Before start talking about e-commerce, we must talk about the predictive models of consumption and also of segmentation, which currently occur in e-commerce techniques through big data analytical techniques
In the first place, we must distinguish between descriptive models and predictive models, the main difference between both models is that if we try to describe the data and try to describe the data from the start time that we have collected to a current moment, whenever we try we are using purely descriptive techniques to describe that information, but if we try to describe the data anticipating its behavior from a historical point of view, from a current instant of time to the future, then we will already be talking about predictive techniques.
One of the typical cases of e-commerce customization are product suggestions when we add products in the amazon shopping cart, for example, where they show us that users who were interested in "x" product were also interested in such others, the objective they have from the point of view of purchase personalization is to find the patterns of the products that have been most demanded simultaneously in the same browsing session, we are facing a purely descriptive type of analysis, there are different types of algorithms a background which are the a priori a priori tease and others that are in charge of finding those patterns, those combinations of navigation or selection of the most recurring products in that portal and show users who are browsing the website to make this one much more customizable shopping experience.
The second one that really has more impact in the world of e-commerce is user segmentation, customer user segmentation of whatever you want. What is intended is to create user groups according to whether they have been or are doing in real time their browsing profiles, purchase profiles and others. In this case we resort to the typical two-dimensional or three-dimensional representation of point clouds in a way as you have here in the graph as they are the red points that correspond to users who have spent a high number of dollars in a few minutes of browsing, while those in green are those who have spent a lot of money in a long time browsing .
In this way you can customize offers within your shopping website for different users according to their previously analyzed spending level.
The third point of the use of data in e-commerce is to try to trigger early alerts prior to leaving the page without purchase, the customer has a certain browsing profile and we can infer from what their browsing pattern is being through the portal we can predict when you are going to leave the page without purchase based on what data we are registering at all times
These are some brief examples in which I wanted to tell you how you can personalize your shopping websites using current processes and trends.