Distributing digital product in peer-to-peer network: The diffusion process and the optimal referrals

Peer-to-Peer (P2P) networks are fast emerging as a viable and cost effective alternative for content delivery on the Internet. A widely documented problem in P2P networks has been the large number of free riders---users who consume content from others in the network without distributing it to other users. Academics and practitioners have proposed offering distribution referrals---payments to users who distribute content to others---to provide incentives for users to distribute content, This research studies the diffusion process in P2P networks and the optimal referral strategies such that a content distributor's profit is maximized.; Starting with an analogy between P2P diffusion and epidemic diffusion, we first develop a stochastic diffusion model for flat P2P networks. Then we develop a deterministic approximation that is computationally efficient, and expand our study to account for generation of multiple requests and forwarding of requests. Finally, we model the diffusion process in a hierarchical P2P network. Our results show that offering referrals is effective in speeding product diffusion. But the marginal benefit of referral is decreasing. We also find that network structure exerts a dominant influence on diffusion and determines whether and how other parameters influence the rate of diffusion. Simulation results suggest that our models yield good predictions.; Based on a slightly simplified diffusion model, we study the optimal referral strategies. We first propose a discrete referral policy in which a constant referral is provided throughout a part of or the whole diffusion process. The results indicate that the optimal referral decreases with demand for content and number of unselfish users in the network. Then we present a dynamic referral strategy where the firm continuously adjusts the referral. For a short-term marketing campaign, the optimal strategy involves a constant high referral at the beginning, followed by a convex decreasing trajectory that may or may not reach zero. In the infinite time horizon case, after the constant referral period, the optimal referral trajectory always decreases and converges to a stationary value. This research represents a first step towards studying marketing issues in this emerging distribution format for entertainment and information products.