H&M's savvy data-driven moves to save fast fashion are working
The moth-eaten shirt wasn't a hit in Europe... For decades, H&M designers visited fashion shows and flipped through glossy magazines to spot what was on trend, then bulk-ordered cheaper versions for their stores. The result: the same “fast-fashion” styles delivered to H&M's nearly 5,000 stores, cookie-cutter style. That worked (until it didn’t).
- Wasted fabric: The cover-all-bases approach missed for many shoppers, leading to billions of dollars in unsold clothes piling up. H&M sales shrank the past 2 years, and shares plummeted 51% in the last 5 years.
- Discount rack: H&M had to aggressively slash prices (and even burn clothes it couldn't sell, donate, or Secret-Santa away).
Data = dollas... On Monday, H&M announced it's on track to increase profits for the first time in 4 years, thanks to a huge strategy shift. A couple years ago, H&M stepped up its analysis game, hiring hundreds of data scientists to help make savvier predictions of what clothes go to each store. American shoppers probably aren't into the same rompers as Russian shoppers are.
Drop the "Squad Goals" graphic tees... H&M stepped back and reassessed before clicking "Add to Cart." Instead of using humans to scout trends (like most retailers do), H&M now combs through social media posts, search queries, and the receipts of returned clothes — it better predicts what's earned a spot on the rack:
- H&M sees that its store in a swanky Stockholm neighborhood is mostly visited by upper middle-class women.
- So it removes bland basics for men and kids and swaps them with fashion-forward floral skirts, cashmere sweaters, and $118 leather bags.
- The end goal? Make less clothing that people don't want. Survive the retail apocalypse.