Pricing and Referrals in Diffusion on Networks

TL;DR

This paper models how network structure influences technology adoption, showing that referral incentives and pricing strategies can optimize sales and welfare depending on network topology.

Contribution

It introduces a dynamic game framework analyzing referral incentives and pricing strategies for technology adoption on networks, highlighting optimal policies for different network types.

Findings

Referral incentives induce early adoption among high- and low-degree consumers.

Optimal strategies vary: referral incentives are best for some networks, while price discounts work better for others.

The model reveals welfare implications of different marketing policies.

Abstract

When a new product or technology is introduced, potential consumers can learn its quality by trying the product, at a risk, or by letting others try it and free-riding on the information that they generate. We propose a dynamic game to study the adoption of technologies of uncertain value, when agents are connected by a network and a monopolist seller chooses a policy to maximize profits. Consumers with low degree (few friends) have incentives to adopt early, while consumers with high degree have incentives to free ride. The seller can induce high-degree consumers to adopt early by offering referral incentives - rewards to early adopters whose friends buy in the second period. Referral incentives thus lead to a `double-threshold strategy' by which low and high-degree agents adopt the product early while middle-degree agents wait. We show that referral incentives are optimal on certain networks while inter-temporal price discrimination (i.e., a first-period price discount) is optimal on others, and discuss welfare implications.