The management of corporate stores is not as easy as it used to be, especially in terms of data analysis and decision support. It is often difficult to achieve the goals of brand promotion and performance improvement by relying solely on the efforts of central management. Therefore, how to effectively use store management data to improve the overall wisdom and execution of the team has become a pain point that managers urgently need to solve. Through the amoeba business philosophy, companies can empower individual store leaders to make their own plans, so as to motivate all members and promote the sustainable development of stores. Today, I will continue this business philosophy and analyze 5 practical store management solutions with you. Among them, we will talk in detail about how to quickly build an intelligent data analysis platform and practical operation plan for store management from 0 to 1.
First of all, I would like to share with you a business management information package, which contains an explanation of industry pain points and management solutions for multiple key business scenarios.
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1. Store management background
Students who are familiar with business management should know the thinking concept of amoeba management, which is to take the leaders of each amoeba as the core, let them make their own plans, and rely on the wisdom and efforts of all members to complete the goals.
The same is true for corporate store operations, which cannot achieve the goal of brand promotion by relying solely on the efforts of central management personnel, and must be moved in this direction by the entire team. It can be seen that the guidance of managers to employees is also very important, it is better to teach people to fish than to teach people to fish, how to let employees learn to find a breakthrough to solve the problem by themselves, so as to drive the development of the entire store is the key to management.
And often the most direct exposure of store operation problems is store operation data. Then we can use the management philosophy of amoeba and rely on the common wisdom of all members to make good use of these store operation data, fully release the potential of data, and ultimately improve store business performance.
2. 5 store management plans
This program will focus on sharing the following dry goods in the analysis of enterprise personnel intelligence data:
1. How can enterprises quickly build an intelligent data analysis platform for stores?
2. How to let store managers directly connect with the store database to quickly complete basic data analysis?
3. How to let store managers directly connect with the store database to quickly complete comprehensive data analysis?
4. How to let store leaders directly connect with the store database to quickly complete ad hoc data analysis?
5. How to make the intelligent data analysis results of enterprise stores realize team collaboration and sharing?
3. Effect and implementation description of the store management plan
Below, I will talk about the practical plan of store management in detail according to these five questions, including program description, scene construction, operation practice and program effect. Among them, the tool used in scene construction and operation practice is FineBI, which is the BI tool I am currently using, and I can easily grasp the store time-based business analysis, store comprehensive business analysis, store scrap commodity analysis, store integrated management cockpit, etc. There are also some visualization templates for store operations that can be obtained for free.
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1. How can enterprises quickly build an intelligent data analysis platform for stores?
This part of the work belongs to the preliminary data preparation work of the enterprise IT department, which requires the IT department to create a new data connection in FineBI data configuration - data connection management to ensure the success of the data connection test. Then, add the personnel management service package, add the relevant data tables, and establish the association between the tables (if it is a business package of the FineIndex type, you need to update the FineIndex data). After assigning the corresponding data permissions, users can directly perform ad hoc multi-dimensional exploration and analysis of these personnel data on the front end of the browser.
Taking this solution as an example, as shown in the figure above, we add data such as the store main file information table, comprehensive business analysis table, date information table, hour dimension table, scrapped commodity data table, and business day data source table to the personnel management business package, and establish the data association relationship between the dimension table and the fact table.
Finally, in the data configuration-permission configuration management interface, assign the permissions of the established store management business package to the relevant personnel of the store department, and add the personnel of the store department to the authorized list of BI editing users in the management system-user management.
2. How to let store managers directly connect with the store database to quickly complete basic data analysis?
a. Comparative analysis of store business periods
The first basic and important task of store operation is that store managers need to control the sales situation within the daily time frame and be familiar with the sales status of the store in each time period. Especially for the promotional activities held on specific holidays, we need to pay attention to and track them in time, so that we can better plan future activities and achieve better results based on past experience.
