Why data analytics is crucial for fashion & apparel manufacturers and retailers

Regardless of its size, any fashion or apparel manufacturer is confronted with challenges related to predicting the future.

Unlike the big brands that manage to create and impose trends, smaller players try to foresee what will be the forthcoming changes in consumer tastes. Trends might arise even from occasional events, take for instance Converse sneakers which became a big hit after rock bands like The Ramones wore them onstage on their gigs at the late 70s and early 80s.

Apparel manufacturers often can do nothing but to follow and adapt according to trends. Discussing fashion and apparel, such shifts affects everything – size, color, shape, fabric, price, etc. are a piece of the equation for success or fail.

Winners are those who recognize trends change successfully and early enough. Let’s say it will be pretty sad if a fashion brand decides to emphasize on miniskirts when the miniskirt hype is passing its peak.

Sure, every fashion and apparel brand is unique in its own way. They already succeeded to attract loyal customer base because of style, quality, design, and identity. No matter what is the reason for such trust, neglecting new trends might lead to consumer interest decrease which might result in slower sales.


We have to have in mind that the process of a design creation is not really quick. It’s a product pipeline comprising not just design, but also fabric, sizing, and other variables. Actual producing takes time and often it happens at a remote geographical location. The produced stock must be transported and distributed in stores, then advertised. It takes several months to finish this. If you want to put this on your autumn-winter collection, you have to start the work on it at least the spring before and sometimes such schedule is not comfortable enough.

Thus, the fashion & apparel brands face the challenge to foresee the future few months before their products are market ready. We shouldn’t forget that every product line or collection requires serious resources. For collection`s success, you have to predict next season’s trends and consumers demand. If a brand fails with such a task this might lead to overstock which should be put on sale with heavy discount.

Players on the fashion field are trying to capture their customers’ tastes by following local and world trends. Such approach is less or more empirical than factual.

If you are small and niche fashion/apparel brand you have to follow your own customer base

Consumers trust brands because of their specifics and unique qualities. In order to predict their needs, you have to listen to them by applying data analytics to your sales and consumer behavior. It seems that the data every fashion or apparel brand generates are as important as the big scale trends. Processing such type and amount of data is not an easy task.

Contemporary data analytics offer numerous tools for predicting future also known as predictive analytics. This data-driven approach is giving results via complicated mathematical modeling and is able to extract precise information that supports decision-making process in planning such as:

  • Amount – right quantity at the right time for the right market
  • Type – you don’t want to offer miniskirts right after their hype
  • Size – producing too much XS for a market full of plus-sized consumers is not best case scenario
  • Colors – catching what is hot for the next season is crucial
  • Fabrics – if linen is back, do not miss the trend

As you know, it’s not necessary for fashion trends to be interpreted the same way in UK and France. The confusing truth that online fashion and apparel retailers will share with you is there are key differences even in different cities in the same country.

Data analytics is able to give fashion and apparel entrepreneurs’ key information supporting product management decisions. Via data mining techniques, followed by data preparation it is possible to be extracted data for main categories, dimensions, and slices. Loaded with such information, sales forecasting tools are able to tell you the amount of hoodies’ apparel brands is going to sell for the next season, which will be most attractive colors as well as to define long-term trends in its clients` base – whether the consumer demand will be increasing, decreasing or will be in plateau.

It is a matter of management decision if fashion and apparel manufacturers and retailers will use predictive analytics, but for small and medium brands it might be a key to improving efficiency and minimize loss.

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