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Fashion has always been at the forefront of technology, from the discovery of the sewing machine to the rise of e-commerce.
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Applied Technology Review | Thursday, December 22, 2022
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Data contributes a lot to the fashion industry by helping brands better understand customer behavior so that they can plan out their campaigns well that in turn will increase sales and will help them to retain customers.
FREMONT, CA: Fashion has always been at the forefront of technology, from the discovery of the sewing machine to the rise of e-commerce. As technology is cyclical and progressive, so is fashion. More than ever before, fashion technology is evolving rapidly.
Technology automates, individualizes, and boosts the fashion industry in numerous ways. For example, AI algorithms may be able to predict design trends, and robots may be able to stitch and cut fabric.
Data Influencing Fashion Industry: In the fashion industry, data trends are crucial and are used to drive strategy and decisions that increase sales, improve consumer comprehension, and increase profits. Fashion businesses and designers use data every day to operate successfully. The top fashion retailers today use a variety of analytical technologies, such as:
Fashion Industry Data Utilization- Fluctuations in Seasonality: The fashion industry often struggles to respond quickly to seasonal changes and unforeseen opportunities. By using web scraping and data collection from retail websites, fashion retailers can gain a competitive advantage and boost sales. Using real-time analytics, retailers can accommodate shifting customer preferences and shorten seasonal cycles. By doing so, fashion retailers can prevent losses and eliminate unexpected changes in consumer demand. By embracing analytics, fashion retailers can enhance the responsiveness of their supply chains. Utilizing modern analytics tools to generate insights and optimize the 5Ps (product, promotion, pricing, placement, and people) can improve in-season control.
Analyses of products: Neither every fashion website nor every e-commerce website offers everything under the sun to its customers. Gathering product information has become a challenge for many of these specialized businesses. Manually collecting product data for thousands of products can be difficult, time-consuming, expensive, and error-prone. Due to this, many smaller retail partners who specialize in a particular market use the e-commerce Product Data API to ensure their product pages appear authentic and attract more customers.
Audience targeting: Retailers have long collected digital impressions of various client demographics. Customers may be divided into groups based on their age, gender, ethnicity, location, and other factors. There are many benefits to using this marketing tactic. The situation is advanced by big data. A number of variables can be used to analyze customer behavior. In addition to purchasing periods and touchpoint analysis, social media sentiment can be used to track consumer engagement.
Trend analysis: Fashion trends change faster. Businesses in the fashion sector can track market trends and identify their target demographic using big data analytics methods like sentiment analysis on social media. Additionally, data analytics can be used to investigate how seasonal trends affect consumer behavior.