The Value of Data Analytics in Fashion eCommerce
The fashion industry is easily one of the biggest players in global eCommerce. Although this giant sector has had its fair share of setbacks following the effects of the pandemic, experts are expecting a stellar recovery in the coming years.
From a decline of $532.25 billion to $485.62 billion in 2019 to 2020, the worldwide fashion eCommerce market is expected to bounce back in 2023 to the tune of $672.71 billion. In the digital era, fashion and eCommerce will continue to grow and evolve. This will lead to mountains of useful data, from which the industry can use as actionable insights through data analytics.
Forecasting Consumer Demand and Behavior
Analyzing the growing amount of business data in eCommerce-based fashion can pave the way for retailers to forecast demand, see crucial sales patterns, and even predict future consumer behavior. As a leader in the modern field of big data, global data analytics firm Quantzig notes how these effects are already shaping the growth of fashion in the future. After all, fashion is all about trends. And through the application of data analytics, big brands and other players in the industry have a firm understanding of the tastes and preferences of the masses. In turn, this can give them a competitive edge in terms of bringing the best products to the market and staying ahead of the latest trends. Furthermore, applying data analytics can also help fashion houses in terms of price optimization as well as analyzing consumer preferences, which brings us to our next point.
Personalizing Recommendations
The elements and factors that influence the preferences of eCommerce fashion consumers can be broken down into different data points. From colors and patterns to specific brands and aesthetic categories, all of these elements can contribute to how data analytics can greatly improve personalized recommendations. This is how the global fashion retail platform Fartech uses brand affinity to optimize their luxury fashion recommendations to its users. Aided by data scientists, online fashion retailers can parcel eCommerce data related to aesthetic groups, brands, colors, patterns, textures, and even specific garment preferences. This means that your most recent shopping decisions, have no doubt already been influenced by analytics-enabled algorithms aimed at optimizing eCommerce recommendations.
The Tech Experts Behind the Data Analytics Revolution in Fashion eCommerce
One of the biggest effects of the big data revolution is the spike in demand for data scientists as well as experts in IT security. As the annual amount of data created around the world is expected to reach 180 trillion gigabytes in 2025, those pursuing careers in data analytics can look forward to being in high demand as big data engineers, digital marketers, and market/operations research analysts will all be widely sought after by big eCommerce brands. These data specialists will be increasingly vital to how online fashion eCommerce evolve over the coming years. In addition, these data scientists will also help retailers in avoiding “bad data” or faulty/useless insights that can occur from improper data processing.
Meanwhile, experts training for careers in cybersecurity will likewise also be in high demand as security analysts, ethical hackers, and digital defense strategists. And this won’t just be true for the online fashion industry – it’ll be the case for different business and government sectors as well. The bigger the data being handled by data analysts, the higher the security risks, which is why these two tech specializations go hand-in-hand. And as more global fashion players turn to eCommerce, both IT security and data analytics experts will become even more crucial to fashion’s rapid digital transformation.
To learn more about how analytics can help your business make better decisions and see how Infor Birst cloud business intelligence and analytics platform can be integrated with your fashion operations, get in touch with our business intelligence consultants.
Rian Jamieson
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