# Personalized Advertising Design Based on Automatic Analysis of an Individual's Appearance
## 7 Conclusions  
This research contributed to the development of an innovative deep learning model capable of predicting individuals' dress styles based on full-body images. The model was integrated into a personalized ad generator that created unique and engaging advertisements tailored to each user's style preferences. The research introduced a novel set of rules that establish a connection between predicted styles and well-known theories of color, geometry, and typography in advertising. The key contributions of this research are as follows:  
* **Deep learning model for style prediction**: The research introduced a novel deep learning model that achieved a high accuracy of 98.27% in predicting dress styles based on full-body images. This model surpassed traditional methods of market segmentation, which often rely on less granular demographic data. By leveraging advanced deep learning techniques, this model can enable businesses to understand their customers' style preferences more accurately and effectively;
* **Personalized ad generator**: The integration of the deep learning model into a personalized ad generator offers a groundbreaking approach to advertising. By generating custom ads based on each user's predicted style, businesses can deliver highly relevant and engaging content, increasing the likelihood of positive customer responses and conversions;
* **Automated market segmentation**: The research showcased the automation of market segmentation by using the predicted dress styles as a segmentation variable. This automation can streamline the process of tailoring marketing campaigns to specific customer segments, saving time and resources for businesses while enhancing the precision of their targeting efforts;
* **Data-driven insights for marketing strategies**: By utilizing data to estimate consumer preferences and style traits, the research demonstrated the power of data-driven insights in enhancing marketing strategies. Businesses could make more informed decisions by understanding their customers' psychological traits through observable appearance components, leading to improved customer engagement and satisfaction. The results of this research have significant implications for both researchers and practitioners in the fields of marketing and machine learning. For researchers:  
* The deep learning model and its high accuracy in predicting dress styles can contribute to the advancement of machine learning techniques in the domain of fashion and style analysis;* The study provides a foundation for further research in automating market segmentation and generating personalized marketing content using advanced deep learning methodologies; For practitioners:
* The personalized ad generator offers a valuable tool for businesses seeking to optimize their marketing efforts and create targeted campaigns that resonate with individual customers;
* The research demonstrates the potential of data-driven insights in enhancing marketing strategies, encouraging businesses to adopt more personalized and effective approaches to advertising. The results obtained in this study surpass those of the most similar work in the literature, highlighting the effectiveness and potential of the proposed methodology. Although direct comparisons may not have been possible due to variations in specific tasks, the similarities in the tasks and solution approaches allowed for a fair assessment of the advancements achieved. The automation of apparent style assessment and the generation of personalized ads have significant implications for various fields. Industries such as fashion, retail, marketing, and advertising can leverage the proposed methodology to enhance human resources management, improve security systems, deliver personalized customer service, and produce intelligent recommendation systems. Conceptualization and methodology, M.A.M.-A., H.C., J.F. and C.A.D. ; investigation and resources, M.A.M.-A., H.C., J.F. and C.A.D. ; software, visualization, and data curation, J.F. and C.A.D. ; validation, M.A.M.-A., H.C. and C.A.D.