How Should Game Developers Build a New “Assessment Logic” Amid Ios Payment System Changes?

In 2024, Apple was mandated by regulatory bodies in several markets to lift restrictions on external payment methods on iOS, marking the initial step in the global implementation of third-party payments. Entering 2025, this shift is beginning to significantly impact the gaming industry, particularly in South Korea, one of the first pilot countries, where leading game developers have begun to launch new payment versions.

Several top-tier games, including Tencent's "PUBG MOBILE," NetEase's "Knives Out" (international version), and Smilegate's "LOST ARK Mobile," have tested the integration of third-party payment systems in the South Korean market. Although currently in a gray-scale observation phase, this is no longer a simple regional strategy test but a signal of a fundamental structural transformation within Apple's payment ecosystem.

The changes have begun, but the effects are not uniform

Whether to incorporate third-party payment into product design after the policy is implemented has become a new decision that operations and monetization teams need to jointly address at this stage.

However, the implementation effects of various products are obviously different at present. Some products quickly adjusted the first recharge price after introducing third-party payment, stimulating conversion and driving the recovery of early revenue; some projects chose to restore the original version after only trying to go live for a week - possibly due to delayed data return, retention fluctuations that did not meet expectations, or user maladaptation to the new payment path.

It can be stated that reform is not a one-size-fits-all approach. For game developers, the key challenge brought by third-party payment is not only "product experience", but also the evaluation mechanism of influence.

Changes in the payment link trigger a "window period" for data judgment

In the past, we generally had three major data supports to evaluate the success of a version:

• Monetization conversion: IAP revenue, first payment rate, ARPU, etc.
• User behavior: registration-first recharge path, retention curve
• Advertising feedback: channel attribution, LTV/ROI and other indicators

However, when the payment channel switches from Apple IAP to web payment or SDK payment system, the above links will produce multi-dimensional interruptions:

• Apple background data cannot accurately cover all conversions
• The underlying logic of attribution changes, and the evaluation of buying volume effectiveness is difficult to restore
• The credibility of the "consensus panel" of the original team cooperation decreases

More importantly, this change is not "immediately significant", but often cumulative and lagging. This puts a higher demand on decision-makers: "Can they quickly judge the trend when the data is not yet complete?"

How can small and medium-sized teams establish a "preemptive observation" mechanism?

Large companies, with their in-house analytical stacks or BI teams, can use data reconciliation and user behavior path reconstruction to progressively refine the decision-making process for new payment pathways. However, most small and medium-sized game companies may face the following practical limitations:

• Lack of a sophisticated revenue tracking system.
• Reliance on platform estimations for ad placements, making it difficult to penetrate the true source of underlying revenue.
• The difficulty of large-scale A/B testing for version deployments, where a single deployment error can be costly.

Given these constraints, establishing a "lightweight trend assessment mechanism" becomes the second-best option: rather than striving for user-level reconstruction, the goal is to quickly obtain market feedback signals to aid decision-making.

For instance, some industry professionals utilize third-party market analysis tools, such as FoxData, to construct a readily available yet insightful observation perspective by cross-analyzing public metrics like App Store rankings,user activity, and revenue estimations.

Examples include:

• Download volume trend changes within 3–7 days before and after a new version launch.
• Whether revenue fluctuations are triggered and the competitive performance within similar ranking lists.
• Revenue recovery or fluctuation cycles of competing products in similar markets.

While this approach cannot replace a comprehensive attribution analysis, it offers a lightweight, practical feedback mechanism for aspects such as CPI decline, leading revenue indicators, and the effectiveness of rankings, allowing for the early identification of issues.

From "Calculating ROI" to "Observing Trends": A Shift in Evaluation Methods

The evolution of payment structures reflects a broader change in the industry's evaluation mechanisms. The focus has shifted from promotional ROI to cyclical trend analysis and, subsequently, to leveraging attribution for monetization. These stages were once supported by robust backend systems, but now require more external data for reference.

For teams planning to integrate third-party payments, this is no longer a matter of "development and integration" but a strategic "signal warfare" scenario.

How do you assess the initial user behavior within a new payment system?

Can you establish directional insights within the first five hours of observing changes in lists or revenue curves?

Conclusion: Judgment is the Most Critical Skill at the Onset of Policy Benefits

The iOS third-party payment gateway is open, but there is no definitive answer on whether to enter, when to test the waters, or which solution to adopt. This is not merely a technology-driven upgrade but could become a "differentiation window driven by cognitive efficiency."

During this window, the ability to anticipate data shifts, dissect the underlying logic of trends, and interpret even minor changes in revenue charts as market signals will provide each operational team with more solid strategic anchors.

Experimentation doesn't necessarily require a complete system overhaul. A clear understanding of trends often only requires a simple observation metric.

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By observing the performance changes of recent app versions across various markets, you can utilize FoxData to compare the download and revenue trends of specific products, providing you with an external, objective benchmark.

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