Creating an Intelligent Social Media Campaign Decision-Support Method
Pages 149 - 158
Abstract
Predicting the success of marketing campaigns on social media can help improve campaign managers’ decision-making (e.g., deciding to stop a marketing campaign) and thus increase their profits. Most research in the field of online marketing has focused on analyzing users’ behavior rather than improving campaign manager decision-making. Furthermore, determining the success of marketing campaigns is quite challenging due to the large number of possible metrics that must be analyzed daily. In this study, we suggest a method that incorporates machine learning models with traditional business rules to provide daily decision recommendations, based on the various metrics and considerations, and aimed at achieving the campaign’s goals. We evaluate our approach on a unique dataset collected from the most popular social networks, Facebook and Instagram. Our evaluation demonstrates the proposed method’s ability to outperform an expert-based method and the machine learning baselines examined, and dramatically increase the campaign managers’ profits.
References
[1]
Ali Abdallah Alalwan. 2018. Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management 42 (2018), 65–77.
[2]
Ali Abdallah Alalwan, Nripendra P Rana, Yogesh K Dwivedi, and Raed Algharabat. 2017. Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics 34, 7 (2017), 1177–1190.
[3]
Sercan Ö Arik and Tomas Pfister. 2021. Tabnet: Attentive interpretable tabular learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 6679–6687.
[4]
Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5–32.
[5]
Malte Brettel, Jens-Christian Reich, Jose M Gavilanes, and Tessa C Flatten. 2015. What drives advertising success on Facebook? An advertising-effectiveness model: Measuring the effects on sales of “likes” and other social-network stimuli. Journal of Advertising Research 55, 2 (2015), 162–175.
[6]
Roderick J Brodie, Linda D Hollebeek, Biljana Jurić, and Ana Ilić. 2011. Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of service research 14, 3 (2011), 252–271.
[7]
Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, and Xian-Sheng Hua. 2016. Deep ctr prediction in display advertising. In Proceedings of the 24th ACM international conference on Multimedia. 811–820.
[8]
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.
[9]
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014).
[10]
Efthymios Constantinides. 2014. Foundations of social media marketing. Procedia-Social and behavioral sciences 148 (2014), 40–57.
[11]
Rodney Graeme Duffett. 2015. Facebook advertising’s influence on intention-to-purchase and purchase amongst Millennials. Internet Research (2015).
[12]
Hazem Rasheed Gaber and Len Tiu Wright. 2014. Fast-food advertising in social media. A case study on Facebook in Egypt. Journal of business and retail management research 9, 1 (2014), 52–63.
[13]
José Manuel Gavilanes, Tessa Christina Flatten, and Malte Brettel. 2018. Content strategies for digital consumer engagement in social networks: Why advertising is an antecedent of engagement. Journal of Advertising 47, 1 (2018), 4–23.
[14]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
[15]
Timothy J Halloran and Richard J Lutz. 2021. Let’s Give Them Something to Talk About: Which Social Media Engagements Predict Purchase Frequency?Journal of Interactive Marketing 56 (2021), 83–95.
[16]
Mohammad Reza Hamidizadeh, Nasser Yazdani, Akbar Alem Tabriz, and Mohammad Mehdi Latifi. 2012. Designing and validating a systematic model of e-advertising. International Journal of Marketing Studies 4, 2 (2012), 130.
[17]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
[18]
Roope Jaakonmäki, Oliver Müller, and Jan Vom Brocke. 2017. The impact of content, context, and creator on user engagement in social media marketing. In Proceedings of the 50th Hawaii international conference on system sciences.
[19]
Tun-Min Jai, Dan Fang, Forrest S Bao, Russell N James III, Tianwen Chen, and Weidong Cai. 2021. Seeing it is like touching it: unraveling the effective product presentations on online apparel purchase decisions and brain activity (An fMRI Study). Journal of Interactive Marketing 53, 1 (2021), 66–79.
[20]
Youngseung Jeon, Seung Gon Jeon, and Kyungsik Han. 2020. Better targeting of consumers: Modeling multifactorial gender and biological sex from Instagram posts. User Modeling and User-Adapted Interaction 30, 5 (2020), 833–866.
[21]
Sang Hoon Jung and Yong Jin Jeong. 2020. Twitter data analytical methodology development for prediction of start-up firms’ social media marketing level. Technology in Society 63 (2020), 101409.
[22]
Firuz Kamalov, Ikhlaas Gurrib, and Khairan Rajab. 2021. Financial forecasting with machine learning: price vs return. Kamalov, F., Gurrib, I. & Rajab, K.(2021). Financial Forecasting with Machine Learning: Price Vs Return. Journal of Computer Science 17, 3 (2021), 251–264.
[23]
Sachin S Kamble, Angappa Gunasekaran, Vikas Kumar, Amine Belhadi, and Cyril Foropon. 2021. A machine learning based approach for predicting blockchain adoption in supply Chain. Technological Forecasting and Social Change 163 (2021), 120465.
