Dynamic Ad Orchestration is an adaptive advertising system for Sheyda VOD. It replaces a fixed ad rule with decisions based on content value, viewing scenario and viewer intent, so premium monetization and viewer retention stop competing. I designed the service framework and the intent-aware decision system behind it.
Balancing Revenue and Viewer Experience
No behavioral Adaptation
The player couldn't tell binge-watching, resuming, seeking and rewatching apart
Binge-watching, resuming, seeking and rewatching are different moments, but the player treated them the same.
Decision: Build a user scenario engine with a separate ad journey for each scenario, so delivery adapts to what the viewer is doing.
Revenue and retention are designed together. Every ad decision has to protect the viewer’s experience as well as the sponsor’s requirements.
Ad Logic framework, Behavioral Modeling, and Experience Strategy
Dynamic Ad Experience Strategy
Designed a behavior-driven ad orchestration framework based on content value and user intent
Mapped adaptive ad journeys across key viewing scenarios
Defined service rules and experience guardrails to balance monetization and retention
Behavioral Analytics Strategy
Defined the analytics strategy to measure ad performance and viewer behavior
Chose completion rate, CTR, and abandonment signals as the key metrics
Planned optimization workflows using behavioral insights and scenario analysis
Content Value Logic: Sets ad load from exclusivity level, duration and content value, so premium titles and casual viewing get different treatment
User Scenario Engine: Adapts ad journeys to binge-watching, resume, seeking, rewatch and new sessions to reduce viewer friction
Ad Pod Management: Structures pre-roll and mid-roll sequences with pod rules, frequency control and skip logic, balancing sponsor requirements with seamless playback
Intent Detection: Reads real-time signals of viewer intent, engagement and abandonment risk to guide each ad decision
Playback Experience: Defines guardrails for sequence continuity, stability detection, fallback logic and interruption control
System Optimization: Tracks completion rate, CTR, and retention signals in optimization loops to refine ad strategies over time
Preparing for Launch and Continues Optimization
Alignment and Validation
Aligned ad delivery goals with product, sales and engineering teams
Validated ad decision flows across viewing scenarios and playback journeys
Refined ad placement strategy using behavioral insights and content value signals
Measurement Plan
Set completion rate, CTR, retention signals and playback friction as the launch metrics
Planned structured rule testing and optimization cycles after launch
Planned AI-assisted analysis to generate insights and prioritize improvements
The service is in pre-launch, so these are expected results with a defined way to verify them. Results will be added after launch.
Content-Based Ad Load
Expected Result: Premium titles carry more inventory while casual titles stay light, so monetization grows without a blanket increase in interruptions.
Measured By: Completion rate and abandonment signals, compared by content tier.
Scenario-Based Journeys
Expected Result: Fewer interruptions at high-friction moments, such as resuming playback and seeking through content.
Measured By: Abandonment signals and playback friction across viewing scenarios to identify high-friction moments.
Pod Rules and Guardrails
Expected Result: Smoother playback with uninterrupted sequences that continue without breaking and a fallback when delivery is unstable.
Measured By: Completion rate, retention signals and how often fallback logic triggers.
Expected Result: Better-timed ads that align with viewing behavior and reduce exits caused by poorly timed interruptions.
Measured By: CTR, abandonment signals, and changes in exit behavior around ad placements.
Baseline for Comparison
Every metric is compared against the previous fixed three mid-roll rule, providing a consistent baseline to evaluate performance changes and measure the impact of the new ad strategy.
Behavior-driven decisions can improve ad relevance beyond placement-based rules
Service Design exposed opportunities across content, playback and monetization flows
Defining metrics before launch reduces assumptions in later optimization
Balancing business goals with viewer experience supports more sustainable monetization
Service Design · UX Strategy · User Journey Mapping · Service Blueprint · Behavioral Analytics · System Thinking · Decision Frameworks · KPI Strategy · Experience Architecture ·
Cross-functional Collaboration · AI-assisted UX Research · ChatGPT · Claude · NotebookLM
I focus on designing system-driven experiences that align with business goals.
If this approach resonates with your product, feel free to get in touch.
