Brief introduction:By introducing MAXCompute and Quick BI, Dadon solved the problem of database flash crash caused by data query in the past, and also built a sound report system to deal with the high-frequency and high-concurrency data analysis stably.

Dadong has about 500 new shoes a season. Each branch company under the jurisdiction of the region should report the order quantity of these 500 new products, and this number comes from the previous experience and the KPI issued by the senior management. The branch company determines the order quantity of each type, and then it needs to consider how to place the first place. What kind of shoes to put in what kind of store also depends on the experience. Only after a period of sales can the best-selling items be supplemented according to the operating conditions, and the amount of supplementing orders still depends on human experience or established rules.

At the beginning of the business, I made some radical decisions based on people’s experience, so that Dadong could expand rapidly in the market and make good achievements repeatedly. But as the business approaches saturation and more and more competitors emerge, experiential “aggressiveness” and “instability” become a kind of gamble, which, if not right, can lead to huge losses.

Only data can help the decision to achieve continuous and extreme refinement

Yichuang, a wholly-owned subsidiary of Dadong, is responsible for digital marketing technology and operations of Dadong’s main and sub-brands.

“Data also has different stages of development, just like driving a car. At first, you rely on the old driver’s familiar memory of a certain area, then you have a map that can be used to guide you, then you have digital navigation, and finally you realize autonomous driving. We are now at the stage of digital navigation with AI+BI. “Yi Innovation Retail General Manager Tang Yeqing said.

Quick BI helps with digital marketing and operations

In 2019, big data engine in Dadong Group pull, this is a 0 to 1 process.

By introducing MaxCompute and Quick BI, the report fetch is completely separated from the business system, which not only solves the problem of database flash crash caused by data query in the past, but also builds a perfect report system to deal with the high-frequency and high-concurrency data analysis stably.

Large view of Quick BI capabilities

Marketing management data portal set up 112 branches covering the whole

Digital marketing technology and operations team with professional ability and branch business personnel after investigation and pricing for goods shop, replenishment, and so on scene design more perfect set of index system, the Quick BI background to connect a variety of data sources, to accomplish the data modeling of complicated and calculation, output data report forms, and set up complete data portal.

Digital marketing technology and operation team to complete the construction, and then through the Quick BI space management, row-level permissions management, security of the data down to 112 branches, by branch goods department again along with the change of business needs to choose its own important data indicators, through the way of drag drag, zero output data of SQL statements, And personalized perfect marketing management data portal.

The marketing management data portal tests the data template

In the process of the operation of this mechanism, the data analysts of the digital marketing technology and operation team will receive the new indicator development needs proposed by the branch company, and find that some needs have unique perspectives, which are very worthy of reference. In order to encourage more people to participate in the thinking of digital operation, the group held the evaluation of the application of index system.

At the same time in the same region, all the branches are doing the same thing. For example, in the first store in summer, we need to spread the goods to each store with the support of data. At this time, what are the data indicators they care about most, what kind of statements they will produce, and what kind of value they will generate in the first stage?

This is a good time for business horizontal evaluation and experience exchange, as well as a good opportunity for digital marketing technology and operations teams to precipitate analysis templates.

The intelligent algorithm adjusts the price to optimize the inventory structure and improves the shipping efficiency

Quick BI can provide you with good data visualization and dashboard support. In addition to reporting and self-service analysis services, Quick BI also provides some AI capabilities.

The price of shoes will undergo different times of adjustment in its whole life cycle, and the reason of price adjustment and the price will be affected by many factors.

A price change is usually preceded by setting a target that includes sales volume and average price, taking into account changing circumstances such as temperature, weather, shelf time, holidays, and so on. Then combined with the existing store and commodity latitude business data, the algorithm module is used to calculate the pricing, and finally output the price adjustment model, as well as the business evaluation index and model evaluation index after price adjustment, for the review of the sales performance after price adjustment.

The set goals and dynamic scenario factors that need to be considered are variables that vary with each adjustment. This process is entered through Quick BI’s data filling function, which provides add, delete, modify, search, approve, and export functions. The input data is stored directly in the RDS database.

Calculate the price adjustment model together with the stored business data in the self-built intelligent algorithm model of Dadong, complete the price approval process, and import the model into SAP to generate price adjustment suggestions. Flexible data filling and modification can reinforce the closed loop from data adjustment to intelligence to analysis.

The business evaluation index and model evaluation index produced by the algorithm are built by Quick BI to build a visual report, presenting the completion status of the sales target after price adjustment and insight into the change of detailed data. Taking the spring price adjustment in Hangzhou area in 2021 as an example, the proposed adoption rates of the system output price adjustment is 75.7%, and the sales achievement rate after price adjustment is 95.6%. The automatic driving mentioned by Mr. Tang also appears on the horizon.

High-frequency daily and weekly reports for production efficiency improvement

The commodity department distributed in 112 branches is a highly data-oriented department, in which daily reports are produced to guide the decision-making of goods distribution, replenishment and transfer, and weekly reports are produced and reported.

In the past, IT took as little as 2 hours to submit data development requirements to IT headquarters, from development fetching to report making. Now, the “analyst” role in Quick BI is open to the merchandise team manager for self-help analysis, by selecting the appropriate visual chart or spreadsheet, using controls for conditional constraints, and simply dragging and dropping metrics to complete the daily in 30 minutes. Data results suitable for disclosure can also be widely pushed through the pin group to reach more people.

Push reports to the Nailing Group

Support rich data source direct connection

Openness is what Quick BI has always been about, and this also provides some insight into the types of data sources it supports. In the early stage, due to the cost factor, Daedong would choose a variety of databases to store different business data. As early as in the investigation of BI tool selection, it was found that many BI products could not support the existing database. Quick BI covers as many as 38 data sources, and iterations are fast, with new data source types added almost every time a release is made. Comes along with the development of the business, the more try, currently using DLA subscribe to their Allies SDK data lake buried point data, Allies, data were collected data will return to lake DLA, Quick BI lake can be directly connected data, real-time schedule of RT data read their Allies end, according to the online marketing scenario analysis demand, create a data set for online multi-dimensional analysis.

On the road of conforming to the development of The Times, Dadong Shoes has been in the forefront of actively exploring the transformation of digital intelligence. Centered on the user value, Dadong Shoes made full use of data and technical thinking to quickly insight into the potential needs of target customers, re-engineering the business model, reshaping the value chain, and truly realizing the “7-day fast fashion”.

More data function can be directly connected intelligent visualization platform Quick BI understand:

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