Store operations include market analysis, product selection and development, store monitoring, product analysis, creating popular items, etc. In all operational links, data analysis can provide an objective basis for decision-making. The goal of data analysis is to find the most suitable operation plan for the store to maximize sales profits.
Basic sellers need to master skills such as selecting products, editing product information, purchasing goods, and normal delivery. Advanced sellers need to do a good job in customer service, open express trains, carry out store marketing activities, and promote steady growth in store sales and other key tasks. Star seller. Super sellers must integrate the supply chain, increase inventory turnover rate, enhance bargaining power, build brand awareness, and become the top store in the industry. The optimization of the above work is inseparable from data analysis.
Common steps in data analysis
1) Determine the goal
The problem that needs to be solved should be clearly defined. 2) Collect data
Collect data about your own store. Your store’s past sales records, transaction conversion data, advertising and promotion effects, etc. are the most authentic and valuable data and should be collected regularly, sorted and archived.
Collect data provided by the platform. The “Data Aspect” tool provided by AliExpress Seller Backend is a data product created by AliExpress based on the platform’s massive data. Sellers can make full use of this tool to understand the status of the industry. Industry sales data and competitive product data can be collected through information such as the best-selling rankings and flash deal product lists recommended to buyers by the platform.
Use third-party data tools. Some platforms do not provide rich data to sellers and cannot meet their needs for data analysis. In this case, third-party data tools can be used to obtain the required information. Some third-party tools are specifically designed to serve cross-border e-commerce sellers, providing industry data and competitive product data on the platform, and can monitor the promotion data of their own stores, etc.; some third-party tools reflect the search trends of global netizens, such as Google Trends .
3) Organize the data
The data can be sorted and made into charts. You can also use Excel formulas and pivot table functions to perform statistical calculations. The most important thing is that you can intuitively see what you want. desired result.
4) Comparing data
Usually only after comparing data can we draw conclusions and make judgments. For example, comparison of this month’s data with last month’s data, and comparison of data on different commodities.
5) Make judgments
Discover areas for improvement by comparing data, or screen out better solutions. 6) Try changes
Try to create a new plan for data testing. For example, try to use several different styles of advertising pictures when doing through-train promotion.
7) Compare before and after to determine the optimal solution
After testing options A and B, select the optimal option to achieve the best results.
Common terms for data analysis
Views (PV, Page View), the total number of times a page has been visited. If a page is clicked once, it will be recorded as one view; if a user clicks or refreshes the same page multiple times, it will be recorded as multiple views, and the accumulation will not be repeated.
Visitor count (UV, Unique Visitor), the total number of unique visitors to the website. A user who visits a store multiple times in a day is counted as a visitor.
Conversion rate. Commonly used conversion rates include detail page transaction conversion rate and full store conversion rate. Details page transaction conversion rate = number of users who made transactions on the details page/number of visitors to the details page. Conversion rate of the whole store = number of users who have made transactions in the whole store / total number of visitors of the whole store
Click-through rate, the ratio of the number of times a certain content is clicked to the number of times it is displayed on the page, reflects the degree of attention the content has received, commonly used To measure the effect of promotional images or product main images
Payment rate, the number of paid orders accounts for the percentage of the total number of orders taken, payment rate = number of paid orders/total number of orders
Bounce rate. The number of times a user visits a store and then leaves after logging in accounts for the percentage of total visits to the store’s login page. Bounce rate = number of bounces/total number of visitors to the landing page.
Visit depth refers to the number of store pages that a user continuously visits at one time. The average visit depth is the average number of store pages that a user views continuously each time.
Per capita residence time in the store, the average time each user continuously visits the store.
Transaction conversion rate, the ratio of the number of transactions to the total number of visitors, transaction conversion rate = number of transactions/number of visitors.
Unit customer price, the average amount paid by each buyer, unit price = sales volume/number of customers within a certain period of time (customer deduplication). The unit price per customer is related to the price of a single product and the quantity of goods purchased by the buyer. Increasing the price per customer means that each buyer provides greater value to the store.
Proportion of old customers. The proportion of old customers among the transaction users. Old customers are generally defined as customers who have made purchases in the store within two years. Proportion of old customers = number of old customers among transacting users/total number of transacting users.