Can Data Solve The Environment’s Fashion Problem?

There’s no skirting the terrible environmental costs that fashion has incurred over the last couple of decades. The fashion industry (apparel and footwear combined) generated 3,990 Million Metric Tons of Carbon Dioxide — and this was just in 2016. Currently, the fashion industry contributes to 10% of global emissions (second only to the Oil Industry) and unfortunately, isn’t slowing down anytime soon. It is predicted that the fashion industry’s growth will lead to a 60% increase in emissions by 2030.

Data and Sustainability

In order for a fashion brand to be sustainable — truly sustainable — it needs to crunch numbers. Many sustainable fashion brands talk openly about their products’ impact on the environment. Reformation, for example, includes details about the volume of carbon emissions, water and waste that each of their designs has saved, right on the product page. Bigger fashion conglomerates, the likes of Kering Group (which owns Gucci), Inditex (Zara, Mango, Pull&Bear and more) and H&M have signed pledges to reduce their carbon emissions by 2030, which means that they will be tracking their emissions closely as well. However, the fact remains that monitoring a company’s environmental impact is a process that is intensely time-consuming, given the complexities of fashion’s supply chain. Factories need to be vetted, suppliers, investigated and design, adapted. Collecting that data and consequently, acting on it, can take years. Years, that our planet may not have.

But what if there was a different kind of data that fashion companies can use to become more sustainable?

Data, that can significantly make production processes more efficient and therefore, significantly cut down on waste.

Data, that the company already possesses.

Know Thy Customer

Gone are the days when fashion brands and retailers segmented their customers by age, location or gender. Today, with the advent of retail personalization suites, every customer is a segment. There are tools that allow fashion brands to interpret customer buying patterns across categories and accordingly predict future purchases accordingly. When brands can forecast purchases accordingly, they can produce more clothing that sells as opposed to being sent to the landfills or burned. For context: 92 million tons of clothing end up in landfills globally. That’s about 80% of donated/discarded textiles. This can take up to 200 years to decompose and as if that wasn’t bad enough, the fabric releases methane, a dangerous greenhouse gas, along the way. If you think fashion related pollution is a fast fashion problem, think again — Luxury brands regularly incinerate unsold inventory to preserve their brand and maintain exclusivity through scarcity.

Producing what the customer wants boosts revenue and retention rates (sometimes as high as 35%). It also reduces the financial and more importantly, the environmental costs of excess production.

Clean Your Catalog, Clean The Planet

Plotting customer purchase patterns is only one part of the data story. The foundation of this analysis lies in the brand’s catalog data. If the catalogue isn’t clean, or worse, inaccurate, brands may find themselves making costly mistakes with their production decisions. The unfortunate truth is that most brands outsource product tagging to catalog management farms, where product attributes are keyed in manually. Manual processes are both time-consuming and have plenty of room for inaccuracies. These inaccuracies can impact not only production decisions (think crew necks instead of scoop necks, yellow instead of white, fit and flare dresses instead of shift dresses), but also create an inferior customer experience for shoppers browsing the site. After all, it is catalog data that is used to power search. Inaccurate catalog data means that customers aren’t going to be able to find what they’re searching for and will leave the site, irrespective of whether the brand actually stocks it.

Brands must look to ensure that their catalog data is accurate and rich while cutting down on the time it takes to actually input the data. The good news is that automation, if brands choose to go that route, can save up to 30 hours per person per week.

Data = Sustainability

A clean catalog containing rich data coupled with customer’s purchasing patterns will enable brands to predict future purchases accurately and cut down on obsolete inventory. Producing less means that businesses also get an additional advantage — a faster supply chain. By mining data and dedicating resources to produce only what sells, brands can optimize lead times, reduce emissions, cut down on waste and hopefully, save the world.

Photo credit: Christian Boltanski ‘No Man’s Land’ exhibition, a 50 ton mountain of discarded clothing.