Best Product Finder does just what the name suggests: it finds the best products for thousands of different product categories.
Our unique machine-learning algorithm uses dozens of different data points on commonly purchased products to suggest the top 10 products that you should consider when purchasing.
What kind of data points do we analyse?
We can’t give out our secret sauce, but our A.I. system looks at information like:
- The number of reviews for a product
- The star rating of those reviews
- Sentiment analysis to determine if reviews are positive or negative
- Social signalling when products are shared on social media
- Mentions across platforms such as web forums and product review websites
- Purchase velocity of products (are they currently trending)
- Much, much more!
Simplify your online shopping experience
Our mission is simple. We use the big data we’ve gathered on thousands of products to make finding the best product to buy much easier for you.
Scouring reviews online can be painful, and often review bloggers choose products which pay them more in affiliate commissions rather than picking the actual best products.
Our system has no bias and analyses data to find the best of the best.
Make your online shopping experience more enjoyable and use Best Product Finder to search for any product you need to buy.
Is the entire platform automated?
No, we don’t rely on the algorithms because the products we recommend are for real people. We employ a team of writers, editors and product experts to individually check every list of products we produce.
If certain products do not fit and are not the best, the human reviewers flag that up to our machine learning model and the algorithm improves over time.
What does it mean to be in beta?
We are in beta mode right now as we are training our machine learning model to fully understand how to rate, review and list the best products on the web.
This means that our platform is constantly improving and product listings may change on a monthly, weekly, or even daily basis as we update the algorithm accuracy.
If you have any feedback, questions, or suggestions for the team, please get in touch.