A study on evaluating the effectiveness of cross-border e-commerce digital marketing based on deep learning

Businesses looking to broaden their consumer base and access overseas markets are realizing the importance of cross-border e-commerce as the landscape of international trade changes. In an increasingly globalized e-commerce landscape, organizations are grappling with the challenge of tailoring digital marketing tactics to specific nations and client bases. Continued success in international e-commerce necessitates digital marketing strategies that can adjust to local tastes and market conditions. Combining deep learning with quantum computing, the study suggests Quantum Fusion Deep Learning for Cross-Border Digital Marketing Analytics (QFDL-CBDMA) to enhance the assessment of digital marketing campaigns. Quantum Fusion Neural Networks (QF-NNs) are an integral part of this approach; they use quantum computing to enhance deep learning models that deal with high-dimensional, multi-modal inputs. For better feature extraction and pattern recognition, the QF-NN encodes complicated features into quantum states. This allows for quicker and more effective analysis of data related to demographics, markets, and consumer behaviour. To further improve the model, the Quantum Gradient Descent (QGD) optimization method uses quantum computing to speed up training, leading to quicker convergence and better predictions. QF-DL-CBMS offers adaptive marketing methods that adjust to the ever-changing market, enhancing campaign targeting, consumer engagement, and budget allocation. International e-commerce dataset verification studies show that QF-DL-CBMS achieves a projected accuracy of over 98% and beats competing approaches in ROI, customer engagement, and cross-cultural consumer insights.