Sequential Workflow Ad Placement Using Dynamic Risk-Reward Scoring
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Solution Overview
Problem
Existing advertisement placement systems fail to dynamically adjust to user interactions in sequential workflows, leading to potential distractions, resource wastage, and reduced completion of primary activities due to inappropriate timing and relevance of secondary advertisements.
Innovation Solution
A system that uses machine learning techniques to evaluate a risk-reward score for advertisement placement, dynamically updating thresholds and recommending optimal stages for displaying advertisements based on user interactions and contextual factors, ensuring the advertisements enhance rather than hinder the completion of primary activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If advertisements are placed in sequential workflows, then revenue generation is improved, but user completion of primary activities deteriorates due to distractions
Solution Approach 1:
The system dynamically adjusts advertisement placement decisions based on real-time user interaction data and contextual factors. The risk-reward score is continuously updated as users progress through workflow stages, allowing the system to adapt advertisement placement timing and targeting to minimize disruption while maximizing revenue potential.
Solution Approach 2:
The system changes parameters such as risk-reward thresholds, user engagement metrics, and contextual relevance scores to optimize advertisement placement. By adjusting these parameters based on user behavior patterns and workflow stage characteristics, the system balances revenue generation with maintaining user productivity.
2Quantity of substance
If advertisements are displayed at multiple stages, then advertising revenue is improved, but user distraction and resource wastage increase
Solution Approach 1:
The system applies different advertisement placement strategies to different workflow stages based on local characteristics. Each stage is evaluated individually using risk-reward scoring that considers stage-specific user engagement patterns, making advertisement placement decisions tailored to each location in the workflow rather than applying a uniform approach.
Solution Approach 2:
The system selectively places advertisements at only certain workflow stages rather than all stages, using partial action. By choosing to display advertisements only when the risk-reward score indicates favorable conditions, the system avoids excessive advertisement exposure that would waste user resources and cause unnecessary distraction.
3Productivity
If risk-reward thresholds are set low, then advertisement placement frequency is improved, but user experience deteriorates
Solution Approach 1:
The system uses feedback from user interactions with advertisements and workflow progression to dynamically adjust risk-reward thresholds. User experience data feeds back into the scoring mechanism, allowing the system to learn from past placements and optimize future decisions to maintain both advertisement frequency and user experience quality.
Data Source
AI summary
A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include engaging a user in a primary activity; the primary activity may include a plurality of stages. The operations may include compiling at least one risk-reward score for an advertisement placement of an advertisement for at least one of the plurality of stages. The operations may include updating, dynamically, the at least one risk-reward score and identifying a recommended stage for the at least one advertisement; the recommended stage may be based on the risk-reward score. The operations may include displaying the at least one advertisement to the user at the recommended stage.