How do enterprises establish a new retail data index system?

How do enterprises establish a new retail (online+offline) data index system? Participation: Leng Yun Friends of Fashion Group 8 Time: 65438+2022122 October Owner: Leng Yun Participants:-Shanghai-looking for a job, Ruby is Bobo-Manchester City-merchandiser, Jay-Shenzhen-Finance, Yang Amin-Anyang-Children's Wear, Helen-Shanghai-Business. These sharing belong to the crystallization of collective wisdom. Does not represent Leng Yun's personal opinion. I hope this way can benefit more people in the industry! Many enterprises lack systematic thinking in data analysis, basically thinking where they want to go, lacking the consciousness of scientific data management and the concept of establishing index system. Without the establishment of these standards, enterprise data analysis is a castle in the air. | 1 | Current situation of enterprise data management: If you use three key words to describe your enterprise data management level, how would you describe it? Why? Although digitalization seems to have spread to all aspects of our work, life and production, the data management level of each enterprise is different, which needs to be improved and refined. The enterprise where Yunyou A Min is located mainly relies on the regency system for some extensive management. The data mainly includes some data of purchase, shipment, sales, basic outline and color. Generally speaking, the data management level of shoes and clothing enterprises is still in a state of "backwardness, primitiveness and neglect". At present, the company where Yunyou Ruby is located is still in the primary stage of singles. The number of stores is less than 20, mainly relying on the POS business sales data of offline stores. There is no other technology to collect target customer information, and enterprises still use basic data analysis methods. "The work I am responsible for is mainly composed of WSSI analysis (budget comparison analysis) and product category analysis. According to the established working methods of Retail Week, Zhou Yue, Spring and Summer, Autumn and Winter Promotion Month, the feedback process will be specific to the performance of each store in these aspects. Focusing on local optimization, small and micro companies have limited resources and are in the stage of changing systems. Our department is trying to optimize the operation process. Let's discuss and practice new ideas first. Help the company to have a more reasonable product line in the next quarter and improve the company's sales growth. " Not only the enterprises where the above two cloud tours are located, but also the domestic small and medium-sized companies are very primitive, especially the data management ability of domestic shoes and clothing enterprises is generally weak, and there are still few smart shops and RFID shops. Although China is in the forefront of the world in the stage of digital retail transformation, the price of smart devices is relatively high at present. Generally speaking, a 100 square meter store in China needs to be upgraded to a smart store, and each store costs about 200,000 yuan more. And everyone's evaluation of the company's data management is relatively low, because many management systems are oriented and the development cost is tens of millions. However, there are many practical problems in actual operation, that is, the system is not built by a group of people, and there is often a huge gap in communication between them, which leads to the inefficiency of the system. And sometimes it is difficult to unify customer information, especially different types of foreign customers or different types of customers. At the other extreme, although a lot of money has been invested in hardware equipment and information systems, it has no effect. This is mainly because it is not enough to rely solely on technology for digital transformation. If the thinking and management of enterprises do not change, only investing in hardware is equal to "burning money in vain." Therefore, digital transformation is carried out simultaneously by hardware and software (management). | 2 | Introduction to Data Analysis 1. The purpose of data analysis enterprises must implement data analysis and data management to specific people. If you are the person in charge of data analysis, how many points will you give yourself if you master the ability of data analysis? Why? Friends generally give themselves 5 points. "Because they understand the operational logic of major platforms, they have their own data capture and processing methods." Solid design foundation, able to put forward constructive suggestions from the perspective of research and development. But my ability to apply data tools and basic knowledge of statistics need to be strengthened. "These capabilities are also the capabilities that enterprises must pay attention to when doing data management systems. Why do you have to do data management and analysis? What is the purpose of analyzing data and what problems are solved? The answers of several friends gave us a new idea: business insight and creating value. Specific to the department is to measure the opportunities and risks of product series, predict what should have happened in advance, deal with differences, and provide data support for the design product planning and procurement budget for the next