About AlgoFinds
Our Story
My name is Matt Dombrowski, and I spent nearly 10 years at Amazon developing machine learning algorithms, most recently to help creators find the best deals for their audiences. I saw firsthand how difficult it is to navigate the overwhelming number of deals on Amazon. E-commerce companies like Amazon employ thousands of scientists and engineers working to manipulate prices and promotions with the specific goal of extracting the maximum amount of money from your wallet. I created AlgoFinds to level the playing field, putting the power back in the hands of us consumers where it belongs. AlgoFinds uses simple but powerful algorithms to sift through the noise and find the quantifiably best deals that are actually worth your time and money. So we can stop doing this:
How It Works
First, we scan Amazon to find the population of products that are currently at their lowest price ever. Next, we evaluate products on a scale of 0 to 1 based on various factors including:
- Price score: Compares current product price with its historical average on Amazon
- Brand score: Evaluates the product brand's name recognition, desirability, and quality from independent sources
- Trend score: Assesses a product's trendiness based on its rise in Amazon sales rank, with recent change weighted more heavily
- Product score: Assesses a product's quality based on review count, star rating, and Amazon sales rank,
- Listing Score: Assesses a product's listing quality based on information on the product detail page and image quality
We combine these inputs to compute an overall numeric deal score between 0 and 1 then rank deals accordingly, ensuring you only see the absolute best deals on Amazon right now. We make the component scores transparent to the user in order to convey why a given deal is being recommended. Users can also sort or filter based on factors most important to them.
