Pricing Strategies for Viral Marketing on Social Networks

David Arthur, Rajeev Motwani, Aneesh Sharma, Ying Xu

Research output: Chapter in Book/Report/Conference proceedingChapter

74 Scopus citations


We study the use of viral marketing strategies on social networks that seek 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. This choice of prices for the buyers is termed as the seller's strategy. 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. © 2009 Springer-Verlag Berlin Heidelberg.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
PublisherSpringer Nature
Number of pages12
ISBN (Print)9783642108402
StatePublished - 2009
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research has been supported in part by NSF Grant ITR-0331640, TRUST(NSF award number CCF-0424422), and grants from Cisco, Google, KAUST,Lightspeed, and Microsoft. The third author is grateful to Jason Hartline andMukund Sundararajan for useful discussions.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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