Most of us with a pulse on the business of fashion are familiar with the concept of trend forecasting, informed by runways, specific en vogue patterns or colors, and myriad other factors that influence the merchandise we see when we walk into our favorite retailers.

Increasingly, this is being done with AI modeling and machine learning informed by large swaths of data that paints a clear picture of not only what consumers want, but how much they’re willing to buy. Leveraging a data-informed approach isn’t just a useful insight for successful production and sales — it’s virtually vital to their survival.

In very plain terms, fashion forecasting is an industry-wide practice that informs brand designs, sourcing, and production in such a way that allows them to maximize profit, minimize losses through markdowns and excess post-production waste, and, importantly, lessen their environmental footprint along every step of development — from the earliest stages of ideation to the arrival of merchandise on store shelves.

Most merchandisers use some kind of predictive analytics to inform their output, whether that’s past sales, industry trends, or, more commonly, by utilizing large datasets to paint a picture of not only who their customer is, but how, where, and for what they shop. For many brands big and small, it’s the next best thing to a crystal ball.

And while humans are unpredictable, AI modeling can help brands better understand where to trim the fat, and where to funnel additional resources and funding into growing their business.

I’m steeped in this world of fashion forecasting, and I’ve seen first hand how time and time again data can help merchandisers not only boost profits but minimize their environmental impact by making informed decisions about their output.

Currently, I work for a sustainability brand, but prior to that, I worked for years for major fashion brands — long before “sustainability” was even a buzzword in the industry. 

Predictive analytics is an essential need

There was a time not too long ago that waste — both pre- and post-production — was not only normalized but a widely accepted inevitability of the business. But times have changed dramatically since then.

Now, tightening supply and demand is talked about all the time, certainly to the benefit of the environment and each brand’s responsibility to waste mitigation, but also as a proactive business strategy. Trend forecasting is an essential need for brands that want to avoid excessive markdowns and excess textile waste, particularly where it relates to knowing and building rapport with their consumer base. 

Let’s back up a little bit. In order to thrive in an expansive marketplace of virtually unlimited choice, it’s crucial for brands to connect with a specific demographic of consumer, and more importantly, continue to finetune their product to keep that demographic returning.

For example, a brand like Patagonia might focus on marketing to consumers who align with their specific values and are willing to pay a premium to offset the costs of, say, ethical materials sourcing. Another brand like Zara might appeal to someone who wants a luxury look without the luxury price tag, while yet another brand — let’s say Chanel — is able to significantly price up its products because its customers are primarily status shoppers.

Regardless of who the brand is catering to, understanding the specific tastes of those customers allows brands to allocate budgets, source textiles, and retail their products while also mitigating as much waste as possible.

Machine learning can also help brands understand other factors like sentiment analysis, allowing them to assess and recalibrate factors like customer experience to ensure continued business. Moreover, this kind of data provides insights to merchandisers in real-time. That’s as good as gold for brands.

These brands rely heavily on rich datasets to understand otherwise complex patterns of consumer behavior, allowing them to make informed decisions that, again, not only benefit the earth but minimize what can amount to millions in lost profit. It’s no secret that big name brands have long destroyed their excess post-production waste through methods like burning, painting, or shredding — and that ain’t cheap (nor does it benefit a brand’s bottom line).

Finished goods waste is a complete loss at every level, involving pollution forms that span everything from chemical and fiber waste to material waste and excessive warehouse space. Especially in this conscious-consumer environment, it’s not only a bad look, it’s potentially damaging.

Why AI forecasting reigns king

Some critics of the system argue that such forecasting relies too heavily on past consumer behavior to inform future production planning. Alternative methods, for example, focus more on pre-production sustainability rather than behavior-informed trend forecasting. However these data-driven decision making processes have proven an invaluable tool for reducing waste and ensuring continued business with suppliers.

By analyzing consumer buy trends, brands can waste less fabric and make informed decisions about their brand positioning and product offering — the effects of which I’d argue are essential to the survival of brands in a post-pandemic consumer market. 

That said, I’d also argue that while data-informed forecasting is preferential for many merchandisers, there’s room for more than one approach to tackling our sustainability crisis.

Short of made-to-order merchandising, few brands are willing or able to tackle the sustainability crisis from all angles — such as tracing a product from its inception to post-consumer discard — without some form of regulatory intervention. But the benefit of AI forecasting is that brands still have the freedom to generate original ideas and beautiful art while also responsibly scaling those visions with as little excess as is possible under our current system.

Here’s the bottom line: Brands should not only want to use AI forecasting tailored to their individual brands, but merchandisers need them if they want to reduce excess waste and maximize profits. Machine learning modeling helps businesses make informed decisions that people simply cannot.