We have a website and an analytics tool that collects traffic information to the website. The two variables that are collected are Page Views (number of pages a user has seen) and Visits (number of sessions).
Recently we had an annual event, and our analytics code got compromised and damaged our visits count. Luckily, we have our page view count.
We would like to know the approximate number of visits for that period where we lost the data. So, what I did was collect information for all similar events in order to find a model that best fits the data of the compromised event.
The following is the data format:
Each event information is 7 days long, which lists the month the event was held, its respective PageView and Visits information, and an Event identifier.
The following is the page view data for the current event that we recorded (with compromised visits data):
How would I approach this problem?
I was not sure how to 'feed' the new data into the historical data and identify how one can then build the visits data. I also tried to normalize the historical data by taking the sum of information in the last 7 days, and getting a percentage for each of the dates.