TY - JOUR
T1 - Using the data mining technique to predict successful customer engagement of marketing campaigns in social media
AU - Al Rabaani, Fatema Salim
AU - Said, Aiman Moyaid
AU - Fageeri, Sallam Osman
AU - AlAbdulsalam, Abdul Rahman Khalifa
N1 - Publisher Copyright:
© 2023 Inderscience Enterprises Ltd.
PY - 2023
Y1 - 2023
N2 - Marketing in social media platforms plays a vital role in enhancing the return of investment for start-up companies in the fashion industry. Predicting the level of customer engagement of the marketing campaign in social media reveals customers' preferences and public attention towards the marketing campaign. This research proposed using different data mining classifiers to predict the success of online marketing campaigns to reduce the efforts and allocated resources toward achieving the goal of marketing in the fashion industry. The research collected 8,151 marketing campaigns published on Instagram users account for the fashion industry in Oman, formed social-media-based metrics, and formulated data mining algorithms to predict the customer engagement level. The results show that the model induced by neural networks provides the highest accuracy, 96%, in predicting customer engagement levels. The results illustrated that the number of posts published is not essential to gaining additional interactions from the user.
AB - Marketing in social media platforms plays a vital role in enhancing the return of investment for start-up companies in the fashion industry. Predicting the level of customer engagement of the marketing campaign in social media reveals customers' preferences and public attention towards the marketing campaign. This research proposed using different data mining classifiers to predict the success of online marketing campaigns to reduce the efforts and allocated resources toward achieving the goal of marketing in the fashion industry. The research collected 8,151 marketing campaigns published on Instagram users account for the fashion industry in Oman, formed social-media-based metrics, and formulated data mining algorithms to predict the customer engagement level. The results show that the model induced by neural networks provides the highest accuracy, 96%, in predicting customer engagement levels. The results illustrated that the number of posts published is not essential to gaining additional interactions from the user.
KW - artificial intelligence
KW - customer engagement
KW - data mining
KW - machine learning
KW - marketing campaign
UR - http://www.scopus.com/inward/record.url?scp=85168709005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85168709005&partnerID=8YFLogxK
U2 - 10.1504/IJBIDM.2023.132611
DO - 10.1504/IJBIDM.2023.132611
M3 - Article
AN - SCOPUS:85168709005
SN - 1743-8187
VL - 23
SP - 166
EP - 183
JO - International Journal of Business Intelligence and Data Mining
JF - International Journal of Business Intelligence and Data Mining
IS - 2
ER -