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What Is Fashion Trend Forecasting? And How It Relates To AI and Social Media

What Is Fashion Trend Forecasting? And How It Relates To AI and Social Media

Fashion trend forecasting, which aims to master the changeability of fashion, is as essential as it has always been. When it comes to businesses, predicting is crucial. Knowing the trends and generating accurate forecasts can make or break a company’s success.

Forecasting is thus a vital component of the ever-changing fashion sector. So, what exactly is trend forecasting, and how is it done in the AI era? Continue reading to find out!

What is fashion trend forecasting?

Fashion trend forecasting is the process of projecting future fashion trends. The projections allow fashion designers and merchants to know what will be fashionable in the future and plan their collections and merchandising appropriately.

Trend forecasting in fashion aims to create a story using shapes, design elements, colors, fabrics, and trims. Trend forecasters make reports and mood boards that designers use to create new clothing and accessories for brands.

Short term trend forecasting

Short-term and close-to-season forecasts give a good idea of what customers will want in the next 6 – 12 months.

Microtrends have a role in short-term forecasting. Short-term projections vary by season and are often concerned with color, style, and the impact of current events and pop culture.

Long term trend forecasting

Long-term forecasting examines broader directional points that suggest a societal change, commonly known as macro trends. A macro trend forecast depicts consumer lifestyles and makes forecasts across categories.

Long-term forecasting seeks to identify significant changes in local and international demographics, shifts in the fashion industry, consumer expectations, values, and purchase impetus. For example, consumers now tend to favor sustainable fashion, a macro trend.

Technological, scientific, economic, political, and cultural changes can also impact macro trends. A great example is a COVID-19 pandemic, which has changed the way we consume and the products we need or are interested in. 

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To effectively predict fashion trends, it is necessary to observe and study social-cultural movements to project a short, medium, or long-term impact on fashion direction. Trend forecasters develop a scenario using a combination of intuition and data, which they then present through writing and mood boards.

To generate a forecast with confidence, an immense scope of information must be gathered through research. One of the challenges of trend forecasting and why agencies’ services frequently come at a significant price is the complexity required for research and collecting relevant data.

Every brand’s trend forecasting process is unique. Womenswear brands, for example, invest more in micro trend analysis than male brands because they produce more collections every year. Forecasting is also affected by the company’s size and target market. 

Here are some of the sources that businesses utilize to make forecasts.

  • Attending fashion shows – Noting the most promising designs and then bringing that knowledge to chain-store product producers from the catwalk. The process is known as “top-down” forecasting, and it is based on how fashion trends spread from haute couture runways to high street stores.
  • Following influencers – Today, trend forecasters are more likely to go to influencers, street style, and blogs for information on the newest trends. This technique is known as “bottom-up” forecasting.
  • Using a trend forecasting agency – Larger firms that aren’t vertically integrated frequently rely on the knowledge of trend forecasting agencies, who create trend research reports for them.

Most trend forecasters rely on a combination of “top-down” and “bottom-up” forecasting, plus an intimate knowledge of the fashion scene and personal intuition to make fashion predictions.  

However, such systems are often inefficient, costly, and heavily reliant on the expertise of the experts, which frequently incorporates bias into the predicting findings.

But just as every other area of life, AI has made its way into fashion trend forecasting, and it’s aimed to shape its future by making more accurate and more efficient predictions. 

The role of social media and AI in fashion trend forecasting

The Internet, and hence social media, has expedited the life cycle of trends, giving rise to phenomena such as fast fashion and global supply chains. Due to the digital age, trend virality, time-to-market speed, and consumer behavior have all transformed in the previous decade. But those same technological advancements are also shaping the future of trend forecasting.

Fashion forecasting services currently use new technologies to predict what’s next in trend. In fashion forecasting, AI is frequently used to analyze text and hashtags on social media, online collections, and customer activity on e-commerce.

Fashion trend forecasting is gaining study attention as an upstream research task in computational fashion analysis. It is typically researched via the lens of fundamentally related tasks such as fashion recognition, detection, retrieval, and segmentation.

As a computational problem, fashion forecasting extends beyond just predicting the future purchase rate of an item seen in the past. As a result, it is more than just a regression problem.

Instead, the forecaster must speculate on styles that will become popular in the future to build yet-unseen style compositions. The capacity to forecast the future of styles rather than individual clothes is appealing for applications that require models that reflect where trends as a whole are headed.

The purchase record on either online or offline retailing platforms is a prominent data source for fashion trend analysis. But people’s purchasing decisions are impacted by various circumstances such as promotions and thus do not reveal the genuine fashion preferences of users or the actual fashion trend.

Social media captures people’s daily lives worldwide and has evolved into a platform for increasing users to express their fashion preferences and opinions. As a result, it provides a natural outlet for study on trend analysis.

Furthermore, data from social media is enormous, diverse, closely related to the fashion trend, and has a long time span, making intelligent large-scale fashion trend analysis conceivable and valuable.

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That is precisely what Heuritech, a fashion forecasting firm, is doing. Their patented image recognition engine can recognize and classify thousands of components in a single social media snapshot, including forms, characteristics, fabrics, textures, patterns, and colors.

Heuritech’s technology determines that it is looking at a photo of a striped t-shirt, for example, since it detects the product has a crew neck, short sleeves, distinct colored stripes, and even the fabric type. Because this technology analyzes millions of photos every day, the volume of striped t-shirts may also be calculated.

Heuritech can forecast future behavior by quantifying a trend’s prior growth and evolution by looking at its volume or popularity.

These forecasts are critical for merchandisers and designers to see how a particular product is being accepted in real-time, by what type of customer, and in which areas throughout the world.

Then, by utilizing AI to transform real-world photographs posted on social media into relevant data, Heuritech can enable companies to more correctly predict demand and trends, manufacture more sustainably, and reach exceptional competitive advantage.

We may see AI models incorporating more diverse data about consumers in the near future. For example, this paper addresses the fashion trend forecasting problem based on social media, aiming to mine the complex patterns in the historical time series records of fashion elements and predict future trends accordingly. 

The researchers concluded that, although desirable results were achieved, some aspects could be further improved in the future. For example, including information that specifies the user’s fashion preference, such as occupations or hobbies, and more knowledge sources that have significant impacts, such as fashion magazines, bloggers, and brands.

Final thoughts

The use of high-tech in fashion trend forecasting is still in its early stages, but it can completely change the way businesses operate. By harnessing the power of AI and social media, we can get a more accurate picture of what styles will be popular next season or even next year. We can expect even more sophisticated predictions and trends from AI-assisted fashion forecasting as this technology evolves!