Estimating treatment effect using movestay command


I want to use the movestay command in Stata but my data is complicated and I’m unsure how to proceed. I would like to estimate the treatment effect on sales using endogenous switching regression for consumers who participated in a cashback and those who did not. More specifically, I want to investigate the difference between redeemers versus non-redeemers in terms of sales. Furthermore, I want to analyse whether the difference is dependent on several variables. However, I have limited knowledge about Stata so my question is whether someone can help me with the command.

I've got the following variables in the data set:
1) Account ID
2) Sales promoted Brand A before cashback
3) Sales promoted Brand A after cashback

Selection variable:
4) Redeemer (0 = No, 1 = Yes)

The dependent variable in the selection equation (I) is a binary variable, assuming value 0 and 1. The main equation needs to be estimated when I=0 and when I=1.

Above relationship dependent on the following variables?
5) User of the promoted brand before cashback (0 = Non-User, 1 = User)
6) User of the promoted category before cashback (0 = Non-User, 1 = User)
7) User of the specific promoted product before cashback (0 = Non-User, 1 = User)
8) Days since last purchase of the promoted product

Controlling variables (not necessarily required for this analysis):
9) Age in years
10) Gender (0 = Female, 1 = Male)

Above mentioned information is available per account ID.

-How can I perform this regression using the "Movestay" command?
-Is the "Movestay" command the right way to analyze the data?
-How to interpret the results?

I know that I ask many questions, but I hope there are people who can help me a little in the right direction.

Thank you in Advance!