We interact with fashion constantly: from the clothes in our closet, to the shops we walk past, to the social media posts from influencers or fashion publications. The reality of fashion’s constant presence in the consumer’s life is that trends seemingly shift rapidly with every season – and commercially, this means that brands must adapt their product development systems to align with shifting demand.
This is where fashion trend forecasting comes into play. The goal is to anticipate the styles, fabrics, and colors that will define a fashion moment so that apparel companies can meet the trend and maximize sales.
Defining Fashion Forecasting
Broadly, there are two kinds of trend forecasters:
1. Short-Term: This type of forecasting focuses on short-term, immediate industry changes. It usually relies on sales data, current trends, and real-time insights that brands use to shape collections and respond quickly to consumer demand. Forecasters usually draw on current trends on social media and pop culture, directly using these insights to inform product development.
Fast-fashion businesses largely benefit from this forecasting model. A popular example is ZARA, which utilises a ‘Speed-to-Market’ strategy, quickly selling products strongly influenced by current trends, reducing reliance on long-term predictions, and allowing it to adjust production based on real-time market feedback.
2. Long-Term: The more traditional type of forecasting, which seeks to predict market trends at least two years into the future. This approach considers major socioeconomic, environmental, and technological shifts that may influence consumers’ expectations, values, and behaviours.
Larger companies with longer production lead times may largely benefit from these predictions. For example, Nike has developed forecasting software to monitor current and future trends by analysing both contemporary market conditions and historical data. With this in mind, companies like Nike can keep up with rapidly shifting trend cycles while maintaining more sustainable production practices.
In the fashion industry, trends seem to cycle more rapidly than businesses can keep up, making it difficult to maintain traditional long-term forecasts in an age where speed gives businesses a competitive edge.
Today, brands largely benefit from a combination of short- and long-term trend forecasting. Systems such as Apparel ERP software help brands plan production timelines and reduce excess waste, while AI-driven prediction platforms let businesses quickly analyse current media trends alongside historical data. However, AI cannot completely replace the industry experience, cultural insights, and intuition contributed by human trend forecasters, ultimately highlighting the need for technological and human collaboration in the forecasting process.
The Fashion Forecasting Process
Forecasting agencies largely conduct fashion forecasting, and French companies based in Paris have traditionally dominated the profession. For companies to make well-informed decisions, an effective fashion forecast must include:
- Market Research – The initial market research stage allows forecasters to understand where the market currently is. The apparel supply chain ultimately seeks to cater to consumer needs; therefore, every forecast must observe current consumer behaviours and how that might inform future trends.
This stage involves extensive analysis and obtaining data: whether at the consumer level by analysing social media activity, sales data and survey responses, or at the industry level through observing runway collections, reading fashion publications, monitoring popular culture trends, and networking with fashion professionals.
2. Trend Forecasts – After conducting their research, forecasters can then move on to creating their reports. The research data is compiled to indicate either broad (e.g: market shift towards activewear) or narrow (e.g: potentially trending cuts, colours, and styles) predictions for what the market may favour next.
These forecasts may also include a practical assessment of the feasibility of product designs and how to adjust production processes accordingly.
3. Industry Review – Beyond consumer demand, forecasters also track new moves by competitors that may influence product development.
4. Colour Forecasting – Fashion brands will pay close attention to the colours they use in upcoming collections, which largely influence their products’ ‘mood’. As such, forecasters must consider different factors and their potential influences on consumer preferences. Colour predictions allow brands to hypothesise which tones and shades may be more favourable in the market in line with certain socioeconomic, environmental, or technological developments.
These predictions are usually made two years in advance, providing fashion brands ample time to rearrange production schedules and processes before initiating product development. These predictions are incredibly significant in the industry, relying on both real-life data and intuition. In fact, they are so imperative to the design process that there are specialist forecasting companies who will sell a limited colour story on a seasonal basis to the fashion and textile industry.
Colour choices are hugely significant for brands, and getting colour trends right is key to crafting collections consumers will connect with and improving a product’s saleability. This element of trend forecasting is one that has recently been extensively researched, with many papers discussing the importance of colour choice when releasing a fashion product in a micro-trend-driven market.
5. Textile Development – Aside from the visual element of connecting with the consumer, brands will often aim to consider the ‘feel’ of a product through the materials and textiles used. Technological breakthroughs play a role in this element of forecasting, as innovations in materials used may increase a product’s functional value (thereby, its consumer appeal). Forecasters must carefully predict certain materials’ appeal and use that to estimate the monetary value a textile may have to the public.
An example is the evolution of workwear to cater to the consumer’s needs: Some modern fabrics are infused with silver ions, copper, triclosan, and other compounds that inhibit bacterial growth, reduce odors, and improve hygiene; they also draw moisture away from the skin to keep athletes dry and comfortable during exercise.
6. Inventory Analysis – Although forecasting is largely dependent on monitoring external factors and trends, forecasters must also consider a brand’s internal trends. Before launching a new collection, fashion companies must also consider how their product might fare alongside existing collections, and whether a new launch dependent on certain trends might boost projected sales.
7. Economic Forecasts – This final component of fashion trend forecasting is rooted in consumer behaviour, requiring forecasters to consider the global, sociopolitical context that might shape consumer demand, spending habits, and market stability. An effective forecast will thus also include a prediction of a product’s demand based on the market’s economic conditions, informing brands of how successful a certain collection or product might be in the current economy.
The Importance of Fashion Trend Forecasts
In a world where fashion is dominated by micro-trends and rapidly shifting consumer demand, fashion forecasting is more imperative to brands than ever. In response to fast-paced trend cycles, brands will often favour business models reliant on short-term forecasts, or integrate AI systems that can rapidly integrate real-time occurrences into long-term predictions.
Brands rely heavily on forecasting reports, which are sold and protected as commercial intelligence, when developing their next steps as a brand. However, the rapid movement of trend cycles does beg the question of whether forecasting can even keep up with the constant changes in consumer demands. This begs the question: Are trends predictable? Or are they merely announced?
Author: Vyga Krish
Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of Fashion Law Journal
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