Transforming Digital Marketing with Machine Learning Algorithms
DOI:
https://doi.org/10.13052/jgeu0975-1416.1323Keywords:
Digital marketing, machine learning (ML) algorithms, customer segmentation, personalized marketing, predictive analytics, marketing automation, campaign optimization, customer behavior predictionAbstract
In the era of rapid technological advancements, digital marketing has evolved significantly, leveraging innovative technologies to enhance customer engagement, personalize experiences, and optimize marketing strategies. One of the most promising approaches to transforming digital marketing is the integration of ML algorithms, which can automate decision-making processes, predict consumer behavior, and improve marketing campaign effectiveness. This paper investigates the integration of ML techniques into digital marketing, aiming to enhance customer engagement, personalization, and campaign effectiveness through data-driven strategies. The objective is to demonstrate how ML can transform traditional digital marketing approaches by automating decision-making and predicting consumer behavior. Using a Python-based implementation, the study applies key ML models – classification, clustering, and regression – to practical digital marketing tasks such as customer segmentation, personalized content recommendation, and performance analytics. The methodology involves utilizing Python libraries including Scikit-learn, TensorFlow, and Keras to develop and evaluate ML models on relevant marketing datasets. The findings reveal that ML-driven marketing strategies can significantly improve customer targeting, increase return on investment, and deliver more personalized user experiences. Additionally, the paper identifies and discusses challenges such as data quality, algorithmic bias, and ethical concerns surrounding the use of personal data. These insights underscore the transformative potential of ML in digital marketing, while also emphasizing the importance of responsible and transparent implementation. The paper concludes that when thoughtfully applied, ML offers a powerful toolset for businesses seeking to innovate and optimize their marketing efforts in the digital age.
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A. Sharma, S. Poojitha, A. Saxena, M. M. Bhanushali, and P. Rawal, “A conceptual analysis of ML towards digital marketing transformation,” in 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), 2022, pp. 313–316.
G. Ramkumar, B. Othman, B. Malviya, A. J. Mohamma, M. S. Narayana, and D. Verma, “A conceptual analysis on the impact of ML towards on digital marketing transformation,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 2274–2278.
A. Miklosik and N. Evans, “Impact of big data and ML on digital transformation in marketing: A literature review,” Ieee Access, vol. 8, pp. 101284–101292, 2020.
H. Malik, G. Chaudhary, and S. Srivastava, “Digital transformation through advances in artificial intelligence and ML,” Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2. SAGE Publications Sage UK: London, England, pp. 615–622, 2022.
D. Andayani, M. Madani, H. Agustian, N. Septiani, and L. I. W. Ming, “Optimizing Digital Marketing Strategies through Big Data and ML: Insights and Applications,” J. Comput. Sci. Technol. Appl., vol. 1, no. 2, pp. 104–110, 2024.
R. S. K. Boddu, A. A. Santoki, S. Khurana, P. V. Koli, R. Rai, and A. Agrawal, “An analysis to understand the role of ML, robotics and artificial intelligence in digital marketing,” Mater. Today Proc., vol. 56, pp. 2288–2292, 2022.
K. Zaman, “Transformation of marketing decisions through artificial intelligence and digital marketing,” J. Mark. Strateg., vol. 4, no. 2, pp. 353–364, 2022.
A. Miklosik, M. Kuchta, N. Evans, and S. Zak, “Towards the adoption of ML-based analytical tools in digital marketing,” Ieee Access, vol. 7, pp. 85705–85718, 2019.
A. Balkaya et al., “Developing Customer Segmentation Models for Digital Marketing Campaigns using ML.”
C. Wang, “Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach,” Inf. Process. Manag., vol. 59, no. 6, p. 103085, 2022.
R. K. Behera, A. Gunasekaran, S. Gupta, S. Kamboj, and P. K. Bala, “Personalized digital marketing recommender engine,” J. Retail. Consum. Serv., vol. 53, p. 101799, 2020.
M. S. Ullal, I. T. Hawaldar, R. Soni, and M. Nadeem, “The role of ML in digital marketing,” Sage Open, vol. 11, no. 4, p. 21582440211050390, 2021.
V. Singh, B. Nanavati, A. K. Kar, and A. Gupta, “How to maximize clicks for display advertisement in digital marketing? A reinforcement learning approach,” Inf. Syst. Front., vol. 25, no. 4, pp. 1621–1638, 2023.
A. Manzoor, M. A. Qureshi, E. Kidney, and L. Longo, “A Review on ML Methods for Customer Churn Prediction and Recommendations for Business Practitioners,” IEEE Access, 2024.
N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, “Artificial intelligence, ML, and deep learning for advanced business strategies: a review,” Partners Univers. Int. Innov. J., vol. 2, no. 3, pp. 147–171, 2024.
O. U. Idhalama and E. O. Makori, “Artificial intelligence, deep learning, ML, robotics and digital transformation: applications, implications and future,” Ukr. J. Educ. Stud. Inf. Technol., vol. 12, no. 3, pp. 1–21, 2024.
