Quick BI: Lower the barriers to use, data navigation for Dadong Footwear's 8,000 stores

Introduction to by data query in the past, and also built a complete report system to stably respond to high-frequency and high-concurrency data analysis.

Dadong Shoes has about 500 new products in one season. Each branch under the jurisdiction of the region has to report the order quantity of these 500 new products, and this number comes from past experience and KPIs issued by the senior management. The branch company has determined the order quantity of each style, and then it is necessary to consider how to shop first, what kind of shoes to put in what kind of store also rely on experience to support. After a period of time, sales can be post-positioned to replenish the best-selling products according to the operating conditions, and the amount of replenishment is still based on human experience or established rules.

In the early stage of entrepreneurship, we made some more radical decisions based on people's experience, allowing Dadong to expand rapidly in the market and achieve repeated successes. But when the business approaches saturation and more and more competitors emerge, the empirical "radical" and "instability" will become a kind of gambling. Once the gambling is not accurate, it will face huge losses.

Only data can help decision-making achieve continuous and extreme refinement

Dadong established a wholly-owned subsidiary, Yichuang, responsible for the digital marketing technology and operation of Dadong’s main brand and sub-brands.

"Dataization also has different stages of development. Just like driving a car, the road recognition is based on the old driver's familiar memory of a certain area, and then there is a map that can be found according to the map, followed by digital navigation, and finally the realization of automatic driving. We are now at the stage of digital navigation using AI+BI." said Tang Yeqing, general manager of Yi Innovation Retail.

Quick BI helps digital marketing and operations

In 2019, the big data engine will be pulled through by Dadong Group, which is a 0 to 1 process.

Through the introduction of MaxCompute and Quick BI, report access is completely separated from the business system, which not only solves the problem of database flash crashes caused by data queries in the past, but also builds a complete report system to stably respond to high-frequency and high-concurrency data analysis .

Quick BI capability big picture

A marketing management data portal was built to cover 112 branches

After thorough investigations by the professional digital marketing technology and operation team and branch business personnel, they designed multiple sets of perfect indicator systems for the first shop, replenishment, price adjustment and other scenarios. connects multiple data sources in the Quick BI background. Complete complex data modeling and calculations, output data reports, and build a complete data portal .

After the digital marketing technology and operation team have completed the unified construction, they then use Quick BI's space management and row-level authority management to safely transfer the data to 112 branches, and then the branch commodity department can independently select important ones according to changes in business needs. Data indicators, through the drag-and-drop method, zero-SQL output data reports, and a personalized and perfect marketing management data portal.

Marketing management data portal test data template

During the operation of this mechanism, the data analysts of the digital marketing technology and operations team will receive new indicator development requirements proposed by the branch, and find that some requirements have unique perspectives, which are worth learning from. In order to encourage more people to participate in the thinking of digital operation, the group held a selection of the application of the indicator system.

In the same time period in the same region, all branches are doing the same thing. For example, for the first store in summer, everyone needs to use data to store goods in various stores. At this time, what data indicators are they most concerned about, what kind of reports will be produced, and what value will be produced in the first shop?

This is a good time for business horizontal evaluation and experience exchange, and it is also a good opportunity for digital marketing technology and operation teams to precipitate analysis templates.

Intelligent algorithm adjusts prices, optimizes inventory structure, and improves shipping efficiency

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

The price of footwear will undergo unequal adjustments throughout its life cycle, and the reason for the price adjustment and the price to which it is adjusted will be affected by many factors.

Before the price adjustment, a target is usually set, including sales volume and average price, and then some changing scene factors are taken into account, such as temperature, weather, shelf time, holidays and so on. Then combined with the business data of existing stores and commodity latitudes, the algorithm module is used to calculate the pricing, and finally output the price adjustment model, as well as the business evaluation indicators and model evaluation indicators after the price adjustment, which are used to review the sales performance after the price adjustment.

The set goals and the dynamic scene factors that need to be considered are variables that are different each time the price is adjusted. This process is input through Quick BI's data reporting function, which provides functions for adding, deleting, modifying, checking, and approving, and exporting. The entered data is stored directly in the RDS database.

Together with the stored business data, the price adjustment model is calculated in Dadong's self-built intelligent algorithm model, the price approval process is completed, and the model is imported into SAP to generate price adjustment suggestions. Flexible data reporting and modification can strengthen the closed loop from data adjustment to intelligence to analysis.

The business evaluation indicators and model evaluation indicators produced by the algorithm are visualized by Quick BI to present insights into the completion status of the sales target and changes in detailed data after the price adjustment. Taking the price adjustment in the spring of 2021 in Hangzhou as an example, the adoption rate of the proposed price adjustment for system output was 75.7%, and the sales volume achieved rate after the price adjustment was 95.6%. The automatic driving mentioned by Mr. Tang also appeared.

High-frequency daily and weekly reports to improve efficiency

The commodity department distributed in 112 branches is a highly data-based department. Daily reports are produced here every day to guide the decision-making of distributing, replenishing, and transferring goods. It also produces weekly reports every week for upward reporting.

In the past, data development needs to be submitted to the headquarters IT, from developing data access to preparing reports, it took as little as 2 hours. Now, the "analyst" role in Quick BI is open to product team managers for self-service analysis. By selecting suitable visual charts or spreadsheets, using controls for conditional constraints, the daily report can be completed within 30 minutes by dragging and dropping indicators. Data results suitable for disclosure can also be widely pushed through Dingding Group to reach more people.

Dingding group push report

Supports direct connection of rich data sources

Openness is the direction that Quick BI has always insisted on, which can also provide insights on the types of data sources supported. In the early days, due to cost factors, Dadong would choose a variety of databases to store different business data. As early as the BI tool selection survey found that many BI products could not support existing databases. And Quick BI covers as many as 38 data sources, and the iteration speed is very fast, almost every release will add data source types. With the development of its business, Dadong has begun more attempts. Currently, it uses the Data Lake DLA to subscribe to the Umeng SDK to bury point data. The data collected by Umeng will flow back to the Data Lake DLA, and Quick BI can directly connect to the data lake. Read the real-time RT data list of Youmeng, and analyze the needs of marketing scenarios online to create a data set for online multi-dimensional analysis.

On the road of adapting to the development of the times, Dadong Shoes has been at the forefront of actively exploring the transformation of digital intelligence. Focusing on user value, Dadong Footwear makes full use of data and technical thinking to quickly gain insight into the potential needs of target customers, reengineer the business model, reshape the value chain, and truly realize the "7-day fast fashion".

More data functions can be directly connected to the intelligent visualization platform Quick BI understand: https://dp.alibaba.com/product/quickbi

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