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- 1. DATA ANALYTICS A N D " R PRESENTED BY: ANWESHA KALITA BIPASHA NAYAK RECENT ADVANCEMENT IN FASHION STUDIES USING R PROGRAMMING
- 2. In preparation of our assignment, we had to take the help and guidance of a few respected sources, who deserve our deepest gratitude. As the completion of this assignment gave us much pleasure, we would like to show our gratitude towards. Prof TVSN Murthy , Data Analysis & R Instructor, National Institute of Fashion Technology, Hyderabad, who, after numerous consultations, guided us well on this opportunistic assignment. In addition, we would also like to thank him for introducing us to the methodology of work, and whose passion for the "underlying structures" had lasting effect. We would also like to thank our parents for motivating us with the assignment. Many people, especially our classmates have made valuable comments on our assignment which inspired us to improve the overall quality of it. ACKNOWLEDGEMENT
- 3. CONTENT 01 What is R programming? 02 03 04 05 07 08 Advancement of R programming in Fashion industry. Sales forecasting. Sustainability analysis. Textile design. 06 Image analysis. Visual Merchandising. Consumer behaviour. Analyze customer sentiment. 10 Fashion Fingerprints. 11 Naive Bayesian Network (NBN). 12 Conclusion. 09
- 4. WHAT IS R PROGRAMMING? R programming is a popular open-source programming language and software environment for statistical computing and graphics. It is widely used for data analysis, machine learning, and statistical modeling in a variety of fields, including finance, healthcare, and social sciences. It provides a wide range of tools for data manipulation, visualization, and statistical analysis, making it a powerful tool for exploring and interpreting large datasets.
- 5. ADVANCEMENT OF R PROGRAMMING IN FASHION INDUSTRY R programming can analyze consumer behavior and preferences, such as sales data, online search trends, and social media activity. R programming can also be used in textile design to create and analyze complex patterns, generate 3D designs, and simulate fabric draping and movement. R programming has also optimized visual merchandising strategies by analyzing sales data and identifying which products sell best in which locations. Overall, the advancements in R programming have enabled fashion companies to make data-driven decisions, create more innovative designs, making it an invaluable tool in the fashion industry.
- 6. SALES FORECASTING Sales forecasting is a critical aspect of supply chain management, as it enables fashion companies to make informed decisions about inventory management, production planning, and marketing strategies. One common method for sales forecasting using R programming is time series analysis. Time series analysis involves analyzing historical sales data to identify trends, seasonality, and other patterns that can be used to predict future sales. Another method for sales forecasting using R programming is machine learning. Machine learning algorithms can be trained on historical sales data to identify patterns and make predictions about future sales.
- 7. Store Layout Optimization: R programming can be used to analyze customer movement patterns within a store and optimize the layout for maximum sales. This analysis can help retailers to create more effective product displays and optimize inventory levels. Customer Segmentation: By analyzing customer data (their demographics, purchase history, and behavior), retailers can create targeted visual merchandising strategies. Predictive Analytics: R programming can be used to predict future trends in fashion and consumer behavior. Retailers can use this information to create more effective visual merchandising strategies that anticipate future trends and stay ahead of the competition. VISUAL MERCHANDISING
- 8. Many fashion brands have started using R programming to analyze their data and gain insights into consumer behavior, design trends, and social media influence. CONSUMER BEHAVIOUR Zara has implemented R programming to improve its inventory management and sales forecasting. The brand uses a custom R-based system called InStock that analyzes sales data and predicts demand for each product. The InStock system uses a combination of regression analysis, time-series forecasting, and machine learning algorithms to analyze data from Zara's point-of-sale system and other sources. The system is able to make accurate predictions about product demand, Customer buying choices and patterns, etc., allowing Zara to adjust its inventory levels and production schedules accordingly. By using R programming, Zara is able to reduce waste and improve its sustainability practices. Additionally, the brand is able to better serve its customers by ensuring that popular products are always in stock and available for purchase. ZARA
