Hi recently i have started a new project which targets to identify propensity to purchase of any financial product for every active customer. However i am not sure on what methodlogy to follow... Here is my plan - First i will go to 6 months prior to today, and set my flag on ownership of specific product on T-6 mthns - Then i will collect data as of T-6 mthns (or should i take today's data) -I will take every specific product's data on my datamart expect the data related to my target variable.. -I will employ a decision tree to make a reduction in data size -Then i will built my propensity model on outcomes of previous model is that made sense? LAstly i am not sure that do i have to estimate model on all active customer segment or all customer segment. Hence if i calculated my scores on all customer segment many of the individuals will exhibit "0" value for many product ownership and that will deteriorate my models consistency.. am i right?
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