Business competitive analysis using promoted post detection on social media
Aman Srivastava and
Journal of Retailing and Consumer Services, 2020, vol. 54, issue C
Social media has leveraged many brands and companies started interaction with customers through it. As the upsurge of brand popularity and their social growth on social media channels, the role to analyze and investigate the competitor's behaviours on social media becomes a crucial part. By utilizing these behaviours a brand can investigate and optimize their competitive strategies in order to enhance the audience reach, improved customer acquisition and increase in overall profits. Therefore, monitoring and analyzing the social media behaviour of competitors is a cutting edge direction towards the enhancement of competitive benefits and efficiently decipher the competition. This research paper is an effort to help brands to know the insights such as the posts which are promoted compared to those which are organic. This article presents the detailed description of novel approach applied for promoted post detection problem. Promoted post detection makes use of social media data to provide insights related to brand's own and competitors posting behaviours and promotional strategies. We make use of ensemble machine learning techniques-bagging and boosting to train different weak learners and combine the results by majority voting and weighted average. We make use of a logistic regression model in stacking while combining used generalized linear model and ensemble models. Results show the 95% accuracy, 97% precision, 96.5% Recall and 97% F1 score of classification results of brand promoted posts as boosted or organic. The result unveils that ensemble technique stands out as an effective approach for social media promoted post detection.
Keywords: Brand promotion; Ensemble model; Post promotion; Random forest; XGBoost; Bagging; Boosting; Logistic regression (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:54:y:2020:i:c:s0969698919306708
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