Semir Leveraging Big Data To Boost The Full Channel Pformation From Clothing Design To Production

"This children's down jacket has been joined by consumers before double eleven.

200 thousand cars, "this"

When the electronic business platform is sold, the sales of the red money is five times more than that of the blue one.

Every day, analysts of Semir's electricity data Department will spend hours studying all kinds of user data information on the network platform. Through data modeling, we will make a prediction analysis of the key elements such as style, color and fabric of this year's fashion.

R & D and production have become more precise.

In the Internet era, domestic garment enterprises are facing severe challenges, and traditional clothing sales channels are gradually declining.

While a large number of domestic garment enterprises are bogged down in the mire of "closing shop tides", Semir is once again against the trend. In the "double eleven" battle that just ended, the total number of Semir's total electricity network orders exceeded 3 million orders, and sales exceeded 650 million yuan, compared with 396 million yuan last year, sales increased by about 64%.

In fact, from the beginning of 2012, when the Semir electric business was set up, the apparel industry is facing the crisis of "inventory door". Semir electric business initially entered the business channel through the goal of digesting inventory.

Relying on the positive changes of Semir costumes and the brand appeal of the online brand, in a short span of a year, Semir electric business has completely bid farewell to the "elimination of inventory" hat.

After that, Semir began to try to borrow big data, boosting the pformation of the whole channel from clothing design to production.

Shao Feichun, general manager of Zhejiang Semir Agel Ecommerce Ltd told reporters that this year's "double eleven" Semir launched a series of "explosion" clothing is the result of the use of big data.

Using big data to research and produce accurately, effectively reduce product inventory.

"Relying on the data of the company's data department for nearly a year, the analysts have already made enough preparations to get accurate user preferences and collect consumer's favorites data through the previous shop tests. After analyzing, we can figure out the basic demand quantity of the products, and even accurately determine the number of colors needed for each product, and then accurately package the order to the suppliers."

Shao Feichun said that the product sales volume increased linearly, but also reduced the inventory pressure of traditional clothing enterprises from the root cause.

"With the rapid development of electricity providers, traffic growth has been limited, and more needs to be done in customer value mining and customer experience enhancement."

Shao Feichun told reporters that in order to provide customers with a better shopping experience, Semir also conducted a precise positioning and stratification of customers based on the big data tools, combined with product characteristics and user shopping habits, and made a series of membership benefits such as "year-round free card", "collection plus bonus" and so on, and realized thousands of shopping experience on the shop page to stimulate sales pformation, and the conversion rate of the personalized page was increased by more than 30% compared to the base page.