Define the Market Scope
Begin by narrowing your focus to the exact parameters of your activewear range. Specify the region, target consumer emotion, and product category to ensure the data is relevant to your specific customer. Avoid general seasonal trends, which often lack the precision required for high-stakes product development.
- Define the product category: e.g., women’s leggings or performance tops.
- Select the target region and season.
- Input the consumer emotion to shape how the evidence is synthesised.
Analyse Evidence and Synthesis
Use the platform to gather ten stages of evidence, focusing on how silhouettes are evolving in your specific category. Look for shifts in volume and proportion rather than just aesthetic appeal. For example, when analysing runway data, note that current shifts in tailored pieces show a 55 percent representation in blazers, while oversized jeans have grown by 10.2 points.
- Review the design boards for silhouette consistency.
- Compare the carry-over versus new-development split.
- Check the colour harmonies, such as Deep Blue, which saw a 1.4 point increase in look representation.
Validate Against Range Architecture
Convert the forecast into a concrete range plan. Ensure that your silhouette choices are supported by the evidence provided, such as print trends like Checks at 12.7 percent or Stripes at 17.9 percent. This stage allows you to balance your collection with data-backed confidence.
- Align your silhouette choices with the generated design boards.
- Verify that your material and trim directions match the identified themes.
- Confirm that the option count reflects the recommended seasonal architecture.
Expert view
“F-predict platform can help users to identify the best selling Activewear trend through contextual search feature. That is specific to users region, city, Market (Gender based) Activewear subcategory . It helps users to identify the consumer culture, street trends, catwalk, Campaign and narrative trends to build an accurate forecast”
— Samantha, Head Of Trend
Frequently asked
How can AI support fashion designers without replacing creativity?
AI handles the data-heavy evidence gathering and synthesis, allowing designers to focus on the creative application of these insights into their collections.
How does F-Predict compare to other AI forecasting tools?
F-Predict differentiates itself by focusing on a specific market scope rather than general seasonal trends, providing evidence-based design boards and range planning metrics.
How does AI fashion forecasting work?
It collects data from campaign imagery, street style, and brand communications, clustering this evidence into themes that are then weighted and matched to specific design directions.
Keep reading
How can AI support fashion designers without replacing creativity?
Where the line sits between breadth work and judgement work.
How does F-Predict compare to other AI forecasting tools?
The four categories of AI tool, what each answers, and what none of them do.
How does AI fashion forecasting work?
The four-step pipeline, and the failure modes that make AI forecasts useless.
What is F-Predict?
The platform in one page: what it is, what you give it, what it returns.