What is the best methodology for building a financial product propensity model...

[omerzeybek]

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?

[M_Maldonado]

Hey Omer,

Sounds like a nice project. How did you come up with the 6-month observation window, just business knowledge? You can back up that decision with time series analysis or just testing for any seasonality. 6-months could make sense as long as it accounts for high sale seasons like Christmas, special promotions or holidays.

What do you mean with using a decision tree to reduce the size of your data? variable selection? you have a better understanding from this thread correct?

Without knowing too much about the data, you might need to assess what works best for your definitions of active/inactive and how often they go from one to another. It might make sense to have a model for actives, inactive for less than 2 quarters, inactive 2-4 quarters, and inactives more than a year. Establishing those segments will add a lot of value to your business and to your models.

Good luck!

Miguel