How to determine if a company decision was successful or not? I'm trying to figure out if a decision taken in a company (offering discounts for specific products) is successful or not.
I have done some research and saw that A/B testing might be a way to do this but A/B tests can only be carried out having 2 groups (control and experiment), and in this case all I have is past and "after the decision" data.

Can the statistics behind AB testing be used in this case?

The data I have is in the for of sales per day per article only for one company before and after the decision (only one decision).

Example:

sales for articles X and Y from the 2016-01-01 to 2016-02-01.
The decision was to lower discounts on articles A and B on 2016-01-15, so I want to know if sales decreased or not after this decision.
You could use time series approach to model the "what if not" scenario and compare it with your values after the new program introduction. Check the causal_impact packet from Google and you might also find helpful this tutorial for probabilistic programming. http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb