A practical engineering approach to interaction-aware ad suppression
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Why This Problem Matters
In many mobile applications, ads are inserted based on screen placement or timing rules, without considering what the user is actively doing.
From an engineering perspective, this creates several problems:
• Ads appear during transactional or repetitive actions
• UI performance degrades during high-frequency updates
• Monetization logic becomes tightly coupled with UI flows
While building a production mobile application, we observed that the issue wasn’t which ads were shown — it was when they were shown.
This led us to implement an interaction-aware ad suppression system that treats monetization as a runtime decision based on user interaction state.
This post focuses on the engineering side of that system.
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Tracking Interaction Events
The first step is capturing meaningful interaction signals locally.
Examples of events we track:
• Screen transitions
• Button confirmations
• Rapid repeated inputs
• Pauses or idle periods
Rather than reacting to individual events, we group them into short time windows and evaluate them as sequences.
This avoids overreacting to noise while still capturing user intent.
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Deriving Interaction States
From these event sequences, the application derives a small set of interaction states, such as:
• Task-execution state
Active workflows or transactional actions
• Navigation state
Screen changes and exploration
• Review state
Pauses, confirmations, or summaries
• Idle state
Low or no interaction
Each state has different tolerance for interruption.
For example:
• Task-execution → ads suppressed
• Review / idle → ads may be allowed
This keeps the decision logic explicit and explainable.
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Suppression-by-Default Logic
A key design decision was to suppress ads by default.
Instead of trying to detect every possible “bad” moment, the system assumes ads are disabled unless explicitly permitted by the current interaction state.
This approach:
• Simplifies decision rules
• Prevents accidental interruptions
• Reduces edge cases
From an engineering standpoint, it also minimizes unnecessary UI re-renders and network calls during critical workflows.
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Local Decision-Making
All interaction state evaluation happens on-device.
The client application:
• Determines the current interaction state
• Decides ad eligibility
• Records basic post-delivery feedback
Remote services, if used, are limited to:
• Aggregation
• Auditing
• Offline analysis
They are not part of the real-time decision path.
This improves latency, reliability, and behavior under poor network conditions.
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Lightweight Feedback, Not Over-Optimization
After an ad is shown (when allowed), the system observes simple signals:
• Immediate dismissal
• Continued workflow
• No interaction
These signals are used to adjust thresholds gradually, not to aggressively optimize delivery.
The goal is stability, not maximization.
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What Worked Well in Practice
From an implementation standpoint, this approach resulted in:
• Cleaner separation between UI and monetization logic
• Fewer performance regressions
• More predictable application behavior
• Reduced user disruption
Most importantly, the system aligned monetization behavior with actual user interaction patterns.
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Final Thoughts
Ads don’t need to compete with user workflows.
By treating monetization as an interaction-aware engineering problem, rather than a purely revenue-driven feature, applications can remain sustainable without sacrificing reliability or trust.
Sometimes the best decision a system can make is not acting at all.
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About the Author
Pradeep Kumar Jalakam is a software engineer and product builder working on scalable, mobile-first applications with a focus on system design, performance, and responsible monetization.
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