Methods and systems for updating a user interface based on level of user interest

A computer-implemented method of producing a machine learning model for determining whether a visitor is in a browsing stage or in a purchasing stage, comprising:

• performing an iteration of training the machine learning model, by:
• identifying, via at least one processor, at least one historical customer of an electronic store that completed a purchase at the electronic store;
• retrieving, via the at least one processor and from a system associated with the electronic store, tracking data indicative of interactions between the at least one identified historical customer and the electronic store via at least one customer device;
• identifying a point in time at which the at least one customer obtained a prequalification;
• isolating a portion of the tracking data corresponding to interactions that occurred prior to the point in time at which the at least one customer obtained the prequalification, and extracting the portion of the tracking data;
• and
• training, via the at least one processor, the machine learning model using only the extracted portion of the tracking data corresponding to the interactions that occurred prior to the point in time at which the at least one customer obtaining the prequalification as training data, to associate the interactions in the extracted data with the completion of purchases, such that the machine learning model is trained to determine whether input tracking data includes interactions indicative of a customer in a purchasing stage;
• receiving an indication that the electronic store has been altered;
• in response to receiving the indication, performing a further iteration of the training of the machine learning model for at least one historical customer that completed a purchase at the altered electronic store.

A system for personalizing a visitor experience of an electronic store, comprising:

• at least one processor;
• and
• at least one memory operatively connected to the processor and storing instructions executable by the processor to perform operations, including:
• receiving an indication that a visitor has accessed the electronic store via a user device;
• generating tracking data indicative of interactions of the visitor with the electronic store via the user device, wherein the interactions of the visitor include a mouse movement, a mouse hover, a click, a selection, a view of a website page or portion of the electronic store, a search, combinations thereof or patterns formed thereby;
• inputting the tracking data into a customer interaction model, and determining, by using the tracking data with the customer interaction model, whether the visitor is in a browsing stage or in a purchasing stage, wherein the customer interaction model has been trained only using training tracking data from one or more individuals other than the visitor that is for interactions that occurred prior to a point in time at which the one or more individuals obtained a prequalification;
• and
• altering the electronic store based on the determination by one or more of adding, removing, rearranging, re-coloring, highlighting, or de-emphasizing one or more interactive features displayed on a user interface of the electronic store, the one or more interactive features including a selectable element that is operable to one or more of:
• accept or reject a product recommendation;
• or cause the user device to navigate to a resource for obtaining a pre-qualification, financial information, or an electronic offer on the electronic store for purchase of a product.