NetMarvel Advertiser Strategy: How to Avoid User Acquisition Pitfalls and Improve Marketing ROI

In the highly competitive digital marketing space, one of the main challenges for mobile advertisers is effectively acquiring users during the scaling process. Most advertisers' user acquisition (UA) strategies fail when scaled, often due to falling into several marketing traps. This article will delve into 8 common UA pitfalls and provide practical solutions to help advertisers optimize their strategies and improve user acquisition efficiency.

1. Misattributing Organic Traffic to Paid Channels

Many marketing teams often make the mistake of attributing organic traffic (such as app store recommendations and brand-driven installs) to paid channels when analyzing data performance. This leads to inflated metrics, which will gradually collapse as scaling occurs. In reality, paid channels may not have brought these users, yet high CPIs are being paid for them.

Solution: Use regression analysis to separate organic traffic (like app store recommendations, brand effects) from the actual incremental organic traffic generated by paid channels. Stop attributing traffic that would have occurred regardless to paid channels.

2. ROAS Curve ≠ Universal Law

A common mistake among many teams is assuming that ROAS (Return on Ad Spend) curves are consistent across regions, platforms, and ad types. This "universal law" assumption often results in significant costs, especially when data is scarce, particularly when expanding into new markets or channels. For example, a team with SDK network data might seriously overestimate the return on a rewards network, as the ROAS for rewards networks peaks early and then plateaus. Seasonal factors like Black Friday can also cause fluctuations that undermine confidence.

Accurate predictions require stable traffic data, clear confidence intervals, and a humble approach to operations beyond the training set. Otherwise, it's merely gambling.

3. Mismatch Between Gross ROAS and Net ROAS

In performance marketing, data alignment is often overlooked. User acquisition teams usually focus on gross revenue (Gross Revenue), while financial decisions are made based on net revenue (Net Revenue), leading to misaligned goals. For example, setting a 12% ROAS goal on day seven, the difference between gross and net revenue may result in actual returns far lower than expected. In-app purchases (IAP) are typically calculated based on gross revenue, while in-app advertising (IAA) is calculated based on net revenue, with app store fees and VAT reducing returns by 30%-40%.

Solution: Standardize all data to net revenue, or build a data layer to track true net ROAS. Failure to address this issue may result in resources being wasted and decisions being made based on misleading data.

4. CPI and LTV Fluctuations Due to High Spending

Higher spending not only leads to higher CPI (Cost per Install), but it also unpredictably changes the relationship between CPI and LTV (Lifetime Value). When your ad campaigns start expanding to audiences outside of the core target, CPI increases are evident. However, LTV may also change: higher CPI may attract higher-quality users, but it could also bring in users outside the core audience, whose retention rates are usually lower. This makes optimization difficult because while controlling spending and profits, user quality becomes an unpredictable factor driven by market changes.

Solution: Optimize the relationship between spend, profit, and acquisition costs to find an appropriate balance. Experts have pointed out that the elasticity differences across different apps, markets, and campaign types are significant, and it's impossible to control all variables at once.

5. Overlooking Personalized UA Strategies

Many teams set a uniform ROAS target for all campaigns on day seven but ignore the fact that user behavior and conversion curves differ across channels. This one-size-fits-all approach is difficult to scale because every developer is competing for the same goal—potential paying users.

Solution: To acquire overlooked traffic, UA and product teams need to clarify the ideal user journey and convert these insights into customized MMP events. If users who log in on day 3 become the best target group, a specific event should be set for this path. Collaborating with product teams ensures that ad signals and product features do not interfere with each other. In this regard, the NetMarvel platform utilizes deep event tracking and user behavior modeling to create personalized strategies for different channels and user groups, helping advertisers discover high-value users and design more refined user acquisition and growth paths.

6. Building Real Data Sources to Avoid Blind Spots

While MMPs provide user behavior data, they cannot capture all behaviors, such as cross-platform gaming, activities after 180 days, or re-engagement. To ensure data comprehensiveness, MMP should be viewed as a mobile data stream, and other channel data should be integrated. The core issue to address first is Apple's SKAN (SKAdNetwork). If iOS could achieve the same spend visibility as Android, many studios would solve key blind spots.

Solution: Utilize opt-in user data to analyze the behavior patterns of users who agree to share their data, allowing the projection and analysis of behaviors for those who haven't opted in. Re-engagement and post-180-day attribution can yield a 10%-20% increase in returns, but it's crucial to first address the data blind spots created by SKAN.

7. Incorrect Metrics Leading to Misleading Decisions

In UA strategies, precision is often overemphasized, while confidence intervals are equally critical. Two networks that seem to be accurate may exhibit entirely different performance fluctuations, with some networks having stable LTV while others fluctuate wildly, even though their predictive accuracy is the same.

Solution: Excellent advertising teams use MAPE (Mean Absolute Percentage Error) to regularly backtest the deviation between predictions and actual performance, guiding UA teams with confidence intervals to avoid making impulsive decisions based on single data points.

8. Balancing Optimization Pace and Algorithm Learning

Teams often rush to adjust bids and ROAS targets during ad campaign optimization, but frequent adjustments deprive the algorithm of learning opportunities, impacting overall campaign performance. Expert guidelines suggest adjustments should avoid frequent changes, ensuring that each optimization leaves enough time for the algorithm to learn.

Solution: Follow the "7-20 rule"—adjust only once within each conversion window (7 days) and not by more than 20%. This rhythm better supports algorithm stability and minimizes unnecessary waste due to impatience.

Conclusion: The Future of UA Strategies

As technology continues to evolve, so too do UA strategies. We are already seeing more and more advertising platforms leaning towards "tROAS" bidding strategies and extending attribution windows to achieve predictable growth goals. During this transformation period, global performance marketing service platforms like NetMarvel DSP have helped hundreds of apps achieve their target ROAS within 7 days during new market cold starts and automatically suppress CPI increases during scaling, turning "7-day 12% net ROAS" from a theoretical target into a replicable mass campaign.

In the future, UA managers will increasingly play the role of auditors, using automated tools and probabilistic modeling technologies to optimize strategies and reduce human intervention. With the rapid advancement of AI technology, creative optimization will become more intelligent, reducing manual operations in traditional UA processes, and ultimately, outdated spreadsheet tools will be replaced by automation and more precise data analysis platforms.

The core task in this process is to provide the algorithm with accurate decision-making signals and audit the results to achieve truly precise user acquisition.