Fashion is all about innovation and discovery. To win the hearts of the public, an innovation must be supported by solid data. The fashion industry is constantly evolving, with various trends emerging in a short span of time. In this scenario, a clear understanding of the market and consumer likes and dislikes is essential for witnessing glory in your fashion endeavor. 

Data analytics is critical for recognizing customer buying patterns and projecting future demands while providing a competitive advantage to a business. From finding the right fabric to designing a promising marketing campaign, data is inevitable in every stage. Recent breakthroughs in fashion data analytics and intelligence enable users to create sustainable fashion with a clear understanding of what the future has in store for them. With the emergence of personalized fashion, now fashion experts are analyzing customer data to provide a personalized experience to their clients. 

How data analytics is reinventing the fashion segment?

Product intelligence

When a new fashion or product is introduced to the market, its acceptance is highly dependent on external factors. Earlier, companies used traditional methods such as focus group discussions and surveys, now they use predictive analytics to identify products that can create a mark in the industry. Fashion analytics help companies to understand market trends on a more granular level, which enables them to derive insights into why a customer is buying a product. 

Fashion leaders like Ralph Lauren and Sperry utilize predictive analytics to evaluate the acceptance of their products even before it reaches the market. They utilize a data-driven fashion approach to evaluate how fabric, color, design, and pricing impact the customer response. Majority of the companies utilize fashion data analytics and intelligence to identify what is working in the industry to develop products that can supersede the standard that product holds. Data-driven fashion and product development reduces sunk cost and enhances operational efficiency. 

Brand perception

In the fashion world, having a good brand name is quintessential for any industry player to thrive in the market. Companies can use fashion analytics and intelligence to learn how their customers view their brand. Customer reviews, feedback, and customer engagement in social media help brands to evaluate the performance of their offerings. Real-time data enables them to address any negative feedback or remarks quickly and effectively. 

Zara, a Spanish apparel retailer, use social media most productively. They utilize social media platforms to identify its target audience’s fashion sense and market demands. Zara tweets 52 times a day on average, with 98 percent of their tweets being responses. They reply to their customers as soon as possible and address any issues they have to build a loyal consumer base. They respond to consumer questions and assess how customers view their products.

Marketing campaigns

A good fashion statement itself is a good marketing strategy. But in a competitive industry like fashion, it’s not enough. Just like a personalized outfit or accessory, marketing in the fashion industry should be personalized according to your target audience. Here businesses should have a well-defined marketing strategy that should include the following factors, 

  • Well-defined target audience
  • Effective marketing channels
  • Personalized message
  • Special offers

H&M took the fashion game to the next level when they introduced inclusive marketing. Their ads featured women with hijabs and diverse people of varied sizes and backgrounds. H&M took use of behavioral data by evaluating customer purchase, return history and selling items to identify the core demographic for their advertisements. As a result of big data insights, they produce personalized content and engage with their customers.


Fashion can be categorized into affordable and high-end. No matter which category your product belongs to, you need a data-driven fashion approach to identify a market-friendly price for your product. A company can forecast the demand and increase the price accordingly. Dynamic pricing in fashion retail enables them to equate price with demand to tap on overnight market opportunities. 

Amazon fashion, a global fashion retailer extensively utilizes data to tag the right price for its products. They utilize data to identify and predict market demands to give the most appropriate price for their products. A trend-oriented industry like fashion, prices should be flexible enough to fluctuate according to the market changes. With the right use of data analytics and intelligence, Amazon identifies prices that give value for money to its clients with an efficient margin. 

Market trends

Data generated from social media campaigns are helping companies to identify what’s winning the market. Sentiment analysis helps companies to analyze trends in real-time to capitalize on existing market opportunities. Know what your customer wants and then position your product based on your customer preferences. 

With the growing need and human inclination towards environment protection in the recent past, the fashion industry is identifying new methods to give fashion a global name without harming the environment. Fashion apparel manufacturer, Afends identified this opportunity and they found environment friendly and sustainable fibers for their products. Since 2014, the firm has been developing corn starch-based packaging that is 100% biodegradable. 

At, we deliver the right insight on your need to level up your fashion business. Hit the market with the most innovative product using our data-driven solutions. Our advanced analytics solutions can solve your business challenges and provide a better view of the future to rule the fashion world. 


Gopika is a passionate writer, who expresses her views and opinions via writing. She is a budding comic writer who believes in giving her readers comfort by taking them out to a fictional world. Apart from writing, cooking and music take her to the Zen mode.

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