While dealing with Big Data and with data streams in particular, it is a common practice to summarize or aggregate customers' transaction history to the periods of few months. Consequently, we shall compress the given huge volume of data, and shall transfer the data stream to the standard rectangular format, where columns represent secondary aggregated features and rows represent customers. This data-matrix is suitable as an input to many classification or regression machine learning models. Using those models, we can explore a variety of practically or theoretically motivated tasks. For example, we can rank the given field of customers in accordance to their loyalty or intension to repurchase in the near future. This objective has very important practical application. It leads to preferential treatment of the right customers. It also reduces the likelihood of bombarding customers, who are less likely to purchase, with marketing material over email or postal mail. We tested our model (with competitive results) online during Kaggle-based Acquire Valued Shoppers Challenge in 2014.