Data analysis of retail industry

Analysis of the retail industry

This from personal perspective how to use data mining to help retailers improve business, let data truly guide enterprises to operate, and maximize the role of data to provide business decisions.
First, the membership system can help enterprises collect more member data, and it is more conducive to the work of data mining, and it is also conducive to cultivating customer loyalty.
Is when implementing the member system, you must pay special attention to the collection of two key information: membership card ID, customer contact number or mailbox, because these two key information have a lot of great marketing for information collection and later precise marketing in the period. Help. And the rampant of social media such as WeChat and Weibo, if retailers can allow customers to pay attention to the WeChat and Weibo of the company through related activities, it will also be of great help to cultivate customers' loyalty.
The membership system helps to cultivate many loyal customers for enterprises, establish a long -term stable market to improve the competitiveness of enterprises. Through the member system, you can effectively stabilize old customers and develop new customers at the same time. Because retailers provide preferential prices for members, they are very attractive to new customers. At the same time, most membership cards can be borrowed, and they also provide opportunities for new customers to greatly increase their possibility of becoming members.
member system marketing can promote two -way communication between enterprises and customers. After customers become members, they can usually receive information about new products on new products and understand product information and merchant dynamics, and buy products targeted. In addition, enterprises can understand the changes in consumer needs in a timely manner, and their opinions on products, services and other aspects, and provide a basis for improving the marketing model of enterprises.
It, the data mining project of retailers must be provided with key information of the following tables:
Sales form: card number, sales store ID, sales date, product name, product price, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales, sales Quantity, sales amount, discount and other information.
product table: product ID, product name, recommended retail price, actual sales price, first -level category, secondary category, third -level category, level 4 category, brand and other information.
Customer table: card number, card shop ID, city, number, mailbox, enterprise or personal logo, corporate name, industry, address, etc.
The retail store list: store ID, store name, city, store level, etc.
The is more important for sales tables, product tables, and customer tables, and the product table combing is the key to doing a good job for the data analysis and data mining team. It must consume a lot of time.
3. The purpose of clearing data mining with retailers can make the analytical team and retailer gain greater trust, and at the same time, it is conducive to the smooth development of the project.
The mature analysis team pays more attention to the business starting point of retailers, starting from the business value of customers, grasping customers' attention points, and doing the corresponding implementation analysis work little by little.
The questions that customers want to help them answer the most:
Oshin how to allow active customers to buy more products and maximize their value?
How to wake up silent customers and turn them into active customers?
Which customers are my key customer base? What are its characteristics?
Which key customers have lost? Why loss? How to carry out retention means later? …
It, carry out customer segmentation through data to clarify the characteristics of each group.
For retail data, the two major customer bases must be penetrated into the retail industry: enterprises and individuals. The characteristics of corporate customers are very different from the characteristics of individual customers.
The main performance of the company's characteristics: the purchase volume is relatively large, the group purchase or wholesale is often conducted, the sales volume and sales are relatively large, it is a key customer base for retailers. Although the number is not large, it has contributed more than 60%of the sales of retailers. The behavior of enterprises often: super large procurement, medium -sized procurement, general procurement. For corporate data mining, information needs to be thoroughly understood by the company's industries, procurement quotas, procurement laws, procurement of product preferences, whether the loss, and loss of investigation, which helps retailers to carry out corresponding marketing strategies.
For individuals, you need to pay attention to which are active customers, which are new customers, which are silent customers, what are the value of customers, which festivals are the key peak periods, which products are preferences, etc. These help Carry out sales, stocking and other work in retailers.
Fifth, combined with 5W1H analysis method to carry out retail analysis and excavation.
What: How about the sales situation? How many users? How many times have you been here? How much does it cost every time? What did you buy ....
where: Which stores are the best sales? why? (Transportation, region, etc.) ....
When: Which month is the best sales? Which festival is the peak sales period ....
WHO: Which customers are it? What are the characteristics? What products do you buy? What is the product specifications ....
why: Why do you buy? Why buy so much? Will I continue to buy ....
how: How to improve the customer's re -acquainted? How to wake up customers? How to cross sales? How to help pave the goods ...
6. Help retailers carry out marketing activities design, marketing activities execution, marketing assessment and optimization.
Because data mining is a closed -loop process, it is not the project to write a mining report, output marketing customer list, or the project is successful. It must assist retailers to carry out corresponding marketing design, marketing activities, marketing evaluation and optimization. So as to ensure the effective implementation of data mining, real business value for customers, and expand the scale of business.
The marketing activity design is often: discount discounts, distribution trial clothes, gift gifts, multiple points, etc. can be carried out in different marketing activities through different segmented customer groups, and different groups and different activities can be calculated. The input -output ratio is convenient for continuous optimization of data mining rules in the later period.
It, the key achievement solidify IT system, and realize the solidification of data mining results.
For retailers, data mining is a large investment. For the output of key results, I always hope to solidify the rules of the results and automatically replace the manual operation. Modules or systems allow data mining to maximize the company.