As shown in the figure above, we can compare the operation performance of stores on any date through the time control of FineBI, and track the real-time operation status of each store. In addition to the granularity of hours, the performance of stores in different weeks is analyzed and compared (as shown in the chart above, the overall state of store operations on Monday is obviously due to other weeks), so as to prioritize the time when the activity effect is the best.
b. Analysis of scrapped goods in stores
In order to better optimize the products sold in each store, we need to do some data analysis on the end-of-life products. As shown in the figure above, we can calculate the total amount of scrapped goods in the time interval through the dashboard, and at the same time filter out the top 10 scrapped commodities with a comparison bar chart, focusing on observation and supervision. In terms of the sales department, the trial pie chart counted the distribution of the scrapped amount of each sales department, and focused on the rectification and optimization of the stores with serious problems of scrapped goods.
c. Store information inquiry
For store information query, we can try the built-in map of FineBI for data distribution statistics based on geographical location, and display the detailed information of each store (store name, region name, central district name, community name, store address, etc.) through the detailed table component.
3. How to let store managers directly connect with the store database to quickly complete comprehensive data analysis?
a. Comprehensive business analysis of stores
Next, let's analyze the comprehensive sales data of stores, as shown in the figure above, we use the dashboard components to sum the total net revenue, and use the bar chart to count the turnover data of each store in descending order. For the net revenue data of each store within the date range, we use a stacked bar chart for statistics to compare and analyze the net revenue data of each store on a daily basis. For the analysis of sales structure (gross sales amount, net turnover amount, discount amount) and sales category (general merchandise sales amount, beverage sales gross amount, food sales gross amount), we can use line charts to compare and analyze data based on daily data.
b. Analysis of the achievement of store business objectives
As shown in the figure above, let's do data analysis to achieve the business goals of stores in each region of the enterprise. Since our store sales department has three levels of classification, it is most appropriate to choose a multi-layer pie chart to analyze the goal achievement rate under the three levels of sales department. In terms of the net revenue of each store, the target amount and net turnover data of each store are analyzed using the comparison bar chart of the combo chart, and the target achievement rate of each store is analyzed by using the line chart of the combo chart (target achievement rate = net revenue / target amount).
4. How to let store leaders directly connect with the store database to quickly complete ad hoc data analysis?
In addition to the above introduction to the production of some commonly used store data analysis in enterprises, the biggest feature of FineBI is that it can meet the needs of business personnel or business leaders for ad hoc analysis of data at any time.
For example, we only need to drag the transaction date field to the category axis, and drag the gross sales amount and transaction quantity to the left and right value axes, and then the grouping statistics can be automatically completed.
5. How to make the intelligent data analysis results of enterprise stores realize team collaboration and sharing?
The work of the enterprise is mainly completed by the division of labor and cooperation of the teams of various departments, and they need to communicate and cooperate well with each other. However, there is a problem of physical distance between stores and between stores and enterprises, and BI tools are needed to assist in the work.
The cockpit of the store management center shown in the figure above shows the company's product sales type statistics, store gross profit statistics, regional sales statistics, monthly sales/gross profit statistics and other core store management analysis indicators. Then, after the store management personnel have done a store management center cockpit through FineBI, they can actually share it with relevant departments or designated personnel very easily in FineBI.
For example, if I want to share it with the leaders of the enterprise, I can click to share the finished store management center cockpit template with Tom of the leadership department in the dashboard of FineBI, and then click OK. In this way, Tom of the leadership department can log in and receive the cockpit of the store management center shared with him by the store manager.
Four. summary
Everyone knows that after the popularity of online shopping, it is even more difficult to operate stores. So why are there so many stores opening in various places, in fact, it is drainage. Because stores are no longer the mainstream, it is necessary for store management to have a more efficient operation mode to assist enterprises to achieve correct drainage. That's why I say that store management has been moving closer to digital transformation. The traditional business model is not bad, but if it can be paired with some data analysis tools, it will definitely be the icing on the cake.
A consumer retail industry solution is given to you, according to the core solutions extracted by 100+ enterprises, subdivided into 6+ consumption scenarios. It's really quite quintessential, you can read it.
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