In today’s transforming digital realm, we have been witnessing artificial intelligence (AI) taking over many industries, including media, banking, education, and health. One of the most prominent areas that AI has impacted is marketing.
With the emergence and proliferation of AI, practices in digital marketing have seen a shift from general strategies to more nuanced, data-driven solutions tailored to individuals. Additionally, AI-enabled solutions help companies to optimize a wide array of operations, from accurate predictions to generating tailored suggestions. These advancements enable marketers to improve customer journey, boost campaign performance, and maximize ROI, making it an efficient tool for marketers.
As AI continues to evolve, its applications in digital marketing are becoming more essential than ever. From customer relationship management (CRM) to predictive analytics, let’s explore how AI is revolutionizing key aspects of digital marketing.
Better CRM Practices
The usage of AI models has multidimensional advantages both for businesses and consumers. In the scope of customer relationships, companies take advantage of AI-CRM systems for various purposes ranging from customer acquisition to habit formation.
These systems are capable of predicting potential customers’ CLV—Customer Lifetime Value—allowing businesses to achieve selective acquisition in which “better” customers are identified and acquired. Subsequently to customer acquisition, CRM systems combined with AI technology improve the management of two important key aspects: customer development and retention. Putting it differently, it functions well not only in the strategies aimed at increasing revenue from current customers but also sustaining the relationship between the customer and the brand and fostering greater customer loyalty.
Furthermore, with the implications of AI in marketing, people can get more individualized outcomes specific to their needs based on their purchasing history and interactions. In that way, companies can create more personal and intimate dialogue with its consumers, developing strong relationships.
Besides from constructing and maintaining preexisting consumers habit, AI systems can be deployed to facilitate and eliminate former habits (Libai et al., 2021).
Sentiment Detection
After the emergence of digital media and social media platforms, the number of users has been increasing rapidly. People post content about a wide range of topics, providing information about their opinions and emotions. This embedded information is utilized to understand users’ sentiments, extracting insights and building necessary strategies by researchers, politicians, celebrities, and marketers to manage their popularity.
Emojis, particularly used in the digital realm, convey one’s thoughts and emotions. They have become an essential part of analysis alongside texts to do sentiment analysis and targeting in advertising campaigns. To improve the accuracy of sentiment detection, there are some machine learning tools like Universal Sentence Encoder (USE) and SBERT that are being employed to produce sentence embeddings. Marketing professionals widely adopt these models in their advertising strategies, strengthening the delivery of content and customer engagement (Velampalli et al., 2022).
Budget Allocation in Marketing
The incorporation of AI and machine learning models has made budget optimization in digital marketing practices more effective. Compared to traditional decision-making methods, AI-driven models improve the operation of advertising campaigns on a large scale, allowing quick and efficient budget adjustments. Machine learning models examine extensive data coming from marketing campaigns to specify which platform has the highest return on ad spend (ROAS). Particularly, multimodal algorithms enhance the efficiency of both campaigns and targeting while optimizing budgets by establishing a balance between exploration and exploitation. By ensuring better conversations with lower expenses, AI data analysis improves the campaigns’ performance in the long run. Advertisers might maximize their campaign profitability and customer engagement across digital marketing channels by using AI-driven analytics to inform their budget decisions (Gangopadhyay et al., 2025).
Takeaways
AI-enabled solutions have substantially altered digital marketing environments by empowering more tailored and data-driven campaigns. The performance of CRM systems is improved by AI technology, particularly in phases of customer acquisition, retention, and habit formation. AI-powered systems successfully detect Customer Lifetime Value (CLV), strengthening customer relationships. Moreover, sentiment analysis tools analyzing customer content in digital networks are benefited by marketing professionals to adjust their communication and engagement strategies. Machine learning models develop the accuracy of sentiment analysis, allowing effective targeted advertising. Additionally, AI optimizes budget allocation by analyzing large datasets to maximize return on ad spend (ROAS), making marketing campaigns more cost-effective. Overall, AI helps companies to improve customer satisfaction, increase engagement, and boost marketing effectiveness.
References
Gangopadhyay, B., Wang, Z., Chiappa, A. S., & Takamatsu, S. (2025). Adaptive Budget Optimization for Multichannel Advertising Using Combinatorial Bandits. arXiv preprint arXiv:2502.02920.
Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51(1), 44-56.
Velampalli, S., Muniyappa, C., & Saxena, A. (2025). Performance evaluation of sentiment analysis on text and emoji data using end-to-end, transfer learning, distributed and explainable ai models. arXiv preprint arXiv:2502.13278.
