What does it mean to be a data analyst for clothing

The data analysis of general clothing companies can be divided into two categories: one is sales data analysis, and the other is product data analysis.

First, sales data analysis can be done very finely, or it can be regional in nature.

In general, it can be the data report of the regional sales market, the same as the competing brands, and the changes in the data of the same regional market, such as the comparison of year-on-year and month-on-month data. Small aspects can be specific to the individual sales. Through the analysis of regional and individual sales data, it is concluded that sales individuals need what training, promotion, and motivation they need.

So as to provide data support to the operation department and data analysis to the goods department. The operation department (or marketing department) formulates a marketing plan based on data analysis, and the goods department formulates a product allocation strategy based on data. The company's top management uses data analysis to develop development plans.

2. Commodity data.

It can be divided into several links. 1. Data analysis of new sales. Adjust the rhythm of loading through data tracking, adjust the product adjustment plan, formulate the best goods, and control the discount rate.

2. Inventory data, monitoring the inventory situation, inventory early warning 3. The specific style details are sold in style and color statistics, so as to make a memorandum of goods for buyers, provide data support for the display department, and the display department refers to the unsalable goods and adjusts the physical store display techniques. 4. Analysis of sales data and purchase data over the years, verify and adjust the rhythm of delivery, seasonal change factors, and purchase quota.

It should be a statistical indicator of the number of clothes sold in a quarter or month, as well as the total price and profit.

Food, clothing, housing and transportation are the four major elements of people's livelihood, and with the development of the economy, people's basic life demands have also improved, especially as the first "clothing".

The traditional marketing model of the garment industry can no longer meet the changing needs of modern consumer users, and the fiercely competitive market environment makes the garment industry gradually diversify and develop, refine operations, and use data management to achieve smart marketing.

Challenges faced by the apparel industry

In today's environment, the marketing expenses of the garment industry are constantly increasing, and the profits of enterprises are becoming increasingly meager;

Clothing is a commodity with a short epidemic cycle and strong seasonality, which is easy to cause an imbalance between production and sales, and there is a risk of high inventory;

Under normal circumstances, there are many clothing stores and commodity SKUs, and the data volume is huge, resulting in the non-synchronization of financial business information;

The attributes corresponding to clothing products are relatively complex, and the combination analysis of various attributes is flexible and changeable.

Consumers will "label" themselves, but also to the clothing brand "label", how to make goods, channels and consumer reputation "label" match, is the clothing industry needs to solve the problem urgently.

Key points of data analysis in the apparel industry

Figure - Apparel industry index system.

1. From the perspective of ** chainThe data analysis of the garment industry is mainly based on purchase, sales and inventory, among which the inventory-to-sales ratio and the sell-out rate are two important analysis indicators.

Figure - Inventory-to-sales ratio.

Figure - Sell-out rate.

2. The delivery and payment of the first situationIt is also neededReal-time monitoringThis is also an important indicator for financial data analysis.

Figure-Real-time monitoring of delivery and payment collection.

It is also required for goods and storesDo refined, multi-dimensional analysis to trace the sourceto prepare for the next stage of precision marketing.

For example, slowstock is one of the simplest, most intuitive, and most important data factors in sales data analysis. A best-selling model is a product that has sold a lot of money for a certain period of time, while a slow-selling model is the opposite. Bestsellers are not an inherent attribute of the product, but a dynamic attribute that changes with the change of business and time cycle, and the reasons should be analyzed from the change.

Figure - Reason Exploration.

Analyze the value of your data from the cloud

Open up online + offline + logistics data, and fully share consumer-centric data such as membership, payment, inventory, and service;

Real-time response to massive data, dynamic intelligent analysis, and meeting the changing needs of consumers;

Track sales in real time, understand market demand dynamics, and make timely allocation adjustments to goods, so as to reduce inventory risks;

Optimize the first-chain management process, improve the market response rate, and maximize the utilization of resources;

Track and analyze consumer purchase behavior, provide personalized and accurate operation services, so as to improve marketing conversion effect, improve consumer loyalty, and reduce marketing expenses;

Through the sales model, explore scientific pricing strategies to enhance the competitiveness of goods.

Summary

This is an era of "service wins", it is particularly important to accurately understand and quickly meet the needs of consumers, and the digital key analysis cloud can provide one-stop big data analysis solutions for enterprise business scenarios, help the digital transformation of apparel enterprises, and improve the ability of front-line business decision-making.

There are mainly the following aspects:

One is the product. Zhi manager service dao

The domestic product version manager does not understand data analysis, and the competitive intelligence analysis and product agility testing of new product rights need to be completed with the help of data analysts, and the later product iteration optimization still needs data analysts to collect user behavior, habits, evaluation and other data to complete;

The second is to serve the operation, and the user flow, customer relationship management, etc. in the product operation need to be completed with the help of data analysts; Third, the company's data formulation and standard construction, data integration of various departments, data management and other work need to be completed by data analysts;

Fourth, data intelligence and data ** serve the high-level.

From the above four aspects, business analysis ability and business knowledge ability are particularly important, this time is to test the analyst's business understanding ability and ability to solve practical problems for enterprises through data. For example, the analyst's analytical process, analytical thinking, analytical skills, and demonstrated persuasion. You can consider entering a professional company in this area, or if you are lucky enough to meet an experienced teacher to take you for a while, like I was lucky enough to meet a teacher to take me when I entered Cassia Ming, and I made rapid progress, so now I am basically very proficient in this set.