Fashion trend forecasting may sound like a brand new concept brought to us by the wonders of technology, but in truth, it is almost as old as fashion itself. Brands, designers, and retailers have always sought to foresee the next big trend that will shape the industry in the upcoming seasons.
What makes all the difference nowadays is that the industry no longer depends solely on the verdict of a handful of leading designers. As is the case in many other aspects of our lives, a certain autonomy has been introduced to us through the widespread use of social media. The definition of the word “icon” witnessed a shift; fashion bloggers and influencers—who, perhaps, would not be professionally associated with the fashion industry otherwise—started to set new trends in a matter of minutes. The whole fashion scene became rather diverse, dynamic, and more unpredictable than ever—and that’s where trend forecasting with AI came to the rescue.
What’s Trend Forecasting?
In its wider sense, trend forecasting is the process of predicting consumers’ future buying habits based on research and data analysis. Fashion trend forecasting with AI, on the other hand, is the (much more specific) process of predicting trend dynamics through billions of interactions taking place online, powered by artificial intelligence and customized data stemming from deep-learning approaches.
In other words, through fashion AI; brands, retailers, buyers, and manufacturers get to predict the upcoming trends in the fashion scene—including colors, fabrics, textures, styles, patterns, prints, and much more—and make informed decisions accordingly, which, in turn, result in higher sales and bigger margins.
How Does Trend Forecasting Work?
The approaches and specific processes behind AI-powered trend forecasting platforms can vary depending on the company. At T-Fashion, the first step of providing our clients with customized fashion analytics and data-driven trend insights involves defining their unique target audience and brand identity.
Our artificial intelligence tool, Audience Intelligence, listens to our clients’ target audiences on social media and other online resources. The tool then gathers customized data from these resources upon analyzing millions of images, hashtags, and pieces of relevant text.
How Is AI Changing Trend Forecasting?
How is AI changing the world? By making everything more convenient, fast, and accurate. The same principle applies to trend forecasting; prior to the use of machine learning in this field, decision-makers in the fashion industry had to physically follow runway shows, fashion weeks and printed catalogs to pinpoint emerging styles and make “educated guesses” at best.
With AI, however, paired with the undeniably powerful impact of social media on practically every industry in the world, collecting valuable data and insights has never been faster and easier. From a celebrity on the street becoming a fashion icon in a matter of hours to young trendsetters becoming influencers with massive followings; it is now an industry where every single online material carries the potential of going viral.
It is true that data is abundant in this day and age; after all, social media in itself is reshaping the industry with 1 billion active users only on Instagram, more than 5 billion fashion-related posts, and 8 million active business accounts. However, when it comes to data, the more is not always the merrier. With the rapid increase of fashion content on social media (more than 100 million posts shared every day), it is not easy—even for highly qualified analytics teams—to determine where to look, organize endless amounts of data, then draw meaningful insights from this data on a daily basis.
That’s where AI-based trend forecasting comes in: a practical tool that translates billions of valuable data full of potential into useful information that can help brands produce or buy the right products at the right time, hence gaining an advantage over competitors and minimizing overstock products.
Benefits of AI and Big Data-based Trend Forecasting
We have already covered the general benefits of AI-based fashion trend forecasting, focusing mainly on its accuracy and convenience. But what are some more specific uses of big data-based trend forecasting for clients fulfilling different capacities in the fashion industry?
- Understanding the tendencies and expectations of your target audience
- Accommodating market trends
- Gaining insight into trend lifecycles
- Improving your demand planning process
- Targeting the right buyers for every trend
- Manufacturing more sustainably
- Building your social media strategy based on market data
- Showcasing meaningful product selections
- Following fast-changing trend dynamics
- Identifying your target audience
- Keeping your brand up to date with consumer behaviors
- Backing your creations with market data
- Designing best-selling products aligned with your brand identity
- Planning relevant collections
- Benefiting from tangible data while collaborating with business teams
- Remaining informed about changing consumer behaviors and emerging trends
- Buying the right products at the right time
- Buying products in the right quantities, minimizing overstock and waste
With all these potential benefits waiting to be reaped, more and more brands and companies are turning to the power of data to improve their business and outperform their competition.
In fact, a striking gap in total returns to shareholders (roughly 68%) has been witnessed between leading companies with digital and analytics capabilities, and those that adhere to rather traditional processes.
The problem with the traditional trend forecasting services is that they don’t tend to be objective, targeted, or comprehensive. Unless the data is drawn from a very wide and diverse source (such as social media) and analyzed strictly within the context of defined target audiences, it cannot effectively reflect customer reception and perception. That’s why building analyses on online catalogs published by brands or fashion news platforms rarely ever yield accurate insights.
The shortcomings of traditional trend forecasting methods in comparison with machine learning solutions are clear, but not all AI-powered platforms are created equal.
T-Fashion’s trend forecasting platform is designed with both creative and analytics teams in mind, and it is capable of analyzing more than 20 million images, extracting more than 250 million data points. Through its use, a 40% decrease in cost per sale is estimated to take place, in addition to a 10% revenue increase. To see how T-Fashion is leveraging social media to help you grow your business, request a demo now.