How Can Retailers Respond To Data As Quickly As Zara Does?

The traditional retail industry used to be "Xu San more" - more customers, more goods and more stores.

Today, we need to add a lot more data.

The development of the Internet and the blowout of the electricity supplier caused the retail industry to suffer from a closed shop in 2016. However, even in the field of clothing retailing, where the entity and the electricity supplier competed fiercely, there was a typical profit from big data.

In 2017, if physical retailing wants to go out of the way, it must pform and become a data management enterprise.

According to the world clothing shoes and hats net, in early 2016, business super giant.

Announced that the world closed 269 stores, involving tens of thousands of employees; Wanda Department stores closed nearly forty stores; Tianhong Department store, sunshine department stores, Martha department stores also fell; at the end of the year, the circle of friends was crazy about the list of physical stores killed, in addition to the previous ones, Messi, Carrefour, McDonald's, Metersbonwe, Lining and other famous.

It's also on the list.

At the same time, as the parent company of many fast fashion brands, such as Zara, Pull&Bear and Bershka, Inditex surrendered its net profit by 10% in 2016, with sales reaching 23 billion 310 million euros, becoming the highest record in history.

This upward trend continued to 2017. Sales of Inditex group's brands, which opened for more than 1 years, rose by 8% in the first six weeks of the fiscal year.

H&M, however, has lost 1% of its results.

Lang Xianping, a famous economist, once pointed out: "successful enterprises after 2000 are not created by innovation, but by rapid reactions."

The success of Zara is a test of this.

Zara has its own rapid-fire production system (production), which relies on the PDA in the hands of every store manager to deliver to the head office for production and purchase. The data generated in POS, ERP, SCM, CRM and CAD are never isolated from each other.

Therefore, both inventory management, production mode, and distribution of stores are like a rocket. It is not easy to copy other brands.

Why is it called fast fashion, but H&M, GAP is fast but Zara? Inditex CEO Pablo Isla said in an interview: "there is no secret. We just did a quick response to the data."

Then, how did retailers look like in 2017?

Like, do you respond to data quickly?

According to the characteristics of the retail industry, we summarize the five major trends of data analysis in the retail industry for your reference.

1. Full source data integration

There are many stores, customers and Sku in retail industry. The problem often faces is that different data in different systems are not related to each other. It is difficult to find hidden problems or business opportunities from data. It is even more impossible for Zara to store the turnover rate in the industry according to information feedback from each store, which is 3~4 times higher than that of other brands.

Decision makers usually need to see a certain indicator to guide decisions, but it takes a week or even longer to make a request to IT response.

The data has expired and can only be used for reopening.

In 2017, more and more retail practitioners will benefit from the full source data integration tool, which will integrate data scattered by retailers in local documents, cloud data and third party systems, and use online data processing tools to extract key indicators to form a customized data set.

From the demand to get the report, it will take hours or even minutes.

Managers can use first-hand data to make sales layout and adjust marketing strategies.

2, data analysts are no longer monopolized by professional analysts.

The retail industry is all inclusive, with tens of thousands of global employees and a small shop for individual soldiers.

In the past, data analysis, data extraction, data cleaning, modeling and other processes must be carried out by professionals, and usually a professional data analysis department should be deployed.

But in 2017, even if the convenience store owner could plough the data, it was entirely due to the birth of a visual tool for interactive data - just clicking and dragging the mouse, it could generate a variety of charts, highly chiral, and anyone could operate.

Moreover, data visualization integrates the essence of human brain science, management science and information science, which can stimulate users' business intelligence and drive decision-making quickly.

It can be said that in this era, we have the ability to embrace this highly specialized, yet more easily operated data analysis tool.

Moreover, because of relying on the cloud, this tool does not require local deployment, and does not need to purchase hardware, so the price is relatively low.

