In this study, we investigate the strategic implications of using AI applications in consumer-oriented marketing and identify the critical AI technologies that drive content engagement. A survey questionnaire was administered to 421 marketing professionals and business owners in the Coimbatore region. Perception Analysis: The mean values for the three items (5-point Likert scale) on acceptance of AI tools were all above 3.9, indicating a high level of acceptance of AI tools. Personalization engines received the greatest support (Mean = 4.29; RII = 0.858), followed by recommendation systems and marketing automation tools, suggesting that respondents are interested in technologies that facilitate personalized communication and real-time customer interactions.

The sampling adequacy was considered satisfactory based on the KMO (0.842) and Bartlett's Test of Sphericity (p < 0.001). These results supported the factor extraction. The EFA identified three constructs of AI tools: consumer experience systems, insight-driven analytics tools, and automation-enabling tools. These clusters represent a marketing universe where personalization and predictive engagement drive action, orbiting around backend analytic intelligence and operational prowess.

Garrett Ranking Technique revealed that personalisation (30.40 percent) and recommendation systems (24.46 percent) are key AI roles. Chatbots and predictive analytics are also popular. Segmentation and sentiment analyses scored lower, indicating that they are less important, but their significance is increasing. Overall, the results show that AI drives customer-focused strategies with a clear shift toward hyper-personalization and automation. Businesses are advised to invest in AI tools that target personalization and recommendation systems.

A survey of 421 marketing professionals and small-business owners found that personalization was ranked as the most important AI feature (30.40 percent), followed by Recommendation Systems (24.46 percent), Chatbots & Virtual Assistants (13.31 percent), and Predictive Analysis (10.69 percent). Customer Segmentation (3.09 percent) and Sentiment Analysis (4.27 percent) were the least aggregating factors. These observations can help companies invest in the successful use of text analytics by avoiding failures in less prominent dimensions. The results validate that AI personalization and recommendation technologies are important for consumer experience and marketing performance in the digital era..