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Two-stage pricing strategy with price discount in online social networks.

Authors :
Liang, Ziwei
Yuan, He
Du, Hongwei
Source :
Theoretical Computer Science. Aug2021, Vol. 882, p1-14. 14p.
Publication Year :
2021

Abstract

• We broaden the content of the profit maximization problem of the product company based. • We proposed a Two-stage Pricing with Discount Model (TPDM). • We proposed a Two-stage with Discount Greedy algorithm (TSDG) and evaluated its performance. The rapid development of online social networks (OSNs) has changed the way people communicate and has brought many new opportunities for Internet product marketing. For a product company, the profit gained is directly related to the price of the product and the number of products used. Designing an effective marketing strategy is crucial to obtaining more profits. However, most existing research only focuses on maximizing product influence, rather than explicitly incorporating pricing factors into the design of marketing strategies. This article studies product marketing strategies and pricing models, and proposes a two-stage pricing discount model (TPDM). The TPDM model divides product marketing into two stages: the original price stage and the discount stage. It also studies the impact of advertising marketing (AM) and word-of-mouth marketing (WM) on the number of products used. Based on the TPDM model, a two-stage discount greedy (TSDG) algorithm is proposed to realize product pricing for product companies. At last, we use several real social network data sets for comparative experiments. The experimental results show that the TSDG algorithm divides the product marketing into two stages and pricing can tap the economic benefits of the product, so that more users can use the product. Compared with other algorithms, it not only increases the profit of the product company by more than 20%, but also improves efficiency. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03043975
Volume :
882
Database :
Academic Search Index
Journal :
Theoretical Computer Science
Publication Type :
Academic Journal
Accession number :
151734494
Full Text :
https://doi.org/10.1016/j.tcs.2021.05.035