Estimated monthly sales are, of course, estimates. However, our customers have reported anywhere between a 10% to 50% miss rate for our estimates (90% to 50% confidence rate), with a rather strong concentration in the ballpark of a 20% miss rate (80% confidence rate).

We use a proprietary algorithm that mainly looks at how a product’s number of reviews are changing on a daily basis and at the product age, meaning how long it’s been listed on Amazon, then apply certain theoretical reviews rates. For instance, assuming that all customers who purchase a product leave a review within 30 days, that the product has had 10 new reviews over a time-frame of 30 days, and that 3% of customers who purchase a product leave a review, then you can make an educated guess that 333 units of that product were sold during that period by dividing 10 by 3%, after which you multiply that by the average selling price of the product to get to the monetary sales volume number.

Our algorithm uses other metrics and indicators such as changes in the Best Sellers Rank and Keywords Position but hey, it’s our secret sauce 🙂

Our Amazon Sales Estimator gives you more accurate results for products with a higher number of reviews and most importantly with an increasing number of reviews. As such, they get better over time as we gather updated metrics from a product, so we recommend tracking a product for at least 7 days to get more accurate data.

DataHawk’s BSR Analysis tool lets you unveil the top 100 selling products in that category and analyze the competitive dynamics around them. Leverage the data you gather to get insights on them and reach your dream best seller rank in any category on Amazon. Watch our quick tutorial to learn more about it.