At scale, mobile user acquisition stops being about channels and starts being about mechanics. OEM traffic and in-app advertising are often discussed side by side, but treating them as interchangeable is one of the fastest ways to lose efficiency. Both channels can drive growth, but they behave very differently under pressure. Understanding where each one works — and where it breaks — is critical for UA managers and advertisers building sustainable performance models.

1. Why CPI and Scale Are No Longer Enough

Legacy UA Logic	Why It No Longer Works
Lowest CPI wins	CPI no longer correlates with user value
Scale first, optimize later	Optimization windows are shorter and noisier
Install equals success	Post-install behavior defines real performance
Attribution is precise	Privacy changes reduce signal quality

The modern UA question is no longer how many installs a channel delivers, but how reliably it delivers users who retain, convert, and monetize.

2. Two Channels, Two Types of Demand

Demand Mechanics Comparison

Dimension	In-App Traffic	OEM Traffic
User state	Active inside another app	Exploring device or system
Nature of demand	Interruptive	Contextual and latent
Discovery moment	Mid-activity	Device setup or system navigation
Intent formation	After the click	Before the install
Role in funnel	Demand discovery	Demand capture

3. Core Strengths and Limitations

Channel Capabilities

Aspect	In-App Traffic	OEM Traffic
Speed of scale	High	Moderate
Creative flexibility	Very high	Limited but contextual
Cost model	Auction-driven	Inventory-defined
Cost volatility	High	Low to medium
Early retention	Inconsistent	More stable
Attribution clarity	High but noisy	Often undervalued

4. Hidden Economics UA Teams Often Miss

Cost Behavior at Scale

Factor	In-App	OEM
Budget elasticity	High	Limited
CPI behavior	Increases with scale	More stable
Impact of competition	Strong	Moderate
Predictability	Lower	Higher

Intent Filtering Stage

Channel	When Intent Is Filtered	Practical Implication
In-App	After install via events and LTV models	Requires strong post-install optimization
OEM	Before install via placement context	Cleaner early cohorts

LTV Distribution Shape

Metric	In-App Traffic	OEM Traffic
LTV curve	Wide, long-tail	Narrow and concentrated
Outliers	Many	Few
Median predictability	Low	High
Payback modeling	More complex	Easier and more stable

5. Where Scaling Breaks: In-App Traffic Mistakes

Common In-App Scaling Errors

Mistake	Result	Corrective Action
Scaling low CPI sources	Retention collapses	Gate scale by early events
Scaling single creatives	Creative fatigue	Scale creative clusters
Ignoring auction dynamics	CPI inflation	Gradual budget ramps
CTR-driven decisions	Low-intent users	Optimize beyond installs

6. Where Scaling Breaks: OEM Traffic Mistakes

Common OEM Scaling Errors

Mistake	Result	Corrective Action
Treating OEM like in-app	Quality degradation	Scale by scenario, not spend
Cutting too early	Missed value	Extend evaluation windows
Over-optimization	Performance instability	Optimize in large iterations
Last-click bias	Underinvestment	Measure incrementality

7. Attribution Reality Check

Attribution Aspect	In-App	OEM
Last-click credit	Usually captured	Often lost
Organic overlap	Lower	Higher
Assisted installs	Less common	Common
Incrementality visibility	Medium	High when measured correctly

8. How Experienced UA Teams Combine Both Channels

Strategic Role Allocation

Strategic Role	OEM Traffic	In-App Traffic
Establishing quality baseline	Yes	No
Driving performance upside	No	Yes
Market entry	Yes	Conditional
Rapid experimentation	No	Yes
CAC stabilization	Yes	No
Aggressive scaling	Conditional	Yes

Final Takeaway

Key Insight	Why It Matters
Channels are not interchangeable	Each breaks differently at scale
CPI is not performance	Intent and LTV define value
OEM is stable, not slow	Stability improves predictability
In-app is powerful, not risky	Discipline determines outcome

OEM and in-app traffic are different layers of the same acquisition stack.

UA teams that win long-term are not those chasing the lowest CPI, but those who understand where demand forms, how intent is filtered, and where performance breaks at scale.

That is what turns traffic into sustainable growth.