quarter. The purpose of data analysis is also to better support commodity planning and development and production departments. Follow up the chain links such as order/purchase/production/sales through data, and analyze the data status and abnormal reasons of each node in order to solve the problem more pertinently. To sum up, the purpose of data analysis can be divided into the following four points: (1) Describe the current situation-describe the analysis; These refer specifically to the degree of aggregation (average value, etc.). ) and dispersion (extreme value, variance value, etc. ) data (2) analysis reasons-diagnostic analysis; That is the reason behind the diagnosis phenomenon. For example, what caused the decline in sales. (3) Forecast the future-forecast and analysis; Sales forecast is a kind of forecasting behavior. (4) Improve future prediction and analysis. Some indicators performed poorly. How to improve your future performance? Take the cultural tourism industry as an example. Descriptive data is the average number of tourists this year or in the past five years; Forecast analysis is to infer the annual tourist volume in the next year; Diagnosis is to analyze the reasons for the abnormal number of tourists. Descriptive analysis In the analysis of these data, we should also understand some basic mathematical concepts and statistical knowledge, such as total, average, median, average variance (dispersion), quantile and so on. The median is the middle number. For example, what is the median of 1, 2,3,4,5? Quantile, for example, "1, 2, 3, 4, 5, 6, 7, 8" first arranges the values in order of size. If the above eight numbers are quartiles, they are a set of two numbers, namely: 1, 2/3, 4/5, 6/7, 8. Quantile combination of the top 25% data, the top 50% data, the top 75% data and the top 100% data, and then statistical evaluation data. Dispersion is actually very simple to understand. For example, from the age group, if the age groups are 35 15 years old and VS35 5 years old, which group of data has higher dispersion? What does it mean to be highly dispersed? At this age, +- 15 is definitely more dispersed than +-5 years old. For another example, the money sold this summer is also 100 SKU, with the highest quantity 10000 pieces, the lowest quantity 10 pieces and the average sales volume 1000 pieces. The maximum and minimum value here is the extreme value range, which represents the "degree of dispersion"; The average sales volume of 1000 pieces represents the "aggregation degree" of the data. Although this is only part of the data, we can basically know that this year's sales data is very scattered, which generally shows that the efficiency of commodity sales is very low. Sometimes everyone sells the same amount and the sales volume is relatively concentrated. These are descriptive statistical analysis, mainly to look at the data overview first. If the explosion is an explosion, but others may be almost equal to no sales, this is also a dispersion. Diagnostic analysis Then the analysis process of sales reduction is diagnostic analysis. The most basic statistical knowledge used in diagnostic analysis is correlation analysis. When store sales decline, the first step should be to list all the factors that affect sales. Sales can be regarded as "dependent variable y", and it is "independent variable x" that leads to its decline. Independent variables may be diverse, such as weather, passenger flow, marketing and products, and may be multiple factors, all of which belong to X. First of all, it depends on the strength of their relationship. These can be answered by statistics. Of course, there are many accidental factors, such as staff mistakes, but from the perspective of macro management, it is still necessary to focus on finding the relationship between X and Y, and you don't have to be scared by the word "statistics"! Now we don't need to memorize the complicated formulas behind statistics, which are already in the software (such as EXCEL and SPSS). We just need to know what formula or function to use under what circumstances. Forecast and analyze how many sales targets should be set next year? This is predicting the future. Nowadays, most people predict the sales of next year based on the sales growth of 20%-30% in the previous year. This method does not need much statistical knowledge. For specific sales forecast, you can go to the official account of Leng Yun Fashion WeChat to check the previous article "How to forecast sales scientifically". There is also a statistical prediction model, and people who specialize in data analysis will know how to do it. You can try it without being a professional. But many problems in data are not technical problems, but management problems. Such as data acquisition caliber, data quality and quality evaluation. Many enterprises have many problems in data collection at first. I don't know what data to collect, where to collect it and how to evaluate the quality of the data. There are even many man-made obstacles, for example, it is as difficult as climbing a mountain to get data across departments. Just like the previous software system, if the management is not in place, the software is just a display. 2. Business data analysis process What is your current analysis process? Do you want to analyze the data as soon as you get it The following is the flow chart of business data analysis: (