Fashion forecasting has a surprisingly long history. Originating in France in the 17th century as a means of communicating style, it quickly evolved into a tool for anticipating industry trends. It predicts societal moods, consumer behaviour, and buying habits, guiding brands on future releases. In this article, we want to take a closer look at how trend forecasting is done traditionally and which data-based approaches exist. And we ask honestly: does it make sense at all, and if so, how?
If you’re not familiar with the fashion industry, I’d recommend reading this brief introduction first:
Introduction: What You Need to Know About the Fashion Industry
The fashion industry is a key player in the global economy, facing constant pressure from market shifts and economic uncertainty. It’s a sector that, while historically rooted in craftsmanship and tradition, has always evolved in response to technological and societal changes. As one of the oldest and most dynamic industries, fashion is currently empowered but also challenged by the emergence of big data and digital innovation. Additionally, it’s a highly competitive market environment, consisting of numerous firms offering similar products. Also typical for the whole industry is the widespread segmentation of products, which remain highly volatile due to the regular changes of seasons and trends.
Over recent decades, globalization and shifting consumer expectations have significantly transformed the structure and demands of the fashion market. Companies, therefore, must navigate highly complex supply chains, adapt rapidly to volatile trends, and meet growing expectations for personalisation and sustainability.
A fashion company typically has a complex structure that combines very different jobs, from highly creative to managerial and analytical roles. Because of this organisational complexity, it’s hard to analyse the fashion industry as one industry – still, in this article, we at least attempt to.
How Fashion Is Influenced by Trends
Fashion moves fast by nature. It’s a form of self-expression that also reflects – and even shapes – aesthetic, economic, political, cultural, and social shifts. No matter whether you’re looking at fast fashion, high-end luxury, or sustainable labels, trends have a huge impact.
One of the biggest challenges the industry faces today is the constant stream of new trends, which keeps the market fiercely competitive. Customers now expect clothes to hit the shelves almost instantly, and their tastes can change overnight. More and more, they want pieces that reflect their personal style, from customised fits to unique colours and prints. For fashion brands, this often means dealing with unsold stock that loses its appeal as soon as the next big trend takes over.
Since trends often cycle every 20 years (and it’s shifting more and more to 10-15 years; just think of the ballerina shoes you could see everywhere in the 2010s, which made a huge comeback with the so-called “balletcore” trend recently), forecasting helps to identify which styles may re-emerge. Its primary purpose is to keep brands ahead of trends, enabling them to anticipate consumer demand and plan production in advance.
Why is it so important to get the trend right?
The design process is often seen as the most creative part of a product’s life cycle, usually led by the brand’s creative director. It’s also a stage that can benefit hugely from data-based tools. Big data analytics helps designers anticipate what the market will want and turn those insights into customer-focused collections. Instead of relying only on personal taste, they can look at concrete details, like popular colours, fabrics, sizes, or region-specific preferences. This makes design decisions more precise, efficient, and relevant. If a garment aligns with the right trend, it can boost sales and reduce the risk of overproduction, which also supports a brand’s sustainability goals. In a fast-changing industry like fashion, the ability to spot trends early is a key factor for success.
How predictable are trends?
Ever-evolving trends are accompanied by products’ short life cycles. They also cause rapidly shifting consumer preferences. As a result, fashion products are far more difficult to forecast than conventional goods, making accurate sales predictions particularly challenging. This volatility can make the fashion industry seem unpredictable. Still, the long history of trend forecasting suggests it does work – at least some of the time. But how exactly does it work?
How Does Traditional Trend Forecasting Work?
Traditional fashion forecasting is carried out by creative individuals such as designers or professional trend analysts, working freelance or for forecasting agencies. This type of forecasting typically involves collecting diverse cultural and market data. The professionals travel worldwide, visit design and fabric fairs, exhibitions, events, or simply observe their surroundings, taking music, new technologies, social media trends, and much more into account, depending on the goal and personal preferences. Later, they translate it into anticipated themes, colours, materials, and styles. Creativity has long been seen as central to this process.
Data-Based, Predictive Trend Forecasting
In contrast, purely data-based forecasting now enables a more evidence-driven and scalable method of trend prediction, offering an alternative to conventional, intuition-led forecasting. Data-based trend forecasting covers a wide range of applications, from inventory predictions to sales or market trend forecasting, allowing fashion companies to anticipate consumer behaviour in real time by analysing social media activity, behavioural patterns, and external factors such as weather and economic conditions.
To understand how forecasting works, which technologies are involved, and where data fits into the process, it’s helpful to first look at predictive analytics, since forecasting is a form of it. Predictive analytics analyses historical data to anticipate future behaviours and outcomes, enabling organisations to act more proactively. Common questions companies seek to answer include projections about future revenue or customer numbers. Predictive analytics is an advanced form of data analysis that asks “what if?” questions to guide decision-making. Fashion trend forecasting uses various techniques and data sources, such as big data environments, machine learning, and artificial intelligence. These technologies can support product development and strategic planning; however, in the fashion industry, there is still limited clarity about their exact roles and implications.
Now, What Is Better? A Critical Reflection
The traditional, individual-led approach is increasingly criticised for relying too heavily on personal intuition rather than objective insights. Particularly within smaller companies, they still rely heavily on subjective forecasting. In such cases, a single trend forecaster may base their predictions largely on personal taste, environmental influence, or the selective lens of their social bubble, leading to potential bias.
But the data-driven one has its weaknesses as well that should be considered: whilst there exists the common saying that “data never lies”, data, sometimes, is still open to interpretation, which means that two people can draw very different conclusions from the same dataset, depending on their goals and approach. Another pain point of data-based fashion trend forecasting is that it tends to focus on micro-trends such as colours or styles and often neglects broader strategic perspectives supporting sustainability. In light of growing environmental concerns, the forecasting field must evolve to support more sustainable business models. There is also the risk of replacing creative decision-making entirely with algorithm-based outcomes, which can lead to a loss of originality and brand identity due to increasingly uniform outputs. This would contradict the very essence of the fashion industry.
Hybrid models as a future perspective
A contemporary approach is to bridge traditional, intuition-based fashion forecasting and strategic data utilisation by combining creative industry expertise with input from fields such as data science, sustainability, colour theory, textile innovation, and market research. This hybrid method considers both data-based evidence and “soft factors” such as social, technological, economic, political, or environmental shifts. Aligning emotional and aesthetic fashion values with the help of humans who have a high level of creativity and a deep knowledge of the brand’s identity, together with data-supported business strategies through modern trend forecasting tools developed by data experts, can help to find a good balance.
Conclusion
To sum it up, forecasting not only supports the identification of emerging trends but also contributes to strategic planning, brand positioning, and product line expansion. Fashion companies should also strive to strike a balance between data-driven trend analysis and creative originality to maintain their unique value proposition and avoid blending into a sea of generic outputs. The use of both internal and external data can help identify early signals of emerging trends, but it should never fully replace creativity as the core of brand identity.
At Diconium, we acknowledge that companies already provide high creativity but have difficulty seamlessly integrating supportive, data-based technologies. Therefore, we’ve developed our own Time Series Forecasting Toolkit. Combining advanced machine learning and AI-powered insights with an intuitive, no-code platform, it empowers business leaders to move quickly from data to decision, making intelligent forecasting accessible to everyone.
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