Leveraging data mining technologies for enhanced cloud-based marketing effectiveness

Abstract In this paper, the focus will be on the application of data mining (DM) technologies to enhance cloud-based marketing (CBM) strategies with an emphasis on customer segmentation, predictive modeling, and advertising management. A quantitative survey was conducted with 200 participants, including marketing managers, data scientists and cloud computing experts to find out the effect of DM tools such as Python, Microsoft Azure Machine Learning, Google Cloud Platform and IBM SPSS Modeler on CBM practices. The results show that 70% of the respondents agreed that DM enhances CBM strategies with specific benefits in consumer behavior prediction (75%), advertising effectiveness (65%) and customer engagement (60%). However, there are several challenges, which include data privacy concerns (60%), a shortage of skilled personnel (55%), and data integration issues (50%) The inferential statistical analysis shows that there is a moderate positive correlation between the level of DM usage and marketing performance ( r = 0.57), which means that organizations that are more adept at using DM tools are likely to have better marketing results. The p value of less than 0.001 ( p < 0.001) means that this relationship is statistically significant and the probability of the results being due to chance is low. The regression analysis shows that 32% of the variation in CBM effectiveness can be attributed to the variation in the independent variables considered. To overcome these challenges, the study recommends measures such as developing strong data governance mechanisms, improving the training programs for employees and using sophisticated DM techniques like clustering and predictive analysis. The findings of the study can be used to help organizations improve their CBM strategies and form a basis for further investigations of the integration of artificial intelligence in cloud-based marketing.