How to do data analysis in clothing industry?

Food, clothing, housing and transportation are the four elements of people's livelihood. With the development of economy, people's basic requirements for life have also improved, especially as the first "clothes".

The traditional marketing model of clothing industry can no longer meet the changing needs of modern consumers. The fierce competition in the market environment makes the clothing industry gradually diversified and refined, and intelligent marketing is realized by using data management.

Challenges facing the clothing industry

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

√ Clothing is a commodity with short fashion cycle and strong seasonality, which easily leads to imbalance between production and sales and high inventory risk;

√ Under normal circumstances, there are many clothing stores and SKUs, and the amount of data is huge, resulting in unsynchronized financial business information;

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

√ Consumers will label themselves and clothing brands. How to match products and channels with consumers' labels is an urgent problem for the clothing industry.

Key points of data analysis in clothing industry

Figure-clothing industry index system

1. From the perspective of supply chain, the data analysis of clothing industry mainly focuses on three links: purchase, sales and warehousing, among which the ratio of storage to sales and the rate of sales are two important analysis indicators.

Figure-storage-sales ratio

Figure-sales rate

2. The execution of delivery and collection also needs real-time monitoring, which is also an important indicator of financial data analysis.

Figure-Real-time monitoring of delivery receipt

3. It is necessary to make a refined and multi-dimensional analysis of goods and shops, trace the source and prepare for the next stage of precision marketing.

For example, unsalable sales is one of the simplest, most intuitive and most important data factors in sales data analysis. Best-selling models are goods that sell a lot in a certain period of time, while slow-selling models are the opposite. Best-selling money is not an inherent property of goods, but a dynamic property that changes with the change of business and time period. We should analyze the reasons from the changes.

Exploration of graphic reasons

Analyze the data value brought by the cloud

√ Open online+offline+logistics data, and enjoy consumer-centered membership, payment, inventory, service and other data in an all-round way;

√ Real-time response of massive data to realize dynamic intelligent analysis and meet the changing needs of consumers;

√ Real-time tracking of sales, understanding of market demand dynamics, and timely adjustment of goods distribution, thus reducing inventory risk;

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

√ Tracking and analyzing consumers' purchasing behavior, providing personalized and accurate operation services, thus improving the marketing transformation effect, improving consumers' loyalty and reducing marketing expenses;

√ Explore scientific pricing strategies through sales forecasting models to enhance the competitiveness of commodities.

summary

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