How Do Fashion Brands Utilize Data Science for Trend Forecasting and Consumer Insights.docx

1. How Do Fashion Brands Utilize Data Science for Trend Forecasting and Consumer Insights? Fashion industry transformed by data science. Fashion brands rely on data to stay ahead of trends and understand consumer preferences. Fashion brands use data science for trend forecasting and consumer insights. Fashion industry driven by big personalities and stars who create trends based on instinct and willpower. Industry must prioritize art over science. Fashion analytics enhances the fashion industry by combining art with data-driven science. Data on fashion sales, styles, and trends can be gathered through the use of apps according to fashion analytics. For the purpose of making more informed decisions regarding collections, inventory, distribution channels, and promotions, fashion analytics provides businesses with the ability to analyze past performance and forecast future outcomes. What Is Data Analytics? The process of drawing conclusions from datasets is known as data analytics. Analytics now uses AI to search data for insights without human guidance. In order to make educated decisions, businesses utilize data analytics, which involves the examination of large datasets in search of patterns and correlations. Through the use of data analytics, researchers and scientists are able to put scientific models and theories to the test. Data analytics in fashion helps retailers understand shopper behavior, improve customer experience, personalize promotions, and forecast demand accurately. Fashion Analysis and Analytics Fashion analysis is used by people in the fashion industry to analyze trends, customers, inventory, and future sales. Fashion analytics includes systems, processes, strategies, tactics, and technology for fashion analysis. Fashion analytics now uses AI and ML to do more than just report on the past. Retail artificial intelligence and machine learning assist businesses in better comprehending rapidly shifting fashion trends. Fashion analytics helps retailers understand what items are selling and provides guidance on customer behaviors. This information helps retailers make important decisions like setting prices, offering related goods, and stocking the right products each season. Instead of relying on their gut instincts, retailers can use fashion analytics to make decisions about styles, colors, sizes, and inventory based on data rather than relying on themselves. According to Greg Flinn, a former executive at Neiman Marcus, it brings science into the discussion of art.

2. Fashion Analytics Explained Fashion analytics uses data from various sources like online shopping carts, loyalty programs, POS software systems, inventory applications, marketing campaigns, consumer data sources, and store surveys to inform business decisions. Retailers of fashion use analytics to evaluate performance, gain an understanding of customer preferences, identify trends, and make recommendations for the next steps to take. These companies use fashion analytics to make decisions in the future, such as reordering inventory, as well as decisions in the present, such as encouraging customers to make purchases during their online shopping experience. Why is fashion analytics important? Retail businesses can increase their sales and profits with the help of fashion analytics by better understanding the desires and behaviors of their customers. It moves at a breakneck pace. Trends change quickly, leaving retailers with unsold items they have to heavily discount. It is necessary for retail analysts to obtain concise insights from data in order to monitor the performance of the business, determine the preferences of customers, and make decisions regarding stock and pricing. Customers and production systems are the sources of the data that they collect. Online shopping carts, loyalty programs, point-of-sale systems, marketing campaigns, and in-store surveys are all examples of potential sources of customer data. The manufacturing, inventory, warehouse, shipping, and financial systems are the most important production systems. Fashion analytics helps retailers assess past performance, predict future behaviors, and suggest actions. Retailers' inventory decisions for important shopping seasons and real-time online offers greatly impact their success and survival. Analytics is crucial for fashion due to short-lived trends and fluid consumer tastes. Fashion analytics helps retailers improve their chances of following trends and fixing mistakes quickly. Four Types of Fashion Analytics There are four types of fashion data analytics: descriptive, diagnostic, predictive, and prescriptive. The use of a variety of analytical approaches enables retailers to better understand the behavior of their customers, enhance the customer experience, better manage inventory, and personalize promotions based on the preferences of existing customers. Here is information regarding the four different methods of analysis. 1. Tools for reporting and business intelligence need descriptive analytics to work. You can use it to find out "how many, when, where, and what." This anaanalyticsts the foundation for more advanced analytics. 2. Diagnostic analytics tells you what went wrong and why it happened. Alerts and query and drill down processes are the two methods that are utilized in diagnostic

3. analytics. By using the query and drill down technique, more report detail can be extracted. A manager might want to know why a sales rep closed fewer deals one month. A drill-down could reveal that she took a two-week vacation and worked fewer days. The alerts provide users with advance notice of any potential problems. The app can send a warning to managers or analysts about employees having fewer hours, which may lead to fewer closed deals. 3. Predictive analytics aids retailers in foreseeing future events. This includes what-if analysis, such as testing different discounts or predicting stock depletion. 4. Prescriptive analytics combines AI and big data to determine actions based on predictive analytics. This type of analytics builds on the previous three. Prescriptive analytics uses AI and machine learning to suggest actions for desired results. Because prescriptive analytics may recommend a targeted discount offer to specific customers based on their purchase history or cross-selling, it is possible that analytics will predict that there will be an excess of sweaters. Consumer Insights through Data Science Design & Ideation Designing a black tank top fashion collection requires research and analysis to stay current with trends. Intuition used to be important in design decisions, but now data analytics is more important. Fast fashion brands like Shein, Zara, and Fashion Nova use social media data, consumer purchase data, and trend forecasting to boost profits and meet consumer demands. Brands use analytics to make data-driven decisions on designs and production, reducing waste and increasing profitability. Data analytics can help designers meet the growing demand for sustainable fashion by using eco-friendly materials and practices. Line Planning Line planning is important in fashion, and data analytics is very helpful. When manufacturers measure the reactions of their customers, they are able to eliminate designs that are not popular and carefully examine every aspect of their products. Data analytics provides manufacturers with the ability to effectively manage stock levels, which in turn helps them avoid lost sales and costly markdowns. Planning with spreadsheets is easy for a few lines, but difficult for hundreds. Product pricing can be optimized with the help of data analytics by taking into consideration costs such as materials, profit margins, transportation, and overhead requirements. Data analytics helps manufacturers identify popular colors and styles, increasing sales. Sourcing The fashion industry compiles information on various suppliers in order to identify the most suitable ones. Analyzing data can be difficult and time-consuming. Data analytics helps fashion businesses make informed decisions about suppliers by analyzing data points faster and more accurately. Sustainability is important for all fashion brands. Use sustainable and ethical materials to become a more sustainable brand. An evaluation of a vendor's compliance with metrics and parameters can be performed using data analytics.

