MintegralCPI or ROAS? How to Optimize Mintegral Campaigns for Better Results
Summary
The article explores the strategic use of CPI vs. ROAS campaigns for Mintegral. CPI is best for new apps to drive install volume and collect baseline data, while ROAS suits mature apps aiming for high-value users.
Running both models in parallel is discouraged as it confuses the algorithm, potentially lowering ROAS performance. The author emphasizes that ROI should not be the sole focus during scaling; instead, bid high enough to gain traction but not overspend. Mintegral's Hybrid ROAS dynamically optimizes for both IAP and IAA, using oCPI to adjust bids toward revenue goals.
Actionable takeaways: start with CPI if lacking user data, transition to ROAS once post-install data is robust, and use sub-source management to trim low-performing sources. The article also warns against prioritizing ROI too early, as it can limit installs and slow algorithm learning.
Analyst Note
The article's timing is notable as the mobile advertising ecosystem continues to shift toward value-based optimization amid signal loss from privacy changes. The distinction between CPI and ROAS is well-trodden, but the emphasis on avoiding parallel campaigns reflects a practical tension: many UA managers still run both models concurrently to balance volume and quality. The key implication here is that algorithm confusion can degrade performance, particularly as platforms like Mintegral use increasingly sophisticated machine learning models that require clean signal environments.
Another practical impact is the introduction of Hybrid ROAS for apps with mixed monetization (IAA and IAP). This is a response to the reality that many apps don't fall neatly into one revenue model. For UA teams, this means the traditional CPI-to-ROAS funnel may need rethinking—starting with CPI for data collection, then transitioning to a hybrid model rather than strict ROAS.
From a competitive angle, Mintegral is positioning itself as a platform that offers both optimization paths, but with a clear recommendation against fragmentation. This could influence how UA teams allocate budgets across networks, favoring those that can handle complex value signals without sacrificing volume.
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