Fast-Fashion Industry Dynamics
The rise of fast-fashion brands such as Zara, H&M, Top Shop, and Forever 21 has contributed to the decline of the traditional bi-annual fashion seasons and the emergence of near weekly “micro-seasons”.[1] The success of the fast-fashion business model hinges on anticipating micro fashion trends and bringing them to market quickly and at low cost.[2] Since fast-fashion retailers are focused on predicting rather than creating fashion trends, it is critical that their predictions are correct; otherwise, they risk getting stuck with inventory that they can’t move once the next “micro-trend” begins. As a result, fast-fashion retailers are turning to machine-learning to help detect trends and avoid an unpopular and costly product cycles.
H&M Stock Hits 10-year Low as it Struggles to Keep Up with Competitors
H&M has struggled to keep up with other fast-fashion retailers in predicting retail trends and localizing their merchandise to appeal to consumer tastes. In September of this year, H&M’s stock price hit a more than 10-year low (see Chart 1) after reporting that pre-tax profits shrank nearly 20% from the previous year.[3]
H&M’s declining performance can be attributed to two key factors. First, H&M has consistently failed to predict and respond to fashion trends ahead of competitors. In March 2017, Goldman Sachs reported that H&M’s supply chain lead times are double those of Zara.[4] As a result, H&M’s inability to execute quickly has left the company with nearly $4B of unsold inventory.[5] Second, H&M failed to understand consumer preferences in key markets. According to Forbes, “you could walk into any H&M store whether it was located in Sweden, the United Kingdom or the United States and it would carry very similar merchandise”.[6]
Pathways to Just Digital Future
H&M Looks to Machine-Learning for Turnaround Efforts
In an effort to improve performance, H&M is turning to machine-learning. The Wall Street Journal reports that H&M plans to analyze store receipts, returns, and loyalty card data to better align supply and demand and reduce reliance on markdowns.[7] H&M piloted this approach in their Östermalm, Stockholm store.[8] The store had previously been stocked with basics for men, women, and children—but after using machine-learning to analyze purchase history, they learned that most of the store’s customers were women.[9] As a result, the store was able to reduce the number of items it stocked by 40%, adding more fashion-forward items for women and completely removing its menswear line.[10]
Emboldened by the early success of the Stockholm pilot, H&M is now investing heavily in machine-learning to inform assortment and demand planning. Rather than relying on merchants to predict trends, H&M has built a team of 200 data scientists, analysts, and engineers to analyze data ranging from external blog posts to internal purchasing data.[11] In addition to using machine-learning algorithms to build better assortments, H&M is investing in automated warehouses, with the ultimate goal of achieving next-day delivery for 90% of the European market.[12] Long term, H&M is hoping to implement RFID technology in its stores to further improve efficiencies in its supply chain.[13] The RFID technology would allow customers to scan labels and receive personalized recommendations based on their purchase history or interests.[14]
Thinking Beyond Machine-Learning
H&M is making a big bet on machine-learning to turn the company around from a failing chain retailer to a digitally integrated brand. Unfortunately, this effort may be several years too late. The positive results from the Stockholm store pilot are encouraging, but given H&M’s massive 4,288 store portfolio, I recommend further validating its investment by piloting the technology in a critical mass of stores that is indicative of H&M’s global store portfolio prior to rolling this initiative out to all stores. Also, rather than solely focusing on the rapid implementation of technology, I recommend that H&M invest in radically re-building its culture and bringing in fresh talent that aligns with its new company vision. In an interview with Women’s Wear Daily, H&M’s CEO, Karl-Johan Persson, rejected the need to change company culture explaining that “the recent reasons why we did some mistakes connected to the H&M brand and physical stores is because we haven’t been customer focused enough, we haven’t lived [our] values well enough, so it’s more revisiting that.”[15] I think that customer-focus is exactly the cultural mindset that H&M lacks. Unfortunately for H&M, by the time its senior management team realizes this, no pivot will be able to turn the company around. If machine-learning is in fact the answer to H&M’s problems, given its 3-year slump and all-time low stock price, does the company have the luxury of time to see through the benefits that the technology can offer?
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Footnotes
[1] “The Future of Fashion: From Design to Merchandising, How Tech Is Reshaping the Industry.” CB Insights Research, 28 Feb. 2018, www.cbinsights.com/research/fashion-tech-future-trends/.
[2] Marr, Bernard. “How Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.” Forbes Magazine, 10 Aug. 2018, https://bit.ly/2QAczkQ.
[3] “H&M’s Q3 Pretax Profit Falls More than Expected.” Thomson Reuters, 27 Sept. 2018, reut.rs/2B412oo.
[4] Ringstrom, Anna. “H&M Invests in Supply Chain as Fashion Rivalry Intensifies.” Thomson Reuters, 30 Mar. 2017, https://in.reuters.com/article/h-m-results-idINKBN1711G5.
[5] Chaudhuri, Saabira. “H&M Pivots to Big Data to Spot Next Fast-Fashion Trends.” Wall Street Journal, 07 May 2018, https://on.wsj.com/2rr9qs2.
[6] Marr, Bernard. “How Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.”
[7] Chaudhuri, Saabira. “H&M Pivots to Big Data to Spot Next Fast-Fashion Trends.”
[8] Ibid.
[9] Ibid.
[10] Ibid.
[11] Ibid.
[12] Marr, Bernard. “How Fashion Retailer H&M Is Betting on Artificial Intelligence and Big Data to Regain Profitability.”
[13] Ibid.
[14] Ibid.
[15] Diderich, Joelle. “Karl-Johan Persson on Strategy and Culture.” Women’s Wear Daily. 15 February 2018. https://bit.ly/2RMYMr2
