If you're happy with R you might want to look at the
Seatbelts dataset that's built into the base R distribution. It sounds very similar. Like your problem, this consists of several time series of counts (front seat casualties) and an intervention/treatment (imposition of a seatbelt law) that affects only one of them (the front seat passenger sequence) and a bunch of covariates (seasons, petrol prices etc). On the help page you can see what a tiny analysis would look like. You'll notice several things there:
To use count data it's often helpful to log the outcome. That enables you to work with proportional increases rather than absolute ones which is usually what you want. It also allowing you to fake a log linear link without the hassle of fitting a non-linear models.
The basic visual analysis consists of building some model, an ARIMA model in the example, of the series before the intervention and then projecting it forward, and comparing it to what actually happened in the treatment in a plot. The code to do so is the line with the
Time series modeling
A simple linear modelling approach, such as you see on the last line of the example, adds an intervention variable to indicate the period, a seasonal component, and an autoregressive term to deal with the autocorrelation. When you are content that the non-intervention variables are well set up, particularly that you have captured any seasonal components, christmas or weekend effects, or whatever it is in your movie sales domain that systematically drive variation in sales without being related to the intervention you are interested in, then you may want to interpret your intervention variable as a causal effect.
In that simple analysis the intervention causes a shift in average level. However, it might have different effects that you'd want to model differently.
What else you'd want to do depends on the model class. For ARIMA modelling the basic issues are 'stationarity' and AR, MA, and seasonal 'order'. It is also possible to take a state space approach. Any good Time Series text will discuss these possibilities. I quite like Shumway and Stoffer (2006) and Commandeur (2007) but there are many good choices, and lots of web material.
Time series analysis can get quite complex quite fast, so taking a graphical exploratory approach first is very sensible, so you know how much time it's worth investing in figuring out these more complex parametric models.
An alternative, non-time series approach is to treat the problem as a regression discontinuity design. There you compare the the period either side of the intervention to see the causal effect of the intervention. Morgan and Winship (2007) has a discussion of the pros and cons of this approach.