Artificial Intelligence in Marketing Communication and Customer Satisfaction

Sentiment analysis is a technique to study customer opinions related to customer satisfaction data generated from sources like feedback forms, social media reviews, and chatbots. This study suggested a powerful and effective technique that can process the large volumes of data and can specifically examine the positive, negative, and fake feedback of “AI-driven marketing communication,” which is considered a big challenge, as unstructured data related to the marketing sector is considered of high complexity. On the other hand, personalization efforts and automation-related challenges in AI-driven marketing communication have confused marketers, researchers, and teachers. An efficient machine learning approach should be used to gather more structured insights in order to identify behavioral patterns. Customer sentiment records (structured and unstructured) have gained significant attention worldwide for understanding the behaviors of diverse consumer groups. Analysis of the marketing communication sector still does not provide a clear picture of the information available in these datasets, especially if this unstructured and fake feedback affects the field of customer engagement. This study has proposed an advanced AI-based framework to eliminate inconsistencies in information, the optimization mechanism for a fusion of structured and unstructured data as parts where a fusion of sentiment analysis and machine learning with predictive analytics is applied for the task of customer feedback classification. Experiments show that our hybrid approach obtains a higher accuracy value of customer satisfaction predictions, compared with different baseline approaches, with various sizes of structured and unstructured datasets. Based on our knowledge, the proposed framework can learn from unified datasets to obtain good insights, better results than one that can be learned from isolated data sources.