ZOZO’S AMBITION: CAN YOU QUANTIFY “COOL”?

ZOZO, Japan’s dominant online retailer of apparel and related lifestyle products, with approximately $1B in sales and $7B in market capitalization, has taken on a journey to recreate the world of apparel to the next level. [1] It aims to do so in two steps: 1) enable mass customization of private-labeled apparel products, and 2) quantify all aspects of what makes styles “cool” to become the global leader in product design. Will ZOZO be able to drive the industry in offering customized yet stylish product for the mass? What capabilities will differentiate who will win in the long haul?

ZOZO’S PLUNGE INTO MACHINE LEARNING

ZOZO, Japan’s dominant online retailer of apparel and related lifestyle products, with approximately $1B in sales and $7B in market capitalization, has taken on a journey to recreate the world of apparel to the next level. [1] It aims to do so in two steps: 1) enable mass customization of private-labeled apparel products, and 2) quantify all aspects of what makes styles “cool” to become the global leader in product design.

Machine learning serves a critical function for ZOZO, because it needs to predict and manufacture ergonomic, stylish products to satisfy each customer, based on given basic inputs around the customer’s body measurements. Originally an online retailer of apparel brands (think fashion-dedicated Amazon), ZOZO has risen to its heights by developing unparalleled strengths in user experience and product selection. [2] To take on the next challenge, the company has now introduced a just-in-time production for private-label brand products (named “ZOZO”) such as suits and jeans, such that it can create customized products within two weeks of order placement. [2]

 

ZOZO Online Retail Page

 

Launch of Customized, Private Label Brand “ZOZO”

Specifically, ZOZO relies on machine learning to substitute what had heretofore been done by experienced human beings: translating customer order specifications into physical patterns and defining corresponding manufacturing processes that balance design precision with order size, raw material yield, and speed. [4] The CAD program needs to define the optimum balance and translate it to the physical set-up of sewing machines. Despite the challenge, ZOZO stands in a unique position to leverage its vast data resource; in addition to its 23 million user information and outfit data, it is rapidly collecting data from production trials on body measurement data from over 500,000 ZOZOSUITs distributed globally (see next paragraph). [3]

 

IN SEARCH FOR QUANTIFYING STYLE

In the short term, ZOZO is very clear on its goal to becoming the world’s leader in mass customization of its private-labeled brand “ZOZO.” In the past year, ZOZO Technologies has made two attempts to bring this to reality, both with mixed results. First, ZOZO introduced a full-body suit, ZOZOSUIT, containing sensors of 150 varieties that collectively measure 15,000 different parts of human body. [2] When ZOZO failed to mass-produce the suit, it moved instead to a low-tech, dotted suits without sensors that could measure a human body using 360-degree selfie. In Oct 31, 2018, ZOZO abandoned the use of this suit entirely, claiming that it has “collected enough data to predict the needs of human body with simple information such as height, weight, age, and gender.” [4]

First ZOZOSUIT Launched

Second ZOZOSUIT Launched

 

In the long term, ZOZO is taking on an even more ambitious goal: “Quantify fashion, such that we can scientifically prove the ambiguous, captivating, and complex factors that makes fashion cool, cute, and beautiful.” [3] ZOZO has launched a separate company ZOZO Research to tackle this problem, actively recruiting experts in fields ranging from machine learning and pattern recognition to ergonomics and behavioral economics. In parallel, ZOZO is actively pursuing acquisition of external startups and ideas to accelerate the process.

 

OPPORTUNITY TO LEVERAGE THE “TRADITIONAL”

The mission of ZOZO is to “make the world a cooler place and thereby bring smiles to the world.” [4] ZOZO should consider making two critical adjustments in order to achieve its goal, both in the short term and the long run.

First, as ironic as it may sound, ZOZO needs to learn from its recent mistakes and make investments in hiring experienced designers and merchandisers who can fill the gap between the traditional manual production and ZOZO’s automated production. While I am optimistic about ZOZO’s long-term prospects, ZOZO should seek to accelerate its learning by working side-by-side with designers and merchandisers to articulate which factors make certain designs desirable and how to bring those designs to reality in (semi-)automated production sites.

Second, ZOZO should also seek to connect with customers in brick-and-mortar channel to receive feedback in the tangible space. Currently, ZOZO’s customer base is heavily centered around those in 20’s and 30’s who are completely tech-savvy. However, the target segment that really benefits from customization could be those in older age segments, who have higher economic freedom and are actively looking for apparel products that can make them look fit and stylish as they age. Building an equivalent of Nordstrom Local would enable ZOZO to gather in-person data that could further advance its pursuit of providing stylish products for the mass.

