Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a historical customer dataset, the historical customer dataset reflective of pre-purchase, purchase, and post-purchase stages of a consumption process of a plurality of customers and identifying a plurality of first features, the plurality of first features derived from the historical customer dataset. The method also includes generating a first training dataset from the plurality of first features, training a first machine learning (ML) model using the first training dataset, and determining, using the first ML model, a plurality of second features. The method further includes generating a second training dataset from the plurality of second features and training a second ML model using the second training dataset, wherein the second ML model is trained to output propensity predictions for the plurality of customers.

Abstract: A method for using evaluation of intent to improve website usability includes gathering page text and input text from pages viewed by a user, generating a word set from the page text and input text, and performing latent dirichlet allocation modeling on the word set to evaluate user intent. The intent can be used to provide recommendations, improve search results, or identify weaknesses in a website design.

Abstract: A method for using evaluation of intent to improve website usability includes gathering page text and input text from pages viewed by a user, generating a word set from the page text and input text, and performing latent dirichlet allocation modeling on the word set to evaluate user intent. The intent can be used to provide recommendations, improve search results, or identify weaknesses in a website design.