Idea
Algorithmic ad scheduling platform maximizing user engagement by optimizing ad timing and frequency using psychological behavior models.
Research Paper
Core Innovation
This paper introduces a psychological behavior model incorporating mere exposure, hedonic adaptation, and operant conditioning to optimize ad scheduling. It provides a quasi-linear time algorithm that produces near-optimal ad schedules, outperforming traditional heuristics. The method also enables efficient determination of the optimal number of ads to maximize long-term user interest.
Market Size (TAM)
$20–50B TAM for digital advertising platforms; $2–10B SAM from programmatic ad buyers and marketing agencies. Driven by increasing demand for personalized ad delivery and improved ROI on ad spend.
Potential Customers & Pain Points
- Digital advertisers seeking improved ad engagement
- Ad tech companies needing advanced scheduling algorithms
- Marketing agencies aiming to optimize ad spend efficiency
Business Model
Licensing the ad scheduling algorithm as an API to ad tech platforms and marketing agencies; offering consulting for custom integration and optimization.
Competitive Landscape
- Google Ads
- Facebook Ads
- The Trade Desk
Implementation Challenges
- Integration with existing ad platforms
- User privacy and data regulations
- Complexity of psychological model adoption
Validation Strategy
- Pilot integration with select digital advertisers
- A/B testing against existing scheduling heuristics
- Collecting user engagement metrics to refine models
Research Paper Overview
Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models
Summary
This paper models user interest changes in digital advertising using psychological principles: mere exposure, hedonic adaptation, and operant conditioning. It presents a quasi-linear time algorithm to optimize ad timing and frequency over a continuous interval, achieving near-optimal user engagement. The approach outperforms simple heuristics like uniform spacing and includes a method to determine the optimal number of ads. Experimental results validate the effectiveness of the proposed scheduling strategy.