 

Nordstrom Local Store Display

 

CAN ZOZO WIN?

While ZOZO can leverage its presence in Japan to fuel growth into uncharted territories, startups such as Boston’s Stitch Fix seem to be ahead in applying machine learning to at least predict consumer demand. Simultaneously, Amazon also seems to be on the road to replicate its merchants’ products. Will ZOZO be able to drive the industry in offering customized yet stylish product for the mass? What capabilities will differentiate who will win in the long haul?

(796 words)

 

 

[1] “Company Information: ZOZO, Inc.” SPEEDA, 13 Nov. 2018, www.ub-speeda.com/company/

[2] “Reviewing the Path of King of EC Apparel: ZOZOTOWN.” WWD JAPAN, INFAS PUBLICATIONS, 13 Nov. 2018, www.wwdjapan.com/735174.

[3] “Learning from the Failures of ZOZOSUITS.” Commerce Online, shogyokai.jp/articles/-/1241.

[4] Fukachi, Masaya. “E-Commerce Expert’s Advice on How to Sell on Amazon, ZOZOTOWN, and Owned Websites.” Note, note.mu/fukaji/n/n662f4cfb26e8.

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14 thoughts on “ZOZO’S AMBITION: CAN YOU QUANTIFY “COOL”?

  1. This is something that is important to my company as well!

  2. Great article about Japan’s largest online retailer for lifestyle products.

    I really would be curious to understand how machine learning will take the company to the next level. The author mentions and argues that machine learning will help the company translate customer order specifications into corresponding manufacturing processes that balance design precision and speed. This does sound pretty remarkable – I would be curious to know exact examples as to how this will happen and by how much will the speed be improved.

    The author also other competitors, such as Boston Stitch Fix, I would be curious if we were to benchmark and progress the exact areas where machine learning has helped this company (and correspondingly how the algorithm has learnt/improved over time)

    This was a great article, opened by eyes to how machine learning can really potentially change the apparel industry, and have an effect on many aspects of the manufacturing chain.

  3. Fascinating article about the apparel industry! Dynamic changes are at play and the trend towards customization will definitely have large impacts on how we consumers select clothes and how companies manufacture them. For Zozo, I see their reliance on the suit as somewhat of a liability. Other companies, like MTailor, are using nothing but a phone and self-captured video to measure customers. Convenience will be key and I think online shopping will almost completely eliminate the need for brick and mortar (especially as VR becomes more capable). Being one of the first movers, I think they need to adapt their technology to make it as convenient as possible.

  4. Really interesting article about a retailer trying to bring innovation to the fashion industry. My main question for the company here would be around their mission to “quantify and articulate what makes fashion cool, and what makes certain designs desirable.” This approach seems to suggest that their is something intrinsic to items of clothing that make people want them. I wonder if this is a backward understanding of fashion – to me, fashion changes are more whimsical than intrinsic. For example, you can have any piece of clothing in the world, and if a celebrity wears it (e.g. Kim Kardashian) people will emulate that. The specific clothing does not have to have any intrinsic value. So, I wonder if using machine learning as a way to jump on trends would be a better use of resources than using machine learning to try to create trends. However, if I am incorrect and ZoZo is correct, this would be a revolutionary understanding of not just the fashion industry, but human psychology too.

  5. Looking sharp Issei…I find this incredibly interesting, but I am left wondering: doesn’t the quantification of cool automatically make it…uncool?

    I think the real challenge here is being able to offer customisation that ensures the customer doesn’t feel though they are re-buying the same experience or product. Change is, of course, the spice of life.

  6. Super fascinating article! I am very interested in the retail and consumer products industries so it is exciting to read about companies testing boundaries in those spaces.

    I think that your short- and long-term suggestions for ZOZO are very insightful. I find it pretty humorous how the need for actual human insight and actual physical space makes sense in this super advanced technological context. I feel that all too commonly people are quick to want to “replace” physical things and actual human input and in so many examples I have seen that in the end, tangibility and human-touch are immensely valuable.

    You may find the following article to be an interesting read – it touches on the concept of how technology can’t replace the “human touch”… at least not yet!: https://techcrunch.com/2017/01/15/technology-cant-replace-the-human-touch/

  7. While reading this I found myself thinking, “Of course Japan is printing jeans! Using a weird full-body suit to boot!”

    Having a brick-and-mortar store for consumers to give direct feedback and actually test out material seems like a good idea. I don’t think returning clothing is a common policy in Japan, so having the ability to try on ZOZO clothes in a physical space is very helpful. I also like the prospect of leveraging this technology to help the aging population…it could have CSR benefits, while being lucrative for them as well.

    One aspect of this I’d be interested in exploring would be beyond “normal” fashion. It doesn’t seem like ZOZO’s target demographic at all, but would more complicated designs like couture/high fashion or cosplay benefit from this? Right now those two areas of fashion design are highly labor-intensive and are essentially tailor-fit to individual users. Something like a ZOZOsuit that could actually capture a body’s specifications in one go could drastically reduce the amount of labor that goes into those products. Though that doesn’t at all seem to match the artisanal value proposition of couture, it could be an interesting application!

  8. The concept of automating fashion forward choices is quite counterintuitive. I think it is interesting to think that you could use machine learning to predict consumer preferences but I think that might be different than creating fashion. As machine learning tries to start making creative choices, its inherent code that relies on getting inputs that are similar to the things that it knows, will not allow for novel or creative ideas outside of variations of design or products outside of what it has already done. While that may help innovative for the mix or type of products in the short term, it will be less impactful for creating new or untested ideas or designs.

  9. Very cool concept! I think applying machine learning to optimizing mass customization in clothing is a huge competitive advantage. It’s crazy that we can customize cars and furniture to our liking, but most everyone buys their clothes generic. However, I question how well machine learning can determine new product design in fashion where new trends can’t be extrapolated from historical data. At most, I think machine learning can help provide a snapshot of what’s popular today. As such, I agree with your point that they should continue to work alongside designers who can act as the “training data” for future trends.

  10. The question that I have is what does mass customization do to their supply chain? It seems that mass customization would slow it down because they have to individually produce to certain measurements vs batch producing. Additionally, do consumers really need their exact measurements in their clothes or does mass producing sizing that is “close-enough” for most consumers work more efficiently? I think it’s great that ZOZO is trying to push the boundaries and predict fashion. I’m very interested to see where they go in the future.

  11. Interesting article, I’ve never heard of Zozo and I enjoyed learning about them. I’m curious about Zozo Research and how they plan to use machine learning to recognize patterns and predict trends. In this space, would Zozo plan to be more of a fast-follower like H&M and Zara, or would they try to be a trend leader? I feel like their ability to be a trend leader might be limited with just machine learning, but if machine learning is paired with designers, it could really be powerful in getting new trends to market quickly.

  12. Issei – awesome piece! Really enjoyed reading about ZOZO – an innovator in the e-commerce space that I have not heard of! I couldn’t agree more with your recommendation to explore brick and mortar. As a Warby Parker veteran, I’ve learned that there’s nothing more valuable than live customer feedback as the customer interacts with the product. There is so much information that is communicated “off the cuff”/informally to retail associates that companies can use to improve their product or customer experience. I’m really curious how ZOZO collects “accurate” size information – I’ve learned that consumers are generally misinformed about their sizing / how to measure themselves. I worry that inaccuracies with this data will be a detriment to their machine learning algorithm.

  13. I enjoyed the article, thanks for the read Issei! I can see the value proposition from a customer perspective, being the ability to get tailored products at (hopefully) reasonable prices, but I struggle with how the logistical side of business will work in traditional distribution. With ‘selection’ of a specific unit occurring much earlier in the product’s journey to the customer’s hands, the ability to ship bulk units, with future owners who are not tied to specific units, is removed – adding potentially significant shipping costs. By creating a meaningful relationship at the outset between manufacturer and customer, traditional wholesalers/distributors and retailers are cut out of the loop – it simply becomes a game of moving the box from A to B.

  14. I really appreciated this view into the manner in which another clothes retailer is pairing data science with trendsetting. However, as we saw in the case with Gap, I worry whether this total reliance on “data” to spot trends and tailor them to individuals is going to be actually effective. While a comparison to StitchFix is made by the author, it’s important to know that StitchFix’s algorithm and selection takes into account the fact customers have different tastes, and the algorithm is used in tandem with “stylists” who rely on a combination of algorithm outputs and their own judgement make the final recommendation on fashion pieces. Based on the description above, it almost sounds as if ZOZO is relying on a one-size-fits-all machine learning algorithm, which leads me to worry whether it will be as effective in understanding the diversity of trends consumers want.

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