Detailed explanation of cross-border e-commerce operation process and data business management

1. Cross-border e-commerce operation workflow

Cross-border e-commerce operation is a complex and delicate job, and its workflow mainly includes the following aspects:

1. Old style optimization

Daily operations require follow-up on the old models of the store. The specific operations are as follows:

• Follow link traffic and sales in the Amazon backend, and view ads.
• Check domestic and foreign inventory and replenish FBA inventory in a timely manner.
• Continue to pay attention to the trends of competing products on the Amazon front desk, and pay attention to the display effect of product detail pages on PC and mobile terminals.
• Follow up evaluation and QA, pay attention to keyword rankings and category rankings, etc.

2. New product launch

The launch of new products is an important and urgent part of the operation. The main work includes:

• Selection of images to be put on the shelves, writing of titles, keywords, five-line descriptions, product descriptions/A+ pages.
• Judgment of the product market and analysis of competing products on the site.

3. Store maintenance

Store maintenance work includes:

• Check marked shipment status to avoid missing or prematurely marked orders.
• Track order defect rate and effective tracking rate in a timely manner.
• Follow up the production progress of hot-selling products and check the arrival of FBA inventory.
• Handle unsaleable inventory and maintain store feedback rate.
• Regularly check whether Amazon’s various deductions are normal and handle other performance-related issues.

4. Customer Service Processing

Including daily email responses, review and feedback marketing, QA responses, etc.

5. Others

Including but not limited to internal data statistics, temporarily assigned tasks, etc.

2. Cross-border enterprise data business workflow

1. Data collection

Data collection is the foundation of data business, mainly from two aspects:

• Internal enterprise data: Sourced from various business production systems, including CRM data, call center data, financial data, warehousing data, store data, sales data, office automation data, logistics data, website data wait.
• Enterprise external data: refers to data generated outside the enterprise and obtained through cooperation, purchase, collection, etc. Usually includes competitive data, marketing data, logistics data, industry data, etc.

2. Data storage

Data storage uses data warehouse technology (Extract-Transform-Load, ETL) to integrate data to form a data warehouse and data mart for upper-level computing or business use.

3. Data calculation

Data calculations can be divided into regression models, clustering models, correlation models, time series, classification models and machine learning according to different implementation results. According to the timeliness of calculation result output, it can be divided into real-time calculation and offline calculation. Some companies will also add temporary calculation between real-time calculation and offline calculation.

4. Data management

Data management is achieved through the Data Management Platform (DMP), but most DMP products are still focused on the integration and extraction of underlying data and have not yet risen to the data management level.

5. Data application

Data applications include automated marketing, on-site personalized recommendations, data product reports, etc. Data-driven needs to be realized with the help of technical means, usually using an automated operating mechanism based on data event triggering or data result triggering.

Through the above process, cross-border e-commerce companies can efficiently complete daily operations and improve operational efficiency and decision-making levels through data business management.