacy Sandbox, relying solely on UA attribution data is risky. The future lies in combining what you know about a player’s source with real-time behavioral data and leveraging AI-driven personalization to adapt dynamically.
At Metica, we’re passionate about helping developers unlock the full potential of their data to create meaningful player experiences and scalable growth.
Ready to take your game to the next level? Let’s talk.
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