seasonality in classification model

I am building a classification model to predict customer status a year from a given time. There seems to be some seasonality, for example, more changes occur in Summer than in Winter etc. so my dataset (mainly labels) would change depending on how to define prediction time (eg 2020 Jan) and predicting time (eg 2021 Jan). Let's say there are 100 customers and I could make 1,200 entries (100 per month for every month in 2020, where labels are from corresponding 12 months in 2021). This, however, would create many almost identical duplicates except for month. What can I do to tackle this problem? Create 12 entries per customer and randomly select one?

EDIT: Some duplicated records would look like