Artificial Intelligence driven Deep Learning for Competitive Intelligence to enhance Market Analysis and Strategic Positioning

This research provides insight into the successful application of modern deep learning techniques such as Transfer Learning, BERT, and Autoencoders to develop a powerful tool to sustain market analysis and strategic positioning in competitive intelligence. The conducted analysis proved that BERT was the most effective technique, as it produced the highest results in regards of accuracy, precision, recall, and F1 score values in all ten trials. The results of the experiment showed that with accuracy ranging from 90.5% to 92.3%, precision from 91.2% to 93.0%, recall from 89.7% to 91.7%, and F1 score from 90.4% to 92.2%, BERT proved to be the best technique for extracting insights from various textual data sources. In and of itself, Transfer Learning also yielded promising results, and its accuracy ranged from 85.2% to 88.6%, precision from 86.5% to 89.9%, recall from 83.7% to 87.3%, and F1 score from 85.0% to 88.5%. Additionally, given its ability to adapt to particular tasks with quite limited labeled data, Transfer Learning is a valuable tool for market analysis. In this experiment for Autoencoders, it has been shown that they provided a slightly lower performance than BERT and Transfer Learning. The reconstruction error of the deep learning method used in this experiment ranged from 0.018 to 0.025, while accuracy varied from 87.9% to 90.3%. Nevertheless, further optimization can make autoencoders a promising approach for market analysis. Thus, this research indicates the vital importance of leveraging advanced deep learning techniques for gaining a competitive advantage in the present-day business environment.