4. Businesses in the fashion industry can use data analytics to locate sustainable suppliers, thereby lowering their impact on the environment and enhancing their brand image. Production It is possible to effectively communicate production updates to stakeholders through the use of data visualization. Improving the production process can be accomplished by providing employees with reports and dashboards. It helps supervisors spot issues like low productivity, missed targets, and equipment downtime early on to prevent bigger problems. Data visualization helps stakeholders identify areas for improvement in production performance. Understanding of the production process is enhanced through the use of data visualization, which also identifies areas that could use improvement. It assists in the identification of bottlenecks in production, allowing managers to more effectively allocate resources and reduce lead time. Inventory Management The management of inventory is improved by fashion companies through the use of data mining. In order to gain a better understanding of product performance, channel performance, and customer preferences, it is essential to conduct inventory data analysis using data analytics strategies. Insights determine inventory needs, optimize stock management, and reduce returns. Using data reduces errors and waste. Through the use of data analytics, businesses are able to identify inventory that is moving slowly, which enables them to take corrective actions such as markdowns or promotions. Quality Control Fashion quality is crucial. When it comes to the high-quality products they sell, luxury brands charge a premium price. Fashion brands use prescriptive analytics and AI for high quality. For the purpose of avoiding production downtime, supervisors are able to promptly address quality issues. Quality analytics helps optimize processes by analyzing equipment, defect counts, targets, and other factors. Through the use of analytics, a fashion brand can discover quality issues in garments while they are being manufactured, thereby saving both time and money. How Is Data Analytics Used in the Fashion Industry? Fashion analytics is crucial for fashion companies. It helps them understand consumer behavior, plan collections, manage inventory, forecast trends, target shoppers, and make data-driven decisions. Fashion analytics helps retailers and designers be more competitive, increase sales, improve profits, and enhance customer satisfaction through personalization. Fashion analytics is used in various areas. Collection planning and design are important for success in fashion. Fashion analytics helps brands create appealing collections by providing insights on the latest trends. Analytics guides buying decisions by providing fashion analysts with detailed insights on trends in colors, style, fit, and accessories. ●

5. Fashion analytics helps businesses track product performance and plan inventory. Effective stock management prevents out-of-stocks and excessive discounting. Fashion analytics helps businesses create collections that align with current trends and shoppers' preferences. Fashion analytics helps businesses target consumers effectively. Fashion businesses can use trend forecasting and consumer targeting to drive sales. Fashion analytics helps businesses forecast sales more accurately, leading to increased sales and reduced stock discounts. Forecasting is crucial as consumers have options and may leave retailers without desired items. 63% of consumers would switch brands instead of waiting for something to be back in stock, per Oracle research. Fashion analytics helps businesses make data-driven decisions by providing insights into customer behavior and preferences, leading to increased sales and profit. Fashion analytics helps businesses personalize collections and marketing messages for higher customer satisfaction and sales. ● ● ● ● ● ● What Is the Role of a Fashion Analyst? Fashion analysts provide accurate trend data and advise on purchasing, selling, and pricing fashion goods. They analyze a business's recent performance, experience, and competitor results. Analysts track social media data to understand trends. Fashion analysts combine and clean data from various sources to create valuable and relevant stories or trends for decision-makers. How to Get Started with Fashion Analytics Starting with descriptive analytics helps analysts understand business performance before moving on to predictive and prescriptive analytics. Here's a possible way to start with fashion analytics: ● Make sure you have reliable data from consistent sources. When combining weather data, standardize on Celsius or Fahrenheit. Don't sell snow boots to Canadians in July. ● Vet data sources, develop descriptive analytics reports for past reporting periods. ● Move onto next phase of diagnostic analytics to explain why something happened. ● Start with one subject area for predictive and prescriptive analytics, like pricing or inventory, instead of trying to do everything at once. Future of Fashion Analytics In the future, retailers will use prescriptive analytics to automate tasks currently done by people in fashion analytics. Automated alerts and response decisions enable quicker reactions to changes in demand. Businesses can measure impact faster, improving decision-making. Retailers can use different types of data, like local weather, to understand cause and effect and forecast demand more effectively. Retailers will use machine learning and AI techniques for analysis, allowing them to consider more factors and options.

6. Support Growth with Data-Driven Decision-Making Cloud platforms like Oracle's enable businesses to access advanced business applications, including AI-powered analytics tools. Oracle's fashion analytics uses AI and machine learning to optimize product offerings and pricing for customer satisfaction and business success. Global fashion retailers use Oracle Retail technology to streamline processes, launch new brands, and expand geographically. Retailers use Oracle Retail Demand Forecasting Cloud Service to improve forecast accuracy and save time for customer service. Retailers use Oracle Retail planning and optimization solutions to carry popular fashions and make pricing decisions to reduce markdowns. Fashion industry is highly competitive due to social media influencers, making it difficult for brands to predict trends, stand out, and retain customers. Fashion analytics tools are becoming better at spotting trends and helping retailers choose the right products. This will make it obvious which retailers are using these tools effectively to both shoppers and investors.