Pricing strategies for viral marketing on Social Networks

TL;DR

This paper models viral marketing on social networks, incorporating influence and pricing strategies to maximize revenue, and proposes an approximation algorithm with practical improvements despite the problem's computational complexity.

Contribution

It introduces a generalized influence model with pricing, proves the NP-hardness of revenue maximization, and offers a constant-factor approximation strategy with local search enhancements.

Findings

Proposed a revenue-guaranteed strategy within a constant factor of optimal.

Extended influence models to include pricing and discounts.

Demonstrated practical improvements using local search techniques.

Abstract

We study the use of viral marketing strategies on social networks to maximize revenue from the sale of a single product. We propose a model in which the decision of a buyer to buy the product is influenced by friends that own the product and the price at which the product is offered. The influence model we analyze is quite general, naturally extending both the Linear Threshold model and the Independent Cascade model, while also incorporating price information. We consider sales proceeding in a cascading manner through the network, i.e. a buyer is offered the product via recommendations from its neighbors who own the product. In this setting, the seller influences events by offering a cashback to recommenders and by setting prices (via coupons or discounts) for each buyer in the social network. Finding a seller strategy which maximizes the expected revenue in this setting turns out to be NP-hard. However, we propose a seller strategy that generates revenue guaranteed to be within a constant factor of the optimal strategy in a wide variety of models. The strategy is based on an influence-and-exploit idea, and it consists of finding the right trade-off at each time step between: generating revenue from the current user versus offering the product for free and using the influence generated from this sale later in the process. We also show how local search can be used to improve the performance of this technique in practice.