Big data gets a lot of attention in discussions about the financial, healthcare, and marketing sectors — in large part because they were already numbers-driven fields. Anyone could see how more, better data would improve efficiency and profitability there.
However, big data is having effects on other niches, such as the fashion industry, surprising to say. Fashion doesn’t seem like a good fit for big data because it’s so much more subjective than accounting or sales.
But fashion designers, producers, and consumers are all starting to come under the influence of big data, which is reshaping this industry, too ¦ for the better, one hopes.
New and Better Products
In the first place, big data is useful in an engineering sense: for gathering information from customer feedback and product performance to make new and better products. High-quality makeup brands like Urban Decay continually invest in new research and development to improve their popular products even further, and new lines like the Tyme Style flatiron have made it easier for women to style their hair.
Only through intensive research and analysis are product designers able to improve their existing products ¦ as well as introduce new concepts that consumers didn’t know they needed.
Trend Prediction
Fashion trends are challenging to predict because they mix objective design issues like the size of handbags, semi-subjective elements such as bright, colored stripes, and the largely subjective feel of various products and articles of clothing, for example, those that evoke 1980s nostalgia. To make the situation even more complex, fashion trends come in and go out of style in a cyclical fashion: they disappear for a decade or two before reemerging in full force.
Big data has the power to chart these cyclical fluctuations, however, at least to some degree, which gives producers some idea of the general trends that may come next. They may also be able to analyze customer purchasing behavior from past seasons to ascertain which fashion qualities or elements shoppers may respond to due to their future preferences.
Historical Analysis
Maybe a trend died before you thought it would. Perhaps your sales projections for a new line missed the mark completely. Historical analysis gives a company the ability to learn from the past, whether this means identifying the errors that led to a specific failure; or calculating which marketing and sales techniques would be best to replicate in the future endeavor.
This mode of planning — objective and built on the past — is making it easier for fashion companies and designers to devise better strategies for their products and brand image.
Production Projections
One major profitability killer in the fashion industry is the waste that results from overproduction. If you end up producing too many items in a given style, you’re left with a surplus of stock when the trend dies out, which could entail a massive loss if you aren’t careful.
On the other hand, if you produce too little of a product, consumers may become frustrated and go elsewhere, and you may utterly fail to cash in on a solidly profitable trend. Big data gives you a healthy middle ground; it enables companies to forecast demand more accurately, and produce in response to those figures.
Aggregate Information
Big data also gives companies the power to aggregate information from multiple sources. Rather than rely exclusively on your own numbers, you can tap into an entire network, or collaborate with data firms to get a much broader view of what’s going on across the fashion industry.
Big data is just starting to be tapped as a source for improving and developing fashion trends, and as data collection and streaming technology becomes more available, everything will become more heavily integrated. Soon, you may start to see designs that were manufactured by an intelligent algorithm, and witness trends you didn’t know you wanted to emerge.
It’s a fantastic time to be a fashion producer or consumer, and it’s only going to get more interesting from here.
