(PDF) Deep learning for insight
Deep learning for insight-based analytics to improv e shopping experience Bouslimi Riadh Higher School of Digital Economics, Manouba University, Tunisia
2 Object recognition based on deep learning to locate and classify objects based on a predefined list of categories. In the context of store design, the targeted objects
Deep learning for insight
The accurate and efficient detection of shoppers and classification by age group, gender, and cart or trolley in supermarkets is essential for strategic retail planning. With the advent of deep learning algorithms, various models based on Convolutional Neural Networks (CNNs) have been proposed for object detection in high-resolution spatial images.
The accurate and efficient detection of shoppers and classification by age group, gender, and cart or trolley in supermarkets is essential for strategic retail planning. With the advent of deep learning algorithms, various models based on Convolutional Neural Networks (CNNs) have been proposed for object detection in high-resolution spatial images. In this article, we proposed an architecture ...
Redefining Retail
Dynamic pricing models powered by deep learning further personalize retail. By adjusting prices based on demand, competitor analysis, and purchasing trends, retailers maximize profits and improve customer satisfaction. This adaptive approach aligns consumer expectations with business objectives, highlighting AI's transformative potential in ...
Second, through a case study of FedEx,1 we demonstrate how ML/NLP- based analytics can be used to understand critical marketing constructs like customers' brand preferences and emotional responses (Bass and Talarzyk, 1972; DelVecchio et al., 2006), thus raising pertinent theoretical insight and practical implications.
AI in Retail
10 Areas to Implement AI in Retail Companies 1. Personalized Shopping Experiences Retailers leverage deep learning models and machine learning algorithms to gain more insight into specific consumer tastes and behaviors. These technologies analyze social media activity, purchase patterns, browsing history, and more.
Retail has entered a new phase of intelligent automation and advanced analytics . Traditional retail strategies depended on rule- based forecasting and intuition driven by limited datasets. Today, the scale and variety of retail data from point of sale systems and inventory records to customer browsing behavior and social sentiment require more advanced analytical techniques. Deep learning has ...
Key Details About Deep Learning For Insight Based Analytics To Improve Shopping
Recent developments in artificial intelligence are profoundly reshaping the frameworks and approaches utilized in e-commerce. AI technologies, especially those that leverage deep learning , natural language processing, and real-time behavioral analytics , are becoming increasingly essential for delivering customized services.
This survey provides a comprehensive and decision-oriented review of recent advances in Deep Learning for e-commerce, covering consumer behavior prediction, demand forecasting, recommendation systems, sentiment and review intelligence, catalogue understanding, fraud detection, cybersecurity, and large-scale operational optimization.