- 9. ANALYZE CUSTOMER SENTIMENT Levi's has implemented R programming to analyze customer sentiment and gain insights into customer preferences and needs. The company uses a custom R-based system called Levi's Insights Studio that analyzes customer feedback from various sources, including social media, customer surveys, and online reviews. Levi's Insights Studio uses a combination of sentiment analysis, natural language processing, and machine learning algorithms to analyze customer feedback and gain insights into customer sentiment, preferences, and behaviors. By using R programming to analyze customer sentiment, Levi's is able to gain valuable insights into customer preferences and needs. The company can use these insights to improve product design and development, develop more effective marketing campaigns, and better understand its customers' needs and preferences. LEVI'S:
- 10. NAIVE BAYESIAN NETWORK (NBN) Amazon's Echo Look is a personal styling device that uses NBN to make personalized fashion recommendations. The device analyzes a user's clothing choices and makes recommendations based on their style and preferences. Echo Look's algorithm considers factors like style, color preferences, body shape, and size to create a comprehensive picture of a user's fashion preferences. The use of NBN in fashion personal styling has enabled Amazon to offer a highly personalized shopping experience to its customers. Amazon's Echo Look:
- 11. TEXTILE DESIGN Pattern creation: R programming can be used to create complex patterns, such as repeating designs or intricate motifs. 3D design: R programming can be used to create 3D designs of textile repetitive prints, such as clothing or upholstery. Fabric simulation: R programming can be used to simulate how different fabrics will drape and move on the human body. This analysis can help designers select the most appropriate fabric for a particular product, or to identify areas where the fabric may need to be altered to achieve the desired effect. Dyeing and printing analysis: R programming can be used to analyze the impact of different dyeing and printing techniques on fabric properties, such as colorfastness or durability. Here are some ways that R programming can be used in textile design: 1. 2. 3. 4.
- 12. IMAGE ANALYSIS Color analysis: R programming can be used to extract color information from fashion images and analyze color trends over time. This analysis can be used to identify popular color palettes, analyze the impact of color trends on sales, and optimize inventory management. Texture analysis: R programming can be used to analyze the texture of fashion images, such as the weave of a fabric or the pattern on a shoe. Shape analysis: R programming can be used to analyze the shape of fashion images, such as the silhouette of a dress or the cut of a jacket. Product recommendation: R programming can be used to improve the accuracy of product recommendations by analyzing customer browsing and purchase behavior. R programming can be used in fashion image analysis in several ways: 1. 2. 3. 4.
- 13. Fashion fingerprints are a way of analyzing a person's fashion preferences and creating a unique profile based on their clothing choices. R programming can be used to build predictive models that can identify a person's fashion fingerprints and make personalized fashion recommendations. FASHION FINGERPRINTS Stitch Fix is an online fashion retailer that uses R programming and machine learning algorithms to create personalized fashion recommendations for its customers. The company's algorithm analyzes a customer's fashion preferences and creates a unique fashion fingerprint based on their clothing choices. Stitch Fix's algorithm takes into account various factors such as style, color preferences, body shape, and size to create a comprehensive picture of a customer's fashion preferences. By using R programming to create fashion fingerprints, Stitch Fix is able to offer a highly personalized shopping experience to its customers. The company's algorithm has helped it to build a loyal customer base and increase customer satisfaction by providing personalized fashion recommendations that are tailored to each customer's individual style and preferences. STITCH FIX
- 14. H&M using fashion fingerprints for product recommendation according to user's preferences and previous searches, likes and purchases.
- 15. SUSTAINABILITY ANALYSIS Environmental impact assessment: R programming can be used to conduct a life cycle assessment (LCA) of a product, which involves evaluating the environmental impact of its entire lifecycle. Energy analysis: R programming can be used to analyze the energy consumption associated with different production processes and identify opportunities to reduce energy use and associated greenhouse gas emissions. R programming can be used for sustainable analysis in several ways: 1. 2. 3. Supply chain analysis: R programming can be used to analyze the environmental impact of different stages of the fashion supply chain, such as transportation, manufacturing, and retail. This analysis can help fashion companies identify areas where improvements can be made to reduce their carbon footprint.
- 16. CONCLUSION In conclusion, the recent advancements in Fashion studies using R programming have brought about significant improvements in the fashion industry. R programming has enabled fashion companies to analyze customer behavior, conduct sales forecasting, perform sustainability analysis, analyze social media data, and create personalized fashion recommendations using machine learning algorithms. These advancements have helped fashion companies to make data- driven decisions, improve customer experience, reduce costs, and increase revenues.
- 19. THANK YOU