I need to build a model that measures the impact of TV advertising on website traffic.
I have two datasets: one contains the number of visits to the page and a timestamp, the other contains a timestamp and TV ad data such as a channel, information about whether the ads are shown in the middle, after or before a TV show, and so on.
My problem is that I don't know how to merge these datasets as the timestamps in the datasets have different granularity and then which machine learning method should be appropriate to measure this impact.
If you have any ideas or experiences please let me know.