Price2Spy will soon be launching something no other price monitoring tool in the world offers – Product matching assisted by Machine Learning (ML).
ML as an AI subset is based on the idea that machines can learn and adjust through experience. The innovations in Artificial Intelligence (specifically ML) improved the eCommerce industry.
We are very proud of this project – it took us 18 months of hard work, with a lot of tumbling in the dark. 18 months is a lot for a commercial project, it’s not often that software companies the size of Price2Spy go for such an investment. We did, and we are very happy that we can finally present the results.
These days you will read a lot about various ML projects. Please be aware that ML can be roughly divided into:
Product matching combines all 3 of the above – basically, you have 2 products shown on 2 websites, and you need to establish whether they are a match. Their naming might be similar or not, their descriptions will most likely vary, the images used might also have a degree of similarity, and of course, they both have a price, which should be similar, but not necessarily identical.
Let’s try to elaborate on the following example:
Pretty loose problem, isn’t it? And if you dive into ML aspects of it, not an easy one. Yet – Price2Spy managed to pull it off.
In the words of JF Kennedy – we did it not because it was easy, but rather because it was so difficult!
This is why we decided to share with you the story of this project – I believe it will be a good read both for Machine Learning (ML) enthusiasts and for eCommerce professionals who wonder how their product matching can be done in a more reliable and yet cost-effective way.
Back to our question – the above to products are NOT a match. Basically, Sensodyne has 2 very similar products:
So, this was only a short introduction to this complex topic. Stay with us in order to find more about it in the following posts!
For more information please check these links: