Optimal Mix of Incentive Strategies for Product Marketing on Social Networks

TL;DR

This paper models the optimal timing of incentive strategies for viral product marketing on social networks using a mean-field approach, revealing simple optimal strategies and their dependence on network structure.

Contribution

It introduces a continuous-time optimal control framework for incentive strategies in viral marketing, providing analytical structure and practical insights.

Findings

Optimal strategies can be influence-and-exploit or exploit-and-influence.

In some cases, incentives are most effective for low degree nodes.

Numerical studies support the theoretical results.

Abstract

We consider the problem of devising incentive strategies for viral marketing of a product. In particular, we assume that the seller can influence penetration of the product by offering two incentive programs: a) direct incentives to potential buyers (influence) and b) referral rewards for customers who influence potential buyers to make the purchase (exploit connections). The problem is to determine the optimal timing of these programs over a finite time horizon. In contrast to algorithmic perspective popular in the literature, we take a mean-field approach and formulate the problem as a continuous-time deterministic optimal control problem. We show that the optimal strategy for the seller has a simple structure and can take both forms, namely, influence-and-exploit and exploit-and-influence. We also show that in some cases it may optimal for the seller to deploy incentive programs mostly for low degree nodes. We support our theoretical results through numerical studies and provide practical insights by analyzing various scenarios.