[24]
Brendan James Keegan and Jennifer Rowley. 2017. Evaluation and decision making in social media marketing. Management decision (2017).
[25]
Michael A King, Alan S Abrahams, and Cliff T Ragsdale. 2015. Ensemble learning methods for pay-per-click campaign management. Expert Systems with Applications 42, 10 (2015), 4818–4829.
[26]
Stefan Lessmann, Johannes Haupt, Kristof Coussement, and Koen W De Bock. 2021. Targeting customers for profit: An ensemble learning framework to support marketing decision-making. Information Sciences 557 (2021), 286–301.
[27]
Guannan Liu, Yanjie Fu, Tong Xu, Hui Xiong, and Guoqing Chen. 2014. Discovering temporal retweeting patterns for social media marketing campaigns. In 2014 IEEE international conference on data mining. IEEE, 905–910.
[28]
Xianghua Lu, Xia Zhao, and Ling Xue. 2016. Is combining contextual and behavioral targeting strategies effective in online advertising?ACM Transactions on Management Information Systems (TMIS) 7, 1 (2016), 1–20.
[29]
Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).
[30]
Yossi Luzon, Rotem Pinchover, and Eugene Khmelnitsky. 2022. Dynamic budget allocation for social media advertising campaigns: optimization and learning. European Journal of Operational Research 299, 1 (2022), 223–234.
[31]
Pedro Marcelino, Maria de Lurdes Antunes, Eduardo Fortunato, and Marta Castilho Gomes. 2021. Machine learning approach for pavement performance prediction. International Journal of Pavement Engineering 22, 3 (2021), 341–354.
[32]
Masoud Mazloom, Robert Rietveld, Stevan Rudinac, Marcel Worring, and Willemijn Van Dolen. 2016. Multimodal popularity prediction of brand-related social media posts. In Proceedings of the 24th ACM international conference on Multimedia. 197–201.
[33]
Sérgio Moro, Paulo Rita, and Bernardo Vala. 2016. Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research 69, 9 (2016), 3341–3351.
[34]
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems 31 (2018).
[35]
Bala Ramasamy, Matthew CH Yeung, and Alan KM Au. 2008. Can holidays boost consumption? The case of Hong Kong. Journal of Public Affairs: An International Journal 8, 4 (2008), 281–287.
[36]
Robert Rietveld, Willemijn Van Dolen, Masoud Mazloom, and Marcel Worring. 2020. What you feel, is what you like influence of message appeals on customer engagement on Instagram. Journal of Interactive Marketing 49 (2020), 20–53.
[37]
Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. 253–260.
[38]
Tereza Semerádová and Petr Weinlich. 2019. Calculation of Facebook marketing effectiveness in terms of ROI. In Leveraging Computer-Mediated Marketing Environments. IGI Global, 286–310.
[39]
Roy J Shephard. 2003. Limits to the measurement of habitual physical activity by questionnaires. British journal of sports medicine 37, 3 (2003), 197–206.
[40]
Ravid Shwartz-Ziv and Amitai Armon. 2022. Tabular data: Deep learning is not all you need. Information Fusion 81 (2022), 84–90.
[41]
Adir Solomon, Ariel Bar, Chen Yanai, Bracha Shapira, and Lior Rokach. 2018. Predict demographic information using word2vec on spatial trajectories. In Proceedings of the 26th conference on user Modeling, adaptation and personalization. 331–339.
[42]
Pål Sundsøy, Johannes Bjelland, Asif M Iqbal, Yves-Alexandre de Montjoye, 2014. Big data-driven marketing: how machine learning outperforms marketers’ gut-feeling. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer, 367–374.
[43]
Andrea Heintz Tangari, Judith Anne Garretson Folse, Scot Burton, and Jeremy Kees. 2010. The moderating influence of consumers’ temporal orientation on the framing of societal needs and corporate responses in cause-related marketing campaigns. Journal of Advertising 39, 2 (2010), 35–50.
[44]
Esteban Vázquez-Cano and Paz Díez-Arcón. 2021. Facebook or LMS in distance education? Why university students prefer to interact in Facebook groups. The International Review of Research in Open and Distributed Learning 22, 3 (2021), 119–141.
[45]
Taining Yang. 2012. The decision behavior of Facebook users. Journal of Computer Information Systems 52, 3 (2012), 50–59.
[46]
Zijun Yao, Deguang Kong, Miao Lu, Xiao Bai, Jian Yang, and Hui Xiong. 2022. Multi-View Multi-Task Campaign Embedding for Cold-Start Conversion Rate Forecasting. IEEE Transactions on Big Data 9, 1 (2022), 280–293.
[47]
Yang Zhao, Lin Wang, Huijie Tang, and Yaming Zhang. 2020. Electronic word-of-mouth and consumer purchase intentions in social e-commerce. Electronic Commerce Research and Applications 41 (2020), 100980.
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June 2024
338 pages
ISBN:9798400704338
DOI:10.1145/3627043
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Published: 22 June 2024
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UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization
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