Although the cross-border e-commerce industry is a new industry with a much higher chance of success, in recent years, more and more sellers have entered this industry, which has continuously increased the competitiveness of the entire industry. After all, the market is so big, and the more sellers there are, the less chance of success there is. If you want to stand out from the crowd, you need to be fully prepared. Today, let’s talk about how to analyze Wish seller data?
1. Wish industry data.
By analyzing the sales of each product category on the Wish platform, you can understand the number of stores, the number of products, the average selling price, the average daily sales volume, the average daily sales volume, and the average daily sales rate. This information can help you understand the overall market trends and guide new product development and transformation strategies.
2. Wish sub-industry data.
Understand the number of stores, the number of products, the average selling price, the average daily sales volume, the average daily sales volume, and the average daily sales rate of each sub-industry. This is very useful for sellers in specific categories, helping them understand the market capacity and find a suitable market positioning.
3. Store data.
By analyzing the store data of competitors, you can understand their operation methods and strategies. This can help sellers better understand their competitors and learn operational methods from them to stand out from their peers.
4. Product data.
Analyze the popularity and popular trends of products on Wish at present or even in the current season. This can give sellers an intuitive understanding of the hot products in the market and the products that have lost the market. This information is very valuable for sellers and can guide their product selection and market positioning.
5. Collect data.
Get seller data from the Wish platform. This may include information such as sales volume, number of orders, product reviews, refund rate, etc. You can use Wish’s API or export function to obtain data.
6. Data cleaning.
Clean and pre-process the collected data. This includes removing missing values, handling outliers, standardizing data formats, etc.
7. Define indicators.
Determine the indicator or set of indicators you want to analyze. This may involve sales growth rate, product quality score, market share, etc.
8. Data analysis.
Use appropriate data analysis methods and tools to conduct exploratory and statistical analysis of the data. This can include data visualization, trend analysis, comparative analysis, etc.
9. Interpret the results.
Based on the analysis results, draw conclusions and explain them. Identify success factors and improvement opportunities to provide valuable insights for optimizing sales strategies.
10. Build a predictive model (optional).
Based on historical data, build a predictive model to predict future sales trends and performance. This can use methods such as regression analysis and time series analysis.
11. Maintenance and monitoring.
Update data regularly and monitor indicators continuously. This can help you track changes in sales performance and make timely adjustments and optimize strategies.
In summary, by analyzing Wish seller data, sellers can better understand market trends and discover new opportunities; at the same time, by analyzing competitor and product data, they can continuously optimize their own operations and find new opportunities based on market popular information.