A. Nikolajeva and A. Teilans, “ML Technology Overview In Terms Of Digital Marketing And Personalization.,” ECMS, pp. 125–130, 2021.
D. Chaffey and P. R. Smith, Digital marketing excellence: planning, optimizing and integrating online marketing. Routledge, 2022.
J.-A. Choi and K. Lim, “Identifying ML techniques for classification of target advertising,” ICT Express, vol. 6, no. 3, pp. 175–180, 2020.
G. Manoharan, A. B. Mishra, S. B. K. Adusumilli, M. Chavva, H. Damancharla, and D. S. Lenin, “Supervised Learning for Personalized Marketing Strategies,” in 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 2024, pp. 1–6.
T. Sangsawang, “Predicting Ad Click-Through Rates in Digital Marketing with Support Vector Machines,” J. Digit. Mark. Digit. Curr., vol. 1, no. 3, pp. 225–246, 2024.
N. S. Abd, O. S. Atiyah, M. T. Ahmed, and A. Bakhit, “Digital Marketing Data Classification by Using ML Algorithms,” Iraqi J. Electr. Electron. Eng., vol. 20, no. 1, 2024.
G. Gürsoy, A. Varol, and S. Varol, “Impact of ML in Digital Marketing Applications,” in 2022 3rd International Informatics and Software Engineering Conference (IISEC), 2022, pp. 1–6.
G. Paliouras, C. Papatheodorou, V. Karkaletsis, and C. D. Spyropoulos, “Discovering user communities on the Internet using unsupervised ML techniques,” Interact. Comput., vol. 14, no. 6, pp. 761–791, 2002.
K. S. Kyaw, P. Tepsongkroh, C. Thongkamkaew, and F. Sasha, “Business intelligent framework using sentiment analysis for smart digital marketing in the E-commerce era,” Asia Soc. Issues, vol. 16, no. 3, pp. e252965–e252965, 2023.
M. bin A. Lasi, A. B. bin A. Hamid, A. H. bin Jantan, S. B. Goyal, and N. N. binti Tarmidzi, “Improving Digital Marketing Using Sentiment Analysis with Deep LSTM,” in International Conference on Data Analytics & Management, 2023, pp. 217–232.
H. J. Alantari, I. S. Currim, Y. Deng, and S. Singh, “An empirical comparison of ML methods for text-based sentiment analysis of online consumer reviews,” Int. J. Res. Mark., vol. 39, no. 1, pp. 1–19, 2022.
I. Portugal, P. Alencar, and D. Cowan, “The use of ML algorithms in recommender systems: A systematic review,” Expert Syst. Appl., vol. 97, pp. 205–227, 2018.
B. Thomas and A. K. John, “ML techniques for recommender systems–a comparative case analysis,” in IOP Conference Series: Materials Science and Engineering, 2021, vol. 1085, no. 1, p. 12011.
S. Gutnik, “Application of data mining and ML methods to enhance the effectiveness of digital marketing strategies,” Digit. Strateg. a Glob. Mark. Navig. Fourth Ind. Revolut., pp. 131–144, 2021.
B. Muniandi, A. Garg, and E. Howard, “Adaptive Content Recommendation Systems for Digital Marketing Platforms: A Deep Learning Approach.,” Nat. Camp., vol. 28, no. 1, pp. 1888–1896, 2024.
M. Selimovic, A. Abd Almisreb, and S. Amanzholova, “The Role of Sentiment Analysis in Brand Management and Marketing: A Comparative Study,” Procedia Comput. Sci., vol. 251, pp. 579–584, 2024.
I. H. Sarker, “ML: Algorithms, real-world applications and research directions,” SN Comput. Sci., vol. 2, no. 3, p. 160, 2021.
A. De Mauro, A. Sestino, and A. Bacconi, “ML and artificial intelligence use in marketing: a general taxonomy,” Ital. J. Mark., vol. 2022, no. 4, pp. 439–457, 2022.
M. Blomster and T. Koivumäki, “Exploring the resources, competencies, and capabilities needed for successful ML projects in digital marketing,” Inf. Syst. E-bus. Manag., vol. 20, no. 1, pp. 123–169, 2022.
S. Malik, A. Rana, and M. Bansal, “Analysis of Current Recommendation Techniques and Evaluation Metrics to Design an Improved Book Recommendation System,” in Electronic Systems and Intelligent Computing: Proceedings of ESIC 2021, Springer, 2022, pp. 507–524.
S. Malik and M. Bansal, “Recommendation system: Techniques and issues,” Int. J. Recent Technol. Eng, vol. 8, no. 3, pp. 2821–2824, 2019.
T. K. Vashishth, V. Sharma, K. K. Sharma, B. Kumar, S. Chaudhary, and R. Panwar, “Embracing AI and ML for the Future of Digital Marketing,” in AI, Blockchain, and Metaverse in Hospitality and Tourism Industry 4.0, Chapman and Hall/CRC, pp. 90–117.