Dynamic Traffic Allocation for Revenue Maximization on Creator Economy Platform

2026-08-03

AI Summary

This study addresses the trade-off faced by creator economy platforms between short-term advertising revenue and long-term ecosystem health in traffic allocation. Focusing on a heterogeneous population of creators who generate income from both advertisements and direct fan contributions, the work proposes a continuous-time dynamic optimization model and introduces two key strategies: a “most valuable creator first” rule and a “conditional reversal” policy. The analysis reveals that platforms should act as selective gatekeepers—imposing a minimum capability threshold while capping the fanbase size of top creators to mitigate winner-take-all dynamics. Integrating the Bass diffusion model with activation set analysis, the authors design a momentum-adjusted heuristic algorithm. Compared to baseline strategies, the optimal policy reduces revenue loss by up to 25%, and the proposed heuristic closely approximates this optimal performance.

Abstract

Creator economy platforms face a strategic dilemma: allocating traffic to established stars for immediate ad revenue versus nurturing emerging creators to build a follower base for future monetization. We develop a continuous-time dynamic optimization model to characterize the optimal traffic allocation policy for a platform managing heterogeneous creators with dual revenue streams (advertising and direct follower contributions). We characterize the optimal policy analytically, revealing a ``most-valuable-creator-first" rule driven by a forward-looking activation set. Under Bass diffusion dynamics, this policy exhibits a sophisticated ``conditional reversal" strategy, where the platform temporarily prioritizes lagging creators to capitalize on word-of-mouth effects. Regarding the ecosystem structure, we find the optimal policy acts as a selective gatekeeper. Unlike myopic policies that lead to a harsh ``winner-take-all" market, or naive fairness-driven heuristics that foster inefficient ``indiscriminate growth," the optimal policy imposes a strict capability threshold that screens which creators receive platform traffic. Furthermore, it enforces disciplined growth for successful entrants, capping their follower bases at an optimal ceiling to prevent over-investment. Finally, we demonstrate that simple heuristics can lead to significant revenue losses (up to 25\%) and propose a practical ``follower-growth adjusted" heuristic that achieves near-optimal performance by leveraging observed growth momentum.

Problem

• traffic allocation
• creator economy
• revenue maximization
• emerging creators
• platform strategy

Innovation

• dynamic traffic allocation
• creator economy
• optimal policy
• Bass diffusion
• revenue maximization