Classify Customers based on 2 features AND a Time series of events
I know it has been a while since the question has been posted but for future readers I propose here another solution for the problem of classifying time-series of proportional data i.e., vectors of proportions.

Hidden Markov Models (HMMs) are broadly used for time-series classification. The original implementations were developed for discrete and Gaussian data but since then, they have been extended to more probability distributions.

Among them, the Dirichlet, the generalized Dirichlet, and the Beta-Liouville distributions which are perfectly suited to work with proportional data.

Usually, a model is trained for each category ("Tag" in this question) from time-series for which the category is known. When a new time-series has to be classified, its likelihood with respect to each model is computed. The new time-series is usually assigned to the category leading to the highest likelihood.

Here are the reference papers for these models presenting them more deeply and providing all the equations needed for implementation:


Dynamical Dirichlet Mixture Model, Research Report from IDIAP, L. Chen, D. Barber, J.-M. Odobez, April 2007
Proportional data modeling with hidden Markov models based on generalized Dirichlet and Beta-Liouville mixtures applied to anomaly detection in public areas, E. Epaillard, N. Bouguila, Pattern Recognition 55, pp 125-136, 2016


For the additional two features mentioned in the question, HMMs for mixed data have also been developed in the following research paper:
Hybrid hidden Markov model for mixed continuous/continuous and discrete/continuous data modeling, E. Epaillard, N. Bouguila, MMSP, pp. 1-6, 2015.

These models are heavier to deploys than regression models but have the advantage of being discriminative and generative models and to be able to generalize well when some variability is present in the processed data.