In the last trend report I received, there was quite a lot about dresses, skirts and tops. As a man in his late 30s, none of these trends suited me. Which is not really surprising. But even in the trend reports on the latest sneakers, there was nothing that appealed to me. Is it because of me? Not quite. It’s because an editor has to decide on a small number of trends. There’s certainly a sneaker trend that would suit me, it just wasn’t chosen. If a report on all possible trends were drawn up, an almost infinite number of pages would come out.
In the report, the trends would have to be set by gender, age, category, culture, background, occasion, etc. Since this is relevant for us in the company, we tried to get an indicator with the following parameters:
- Sex: 3 (men, women, unisex)
- Age: 6 (Child, Teenager, Young Adult, Adult, Senior)
- Occasion: 6 (Basics, Casual, Chic, Business, Sporty, Rocky)
- Categories: 7 (dresses, blouses, trousers, jackets, shorts, shoes, sweaters)
- Attributes: 4 (color, pattern, length, extras)
- Type: 2 (Extroverted, Introverted)
- Season: 4 (spring, summer, autumn, winter)
- Weather: 5 (warm, cold, rainy, sunny, snow)
This simple listing would already generate over 30,000 reports. Therefore, we prefer to let our Artificial Intelligence observe this and return only the relevant results. It was especially cool when we received the first sketches of trends to better visualize the data from the reports.