The Impact of Digital Marketing Campaign Strategies on Consumer Buying Intention

Consumer psychology and shopping motivation have been studied since ancient times, and their study has progressed with technological advancements.Today's companies can only imagine operating with digital marketing to effectively reach a broad audience and get valuable insights about customer behavior.Digital marketing campaigns are an excellent tool.However, conventional methods of assessing shoppers' propensity have limitations regarding accuracy and breadth of coverage.Aiming to overcome the drawbacks of existing methods, this paper offers a Machine Learning-based Consumer Buying Intention Analysis Method (ML-CBIAM).ML-CBIAM adopts machine learning algorithms to consumer data from online advertising efforts.It provides a more accurate and comprehensive understanding of customer habits and preferences by the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS).The simulated results show that the suggested technique for ML-CBIAM surpasses state-of-the-art methods in terms of accuracy and coverage.Furthermore, results from a small sample of simulations demonstrate that the ML-CBIAM can correctly forecast customer purchase intent for various digital marketing campaign techniques.All indexes of ML-CBIAM are better than those of Fuzzy, SVM, PCA and LDA.On average, ML-CBIAM has a click-through rate of 13.7%, a conversion rate of 3.3%, and a return on spending of 19.4%.Using ML-CBIAM, companies boost the success of their marketing initiatives, increase their profits, and enrich their connections with customers.