Senma leverages big data to boost the omni-channel transformation from fashion design to production

“This children's down jacket has been added to the shopping cart by 200,000 people before the double eleven.” “When this men's baseball shirt is on the e-commerce platform, the sales volume of the red model is more than five times that of the blue one.” ... Every day, the analysts of the Senma E-commerce Data Department will spend a few hours researching various user data information on the network platform, through data modeling, to make key elements such as styles, colors and fabrics of popular clothing this year. Predictive analysis makes design development and production more precise.

In the Internet era, domestic apparel companies are facing severe challenges, and traditional clothing sales channels are gradually moving toward a declining trend. A large number of domestic apparel companies are mired in the "closed shop tide" quagmire, but Senma has once again bucked the trend: in the just-concluded "Double Eleven" battle, the total number of orders of the Senma e-commerce network exceeded 3 million units, sales Breaking through 650 million yuan, compared with last year's 396 million yuan sales growth of about 64%.

In fact, beginning in 2012, at the beginning of the establishment of Senma E-commerce, the apparel industry as a whole was facing the crisis of “stock door”. Senma e-commerce initially entered the e-commerce channel with the goal of digesting inventory.

Relying on the positive change of Senma clothing and the brand appeal of online, in just one year, Senma E-commerce has completely bid farewell to the hat of “eliminating inventory”. After that, Senma began to try to leverage big data to promote the omni-channel transformation from fashion design to production. Shao Feichun, general manager of Zhejiang Senma E-Commerce Co., Ltd. told reporters that this year's "Double Eleven" Senma launched a series of "explosive models" clothing is the result of the use of big data.

The use of big data precision research and development and production has effectively reduced product inventory. "Relying on the data of the company's data department for nearly one year, the analysts have already prepared enough to obtain accurate user preferences through the previous store's multiple tests, collect consumer's favorite data for analysis, and calculate the explosion. The basic demand quantity of the product can even be accurate to the number of colors required for each product, so as to accurately package the order with the supplier.” Shao Feichun said that the product sales have increased linearly and also reduced the inventory of traditional clothing enterprises from the root cause. pressure.

“With the rapid development of e-commerce, traffic growth has been limited, and it is necessary to work hard on customer value mining and customer experience improvement.” Shao Feichun told reporters that in order to provide customers with a better shopping experience, Senma also relies on big data tools. The customer group has been accurately positioned and layered, combined with product features and user shopping habits, customized a series of membership benefits such as “free card for the whole year”, “collection plus purchase and award”, and realized thousands of people on the shop page. The shopping experience stimulates sales conversions, and the personalized page’s conversion rate is more than 30% higher than the benchmark page.

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