New retail refers to the combination of online and offline sales models, the use of digital technology and data analysis methods to attract consumers through online platforms, and provide more personalized and convenient shopping experience in offline stores.
New retail no longer regards online and offline as two independent sales channels, but integrates them into a complete sales ecosystem. New retail makes full use of digital technology to upgrade the retail scene, create an intelligent, personalized and scene-based shopping experience, and improve consumers' shopping experience and shopping satisfaction.
New retail adopts a variety of technical means, such as artificial intelligence, big data analysis, Internet of Things, etc., through data collection, data analysis, etc., to optimize commodity inventory management, logistics distribution, after-sales service and other links, and improve the efficiency of the supply chain , reducing costs. At the same time, new retail also adopts various marketing methods such as social e-commerce and O2O, and integrates factors such as social interaction and user word-of-mouth into marketing activities, which improves user stickiness and word-of-mouth effect.
How to transform traditional retail?
In the digital age, supply chain management will become an important key to the transformation of the retail industry. Enterprises need to optimize supply chain management through digital means to improve the efficiency, transparency and response speed of the supply chain.
The composition of the supply chain includes five major parts: suppliers, manufacturers, distributors, retailers and consumers. The specific supply chain process is as follows:
Whether the supply chain analysis can be done well will directly affect the net profit level of the enterprise. In other words: If the supply chain is not well done, don't even try to make money.
The following will take you to build a supply chain index analysis system from the five sectors of procurement, production, warehousing, sales, and transportation.
1. Purchasing management indicators
2. Production management indicators
3. Sales management indicators
4. Warehouse management indicators
5. Transportation management indicators
Generally speaking, data analysis will use basic Excel pivot tables, chart visualization and other basic operations. If you want to do some advanced data analysis and don't know spss, Python, etc., then you can use BI tools. One is that it can perform big data analysis, and the other is that it is easy to get started.
Data analysis tools
1. Visual display of existing data
Usually, the data of an enterprise is complex, and it is impossible to identify information at a glance. BI is to display the data generated in the daily operation of the enterprise or the pre-made reports in visual ways such as column charts, line charts, funnel charts, etc., allowing business personnel to identify important information. In addition, through functions such as drilling, linkage, and jumping, you can further view further information based on the indicator dimension and find the root cause of the problem.
As can be seen from the dashboard in the above figure, the analysis content is closely integrated with the daily work of the business department. For example, sales personnel care about sales amount and payment amount, HR care about turnover rate and entry time limit, market personnel care about market development rate, The number of cooperative customers, etc., the analysis of each business department will basically involve complex calculation logic and profound knowledge of business indicators. It is impossible to rely solely on IT personnel to make fixed reports or business personnel to view business system data. This is further emphasized here BI is a self-service analysis tool for business personnel and data analysts.
A dashboard like the one above is not only a visual display, but also a business analysis idea. Business personnel can have a clear and direct understanding of the business and make decisions based on data more reasonably.
2. Monitoring and early warning of current data
Normally, we will detect "abnormalities" through color changes or early warning line settings. When business personnel find abnormalities in the data from the visual charts, they need to analyze them purposefully, and find possible problems by checking associated reports and drilling reports of different dimensions.
As shown in the picture above, the three-guarantee fee for a certain product is abnormal. After drilling through the map, it is found that the three-guarantee fee for Jiangsu Province, Wuxi City, and Xishan District is abnormal. At this time, we can use the three-guarantee fee for this area. To explore whether it is because the quality of this batch of products has problems, or because of the specific usage scenarios in this region.
In the end, business personnel gradually formed a relatively reliable and solidified analysis model through one or more dimension and index chart construction. Business personnel at this stage no longer passively accept the information reflected in the chart, but use "abnormal" data to locate a business problem behind it. Data and business start to have a direct correspondence at this level. At this time, you can use The logical relationship between the data charts is used to find solutions to improve the operating efficiency of the enterprise.
3. Scientific prediction of future business.
Prediction of future business is usually realized through modeling analysis. Business personnel who are proficient in business changes can find out potential problems in the business or adjust ways to achieve a better solution by making a suitable visual model, and then feed back the business Decision-making forms a benign process. Business modeling pays more attention to autonomy and exploration, and can maximize the role of BI.
① Shopping basket analysis model
By studying user consumption data, we can associate different products and mine the relationship between different products. Classic cases include: beer and diapers
② DuPont analysis model
The DuPont analysis method decomposes the company's return on net assets into multiple financial ratios step by step, and deeply analyzes and compares the company's operating performance.
③Pareto analysis model
According to the main characteristics of things in terms of technology or economy, classify and queue up, distinguish the key points from the general, so as to determine the management method differently. For example, find 20% of the product categories that can bring 80% of the revenue, so as to arrange different marketing strategies.
④ AARRR analysis model
By realizing the five indicators of user growth: customer acquisition, activation, retention, revenue, and dissemination, tracking the loss of users during the operation process, forming a closed-loop model of the entire user life cycle from customer acquisition to dissemination and recommendation, and continuously expanding the user scale, achieve sustained growth.
More complete information can be obtained by replying to the e-commerce company in the background !