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Top1. Introduction
In today’s information age, with the exponential growth in the scale and complexity of consumer data, businesses need a better understanding of consumer behavior and demand to formulate accurate marketing strategies. Traditional statistical methods and machine learning Alon et al. (2001) approaches may sometimes fail to capture the features and patterns present in the data adequately. Deep learning methods, with their powerful model representation and automatic feature learning capabilities, have emerged as effective tools to address these challenges.
The ResNet-50-GRU transfer learning method offers significant advantages and innovations compared to traditional methods. Firstly, traditional methods have limitations in handling large-scale and complex consumer data. With the exponential growth in consumer data in terms of scale and complexity, traditional statistical and machine-learning methods may fail to capture the features and patterns in the data adequately. This leads to an incomplete and inaccurate understanding of consumer behavior and demands.
In contrast, the ResNet-50-GRU transfer learning method utilizes the powerful model representation and automatic feature learning capabilities of deep learning algorithms to analyze and extract meaningful patterns and features from a large amount of consumer data. The ResNet-50 model, as a convolutional neural network, can automatically learn and extract visual features from image data, such as product preferences and brand preferences. This ability enables marketing decision-makers to gain more accurate insights into consumer preferences and tailor marketing strategies accordingly.
This study aims to leverage deep learning methods to construct a marketing decision model and predict consumer behavior. Specifically, we will utilize the ResNet-50 model to analyze consumer data containing images or visual content and extract visual features and patterns relevant to marketing decisions. Simultaneously, we will employ the GRU (Gated Recurrent Unit) model to model the temporal dynamics of consumer behavior, capturing time-related features such as purchase sequences and browsing histories. Furthermore, we will apply transfer learning techniques to leverage the knowledge from pre-trained models and build accurate models on limited data. We will collect and preprocess relevant data, including demographic information, browsing behavior, purchase history, and potential visual content of consumers. Next, we will train and evaluate deep learning models such as ResNet-50 and GRU using appropriate loss functions and optimization techniques. In the context of transfer learning, we will initialize the model with pre-trained weights and fine-tune it on specific market datasets.
Through experiments on real-world datasets, we will validate the effectiveness and accuracy of the proposed approach. Using the ResNet-50 model, we will successfully extract visual features relevant to marketing decisions, such as product preferences and brand preferences. Simultaneously, the GRU model will capture the temporal dynamics of consumer behavior and accurately predict future purchasing behavior and interests. The application of transfer learning will further enhance the model’s performance, particularly in scenarios with limited data.
This research holds significant implications for marketing decision-making. Through deep learning methods, businesses can gain better insights into consumer behavior and demands, enabling them to formulate personalized marketing strategies and improve the effectiveness of product promotions and sales. Additionally, this study provides novel ideas and approaches for academic research in the field of marketing.
In the field of marketing, several deep learning or machine learning models are widely applied. Here are five common models along with their advantages and disadvantages: