In this issue of Overseas Product Talk, we invited the design director of a large women's clothing ODM in Shenzhen to share their experience with using big data for selecting and modifying designs.
Oral Account | Sophia, Design Director of a Women's Wear ODM in Shenzhen
Organizing | Zhiyi Technology
"In the past, our designs relied on three things: exhibition catalogs, client briefs, and the designer's intuition." Sophia flipped through the 2022 proposal records: a total of 487 design drafts were produced throughout the year, with a pass rate of less than 30%. As an ODM manufacturer collaborating with SHEIN, many peers thought it was glamorous and easy, but Sophia knew that as a design team, they faced tremendous pressure every single day. As competition among ODM manufacturers intensified and SHEIN's selection requirements became increasingly strict, maintaining a high approval rate and reorder rate became increasingly difficult.
As a designer who has been deeply involved in the industry for nearly ten years, Sophia has experienced the era of rough-and-ready development and has also witnessed the red ocean competition in the apparel supply industry. Today, Sophia and her design team, with the help of big data, have found a brand new ODM contribution logic.
01 Days Trapped in the Information Cocoon
Last March, Sophia took a dozen or so puff sleeve dresses developed by her team to meet with a buyer. The buyer only looked through two pieces before frowning: 'Out of the top 100 bestsellers, 47 are puff sleeve dresses. Are you trying to compete on factory prices?'
What was even more heartbreaking was that two weeks later, Sophia saw almost the same style on SHEIN — from a factory in Jiangsu, priced $2 lower than their own cost. Situations like this were not uncommon in the past.
Although the selections were made according to the trends provided by SHEIN, competing manufacturers were all using the same references, and in the end, it all boiled down to competing on low prices. The goods were produced, but the profits were pitifully small.
In Sophia's words: 'We are trapped in a vicious cycle: relying on SHEIN to provide trends and reference selections → developing homogeneous products → facing price cuts; using our own design team to circumvent restrictions to copy social media influencer styles, find blue ocean products, and analyze popular trends, but it is time-consuming and labor-intensive, greatly reducing the efficiency of new releases.'
Until one day, in the cross-border clothing community, members of Sophia's team saw someone using the 'Overseas Product Exploration' market analysis tool and identified a blue ocean category in the women's clothing market in less than five minutes. They immediately scheduled a dedicated product explanation and demonstration.
Through the market analysis tool for overseas product exploration, ODM companies can select target sites, one or multiple sub-categories, choose a custom time period, and view the growth trends of each category within the target period. It can help design teams quickly identify which categories have been performing better recently and which demands are weakening.
In addition, for design details under specific subcategories (such as fabric, sleeve style, lining, etc.), overseas product exploration analysis tools can also provide clear attribute distribution and growth trends. In the [Attribute Concentration] section, you can also see the proportion of a certain design detail among top products in the SHEIN market, quickly identify design angles with overly fierce competition, and find a blue ocean for differentiated competition.
Through such analytical tools, the design team can quickly gain insights into the popular trends of the target site market and obtain richer design directions and ideas than the references provided by SHEIN buyers. After using it for some time, Sophia said, 'After conducting market research and trend analysis with big data tools, the feeling of being trapped in an information cocoon is no longer there.'
02 First time simulating styles with data
With a try-it-out mindset, the Sophia team started using the overseas sourcing tool. During the designers' weekly meeting, when Sophia first projected the interface of the overseas sourcing tool, everyone gathered around. "I still remember the first time we used it. We wanted to try finding references for women's tops and dresses from independent Western websites. Our first impression was that the product database had very rich labels for the apparel industry, allowing us to quickly find the styles we needed according to our company's requirements."
In the overseas sourcing [Product Center], by checking 'Do not view Shein' and merging the same styles from different regions, you can filter the newly listed and best-selling women's tops and dresses on independent sites across the network in the past 30 days. Designers can quickly find a large number of reference styles. It also supports further filtering based on various design details and attributes, and can be combined with some high-potential design elements selected through market analysis.
Not only independent websites, SHEIN's country-specific sites, Instagram, Pinterest, and other major overseas e-commerce platforms' styles can also be quickly and accurately filtered in a similar way. With the help of overseas product exploration, designers no longer need to browse massive amounts of image sources or switch between various websites and platforms; one-stop 'precise style searching' has become possible.
In addition to offering a richer variety of search methods and more image sources, the [Smart Image Search] feature has also become a favorite useful tool of the Sophia team. In the past, designers might find a style they liked on Instagram, but they didn't know which brand or store it originated from, nor did they know its recent sales.
By using [Smart Image Search], designers can link any style they find when selecting designs to the original source, obtaining accurate and objective sales data, and confirming whether the style has already been listed on SHEIN. Additionally, by searching for similar styles, they can also get the sales of similar styles across various sites, allowing designers to gain more inspiration for style modifications.
Sophia: 'Previously, it was time-consuming and laborious for designers to search for images extensively, and the process was quite blind. Using Overseas Collection to select styles can avoid overly subjective assumptions, and it frees up designers' time, allowing them to focus more on design modifications.'
03 Blockbuster Analysis Class
After resolving the issue of selecting styles, Sophia and her team also began trying to use big data to address the problems of low hit-rate products and few repeat orders. In past design processes, designers would find multiple directly referenced styles, but how can they determine which elements or attributes of these best-selling styles made them popular products? It is rare to create a hit product, yet it's impossible to clearly analyze the factors of success. Is it the style? The color? The fabric? The craftsmanship? The price range? ... These various difficulties make it hard for hit product design to form replicable experience.
Every time a new product is launched, it is still a completely new 'gamble.' However, by using overseas product research, it becomes possible to 'dissect' the genes of a popular item. After finding a bestselling dress on SHEIN's US site with monthly sales of 3,000 units, through overseas product research [Product Details], one can perform [SKU Analysis] and discover that the dress's Dahlia Red and Blue color options account for nearly 90% of sales, with sizes S and M making up more than 60%. In the product review analysis, one can immediately see that negative review keywords are concentrated on 'sheerness.'
By using the cross-analysis tools for overseas fund exploration, Sophia and her team discovered that the design combination of 'thin straps and flared hem' was experiencing significant market growth at the time. Therefore, Sophia decisively decided to stock double quantities of dark pink and blue, thicken the lining by 40%, retain the thin straps, and incorporate the flared hem design.
As a result, the upgraded version sold out in the first week, with reorder volume 40% higher than the original. 'In the past, making a hit product was like gambling or drawing lots; it always depended a bit on luck. Now the data gives the answer directly, like an open-book exam,' Sophia said to us with a smile.
04 Conclusion
With the 'market insights, multi-platform selection aggregation, and hot-selling product gene analysis' combination provided by Overseas Explore, the Sophia team is still exploring new boundaries and new possibilities in 'big data clothing design'.
Sophia's colleague discovered another new blue ocean product yesterday: 'Sophia, do you remember that lace top that was snatched by xx competitors last year? Yesterday, I found through overseas product exploration that it's gaining a little popularity again this month on an independent UK site, and there are still not many similar styles on SHEIN.'
He winked at Sophia, 'How about it? This time, shall we be the first to create a blockbuster product?'
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For cross-border clothing selection/finding influencers, just use [Overseas Product Hunt] ↓↓↓
