How to estimate product sales on Amazon: A comprehensive guide
It is very important for sellers to estimate product sales in the Amazon store, but because the Amazon platform does not display specific sales data, many novice sellers do not know how to make accurate estimates. Here are a few ways to estimate sales for your Amazon store.
1. Based on the number of reviews of the product
The Amazon platform is different from other e-commerce platforms. Buyers rarely leave reviews on Amazon, even if the product sales are good. However, the number of reviews for a product can provide a rough estimate of the product’s sales. For most products, the actual review rate is very low, with only one review received for every 80 products sold. Therefore, when researching a product, you only need to count the number of reviews of the product to make a preliminary assessment of the product’s sales. However, it should be noted that since the statistics and display of reviews start from the time the product is put on the shelves, the last three months or six months should be used as the time node, and only the data within this period of time should be counted.
2. Observe the shopping cart situation
This method is relatively accurate but requires some time to observe. Sellers can add the referenced product to the shopping cart and set the purchase quantity to a larger number (e.g. 999). If the other party’s inventory is less than 999, the platform will display the quantity of available inventory. Through this number, the seller can estimate the sales volume. In order to improve accuracy, the same operation can be performed at the same time point in one or two consecutive weeks to get the average daily sales volume of the product.
3. Use third-party tools
There are many third-party tools that can provide more accurate product sales data. These tools help sellers analyze competitor sales and estimate how their own products will sell on Amazon. For example, the amzsount plug-in can easily know the sales volume of the product in the last 30 days when opening any ASIN page.
4. Based on the number of reviews of the store
Taking the US site as an example, sales can be estimated based on the number of store reviews. Generally speaking, if the sales volume of a store is relatively stable, then 4-5 times the number of reviews of the store in 30 days is roughly equal to the average daily order volume of the store. The number of feedback is closer to the real data because few sellers will use feedback.
5. Time series method
The time series method is a statistical method that predicts future sales by building a time series model of sales data. Commonly used time series models include ARIMA model, Holt-Winters model, etc. By fitting the trend and cyclical factors of historical sales data, a relatively accurate daily sales forecast can be obtained.
6. Machine learning methods
Machine learning method is one of the most widely used prediction methods in recent years. By using various machine learning algorithms, such as multiple linear regression, decision trees, support vector regression, etc., models can be built to predict daily sales based on historical sales data and other relevant factors.
7. Considerations
When estimating the daily sales of products on Amazon, you also need to consider some market factors, such as the number of views, clicks, collections, additions to shopping carts, and the amount of advertising investment. These factors will also have an impact on product sales and should be included in forecast models for analysis and forecasting.
Summary
Estimating Amazon store sales is a challenging task, but a combination of different methods and tools can help sellers better estimate product sales. Whether based on the number of reviews, observing shopping carts, using third-party tools, or the number of store reviews, sellers need to continue to learn, analyze and adjust to find opportunities in the market to increase product exposure and conversion rates. At the same time, sellers should pay close attention to market changes and adjust strategies at any time to adapt to changes in competition and demand.