{"id":38686,"date":"2019-09-03T16:41:33","date_gmt":"2019-09-03T14:41:33","guid":{"rendered":"https:\/\/aton.com\/?p=38686"},"modified":"2023-11-29T11:12:23","modified_gmt":"2023-11-29T10:12:23","slug":"mathematics-behind-atons-b2b-commerce","status":"publish","type":"post","link":"https:\/\/aton.com\/en\/mathematics-behind-atons-b2b-commerce\/","title":{"rendered":"Mathematics behind Aton\u2019s B2B e-commerce"},"content":{"rendered":"

Recommendation system in B2B<\/strong><\/h2>\n

It has become an everyday experience: I log into an e-commerce site, having a quite precise idea of what I want to buy in mind; I consider a couple of possibilities, and with a certain amount of satisfaction (the price is really good!) my cart is filled up. At this point, I am about to conclude my shopping when\u2026<\/p>\n

\u2026the last obstacle! A number of products appear on the screen: it seems that other customers, similar to me, have purchased them, therefore I could do it too, and since I have bought a mobile phone, it would only be logical to buy also its cover\u2026<\/p>\n

A bit annoyed, I scroll down this customised virtual shop-window and\u2026 surprise, surprise\u2026I find out that it really interests me!<\/p>\n

In Aton, you know, we are deeply interested in recommendation systems<\/b><\/a>. <\/b>It is our DNA: it is evident that the success they have in the B2C sphere can be reflected into a B2B environment, and we are strongly attracted by their win-win feature: they are a useful tool for the salesperson, improving at the same time the final user\u2019s experience.<\/p>\n

Here to you a quick overview of what we are doing.<\/p>\n

That’s not all… artificial intelligence!<\/strong><\/h2>\n

First of all, it is interesting to note that not everything is.. <\/b>artificial intelligence! <\/b>It is possible to create a recommendation system by simply analysing users\u2019 cart history<\/b> at a statistical level (it is indeed called basket\u00a0analysis), <\/b>by counting up the number of <\/b>presences of given product pairs or groups and identifying purchase patterns to be proposed to final users in the checkout phase. It is in fact a very powerful tool, and many apply it successfully.<\/p>\n

To build up a more \u201csophisticated\u201d model it is obviously fundamental to possess a great amount of data regarding sales history, in order to entirely profit from IA algorithms potentialities. Among these stands out collaborative filtering,<\/b> whose objective is to calculate similarities among system users<\/b> to generate advice based on their affinities and the products they have not yet acquired.<\/p>\n

In this sphere, we find promising:<\/p>\n