Open AccessUncertain KOL Selection With Multiple Constraints in Advertising Promotion
Author(s) -
Meiling Jin,
Yufu Ning,
Bo Li,
Fengming Liu,
Chunhua Gao,
Yichang Gao
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3121518
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Social media marketing is a new mode of marketing industry. KOL (Key Opinion Leader) marketing is a popular way of social media marketing, which is profit-oriented. During the brand building in the early stage of marketing, the product side generally carries out corresponding advertising promotion, so as to achieve the purpose of promoting marketing. As decision-makers, different KOLs selection affects the final promotion effect. Therefore, to understand the advertising promotion effect of social media, this paper considers the instability of the network environment and the uncertainty of a KOL’s promotion ability, solves the advertising promotion problem in the absence of historical data, and provides meaningful insights for decision-makers. First, this paper takes the advertising promotion effect of the KOL belonging to different levels and the cost of advertisers as uncertain variables and constructs an uncertain KOL selection model considering the constraints of income (promotion effect), cost and risk. Second, based on the relevant algorithm of uncertainty theory, the uncertainty of the model is eliminated, the uncertainty model is transformed into a corresponding clear model, and the KOL’s optimal choice at each level is calculated. Finally, the effectiveness and practicability of the model and the algorithm are verified.