From Customer Voices to Intelligent Decisions A Systematic Review of DL and NLP in Digital Marketing

Digital marketing's evolution is increasingly shaped by data-driven decision-making, a trend fueled by the proliferation of customer-generated data across digital platforms. A significant hurdle is the expansion of unstructured voice-of-customer data, encompassing reviews, social media content, chat interactions, and voice communications. Although these data sources provide valuable perspectives on customer behavior and attitudes, their inherent complexity and contextual nuances constrain the efficacy of conventional machine learning approaches, which often depend on manual feature engineering and superficial text representations. This systematic review investigates the application of deep learning-based natural language processing (NLP) techniques within the digital marketing domain. Its central objective is to illustrate how sophisticated models can convert unstructured customer data into practical marketing intelligence. Core architectures, encompassing convolutional and recurrent neural networks, attention mechanisms, and transformer-based models, are correlated with significant marketing applications, including sentiment analysis, customer behavior prediction, brand monitoring, conversational marketing, and personalized content delivery. Research reveals that language models trained on context, such BERT and GPT, perform better than traditional methods in understanding sentiment, intent, and meaning. In the realm of next-generation digital marketing systems, deep learning-based natural language processing is rapidly emerging as a crucial element. Although these models offer scalable analytical capabilities, thereby enhancing the precision and velocity of decision-making processes, several substantial challenges persist. These encompass apprehensions regarding data privacy, the potential for algorithmic bias, limitations in interpretability, substantial computational demands, and the complexities inherent in transferring models across diverse domains.

کلیدواژه ها:

NLP ، Digital marketing ، Deep learning ، Decision-making ، Techniques ، Trends