AmzScope Chrome extension will help you find profitable products for sale on Amazon
and will show the number of sales of competitors. With this data, you can mak
a good decision and run a really successful product
Amazon does not share sales data. This is a commercial secret of Amazon. But there are two methods that help predict the number of sales: BSR and "method 999"
What is BSR? Amazon assigns each product a BSR metric in the category in which the product is sold. The lower the BSR, the better the product sells. Every time the product makes a sale, the BSR decreases. The best selling product in the category has BSR = 1. We track the changes in BSR on goods and make a prediction based on these changes.
The core of "Method 999". In Amazon, one trick works: if you try to add 999 units of a certain product to the basket, and if there are less than 999 units in the warehouse, Amazon issues the remainder of the stock in the warehouse (for example 743). If you do this operation the next day, we will get another number (for example 731). In this way you can find out how many units were sold in one day (in our example it is 12 units).
Step 1: We collect the BSR data and the number of sales per day as coordinate points (at the moment we collect more than 1.5 million points per month)
Step 2: Then we apply the obtained points to the graph and perform the regression analysis of the data, we apply the trend line based on which we can make predictions
Step 3: On the trend line, we report the sales data for the current day, based on the current BSR
In the past, we ourselves used various programs for Amazon market analysis. However, they did not give reliable information. The data in the application did not match the data we had from the sales of our own products. In addition, we noticed that the data is not updated, for example, skis sold equally well in summer and winter
In our case, there was a situation when a person calculated that the goods would be sold at 30 units per day, according to one of the free applications, and bought 2,000 units of the goods. Later it turned out that the goods are sold at 2-3 units per day. As a result, capital was invested in illiquid goods because he trusted an application that offers free access
In our team there is a programmer-analyst. We rent a server to collect data from Amazon. We pay the programmer and for renting the server, so it's impossible to make the application with the relevant data for free. Do not expect high quality data, technical support and updates from free applications