Because of the birth of this tool, advanced analysis will no longer be monopolized by professional analysts. Small and medium-sized retail enterprises can also use data driven management to guide their work with accurate data. And whether they analyze or share with them, visualized charts are more intuitive and more conducive to decision-making than figures or text reports.

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3, enterprise's response speed keeps pace with data production speed.

The response speed of enterprises can not keep up with the production speed of data, which is the key issue of data-driven operation.

Take the speed war between Zara and H&M, for example, two enterprises see the same time of T show (inspiration), but H&M takes 3 months or so from typing to shipping, and Zara only takes two weeks.

If information can not be digested and utilized at the first time, its timeliness can not be guaranteed, and the world's ever-changing and outdated information is equivalent to the wrong information.

How to make the response speed of enterprises keep pace with the production speed of data?

First, the first hand data need to be analyzed directly.

In the past, business intelligence needed a lot of repetitive work. Even though the weekly reports and monthly reports in the same format, each demand represented a series of tasks. But in 2017, as long as the data processing tool was used to make a data flow and produce an analysis Kanban based on the data flow, the next workload was to click on a mouse and choose to update the source data automatically.

Second, first hand analysis needs direct decision makers.

The heads of enterprises at all levels and functions are no longer based on a complex report to split up what they need most; they only need to own a management Kanban and select real-time updates.

Finally, a single decision requires direct execution teams.

Whether it is production, supply, distribution, or operation, it is the first time to get the decision and see the data that support decision making to trust decision, execute the decision finally, and complete the whole process from data to action.

Only data analysis decision execution all seconds response can ensure the enterprise's response speed and the production speed of data.

4. Mobile analysis accelerates the development of retail industry.

Can data analysis and sharing be implemented only on the PC side? If the recipient of the report is the CXO who has been rolling around all the year round, the sales, the buyer, and so on, can not play the role of nine to five in the computer, then can the good tools be unable to achieve "instant response"?

In those days, Zara customized PDA for every store manager, which ensured the barrier free circulation of information.

We can not help but imagine that if every key node in the retail industry chain, from CXO, production departments, suppliers, distribution centers, stores, etc., can share information through mobile terminals, many problems will no longer be a problem.

In 2017, retail entities will integrate with big data. This is not just to say that traditional retail businesses have to shift their positions to online shopping centers, but that stores can also use data to optimize the entire business chain.

One of the most critical tools is communication and collaboration tools that support mobile end analysis.

With it, CXO people can also make decisions based on data output at the airport. Operators can adjust the promotion channels based on data all day, and the sales team can take out mobile phones anytime and anywhere, and display the advantages of their products based on data.

Mobile analysis tools break through the limitation of time and space, and help enterprises to manage data in all directions.

5, the retail industry is going to a technology intensive industry.

The development of machine learning and artificial intelligence also brings opportunities for retailing.

H&M "recycling old clothes", in addition to environmental protection, also has the purpose of saving production resources.

Zara uses machines to plan the use of each piece of cloth at the source of production, ensuring that the tailoring is the most economical.

Now, AI, machine learning and so on may be a bit distant for some traditional retail enterprises, but maybe in a few years, AI will enter a large number of enterprises to complete some daily work instead of manual work, and lead the retail industry from "labor-intensive" industry to "technology intensive" industry.

At that time, the increments of useful data will be even more impressive than today. Are we ready to welcome new wisdom?

Coming with you into the future

Catering to the five major trends of retail industry development, we provide five functions of data source integration, data visualization, mobile terminal communication and collaboration, data instant updating, and mass data second level response, breaking the data barriers between multiple customers, multiple single products, multiple stores and multiple links.

We believe that big data never stand on the opposite side of the entity, but when someone takes off with big data, the big army is still hesitant in front of big data.

In fact, as long as we have the right tools, big data will be treated equally and lead us into a more exciting future.

Zara's "speed" will eventually become the norm of the physical retail industry.

More interesting reports, please pay attention to the world clothing shoes and hats net.