Managing Online Content to Build a Follower Base: Model and Applications

Abstract

Content providers manage their production by regulating the pace at which content is created and released. They have two types of consumers: existing ones, or followers; and new visitors who may become followers. To maximize their effectiveness, providers must consider the direct, short-term effect of content and also its indirect, long-term effect on the retention and expansion of their follower base. We develop a simple model to study the dynamics of building up a follower base and then combine that model with stochastic dynamic programming to optimize the content provider’s profit. We find that optimal content policies exhibit trajectories whereby both content and followers increase until reaching a steady state at which revenues equal marginal costs. It is therefore preferable to start slow and then accelerate only after successful content has increased the follower base; thus past success makes the content provider work harder. We apply our model to bloggers by analyzing the traffic from new and returning visitors to several blogs. We find that optimized posting activity yields significantly higher profits than does a steady or seasonal content strategy. Our model contributes to the growing literature on data-driven optimization and prescriptive analytics, specifically for content management.