Attached is an example for each of your questions, there was some logic that I did not quite understand from your description, so I just took guesses. with more details, I am sure your exact desired logic can be computed in Tableau.
The downside of the attached is that it requires substantial knowledge about Tableau, specifically table calculations in order to produce these results. Also because of densification, the stacked bars takes a few seconds to compute.
If you have the ability to process your data prior to Tableau, assigning the status to each userid, then that lowers the bar, eliminating the need for complex table calculations, making is easy to analyze and produce visualizations from.
Also there are a great deal of other routes to produce these same results, so if you have additional requirements or constraints, other routes may be better.
As you are considering Tableau, I highly recommend http://www.tableausoftware.com/videos/zen to better understand what situations Tableau is a great a fit for, and you are welcome to contact me if you have other questions.
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1st, Joe has given you an excellent example showing it can be done in Tableau, but it is likely a poor choice for applying all levels of complex logic like this for your data. Chief amongst the reasons:
-Speed to compute (one of Joe's views required 80 seconds to compute, with about 20k customers, not that large considering many retailers have millions or tens of millions),
-Ease of maintenance since these calcs are opaque and don't automatically work in every view without understanding Tableau fairly well,
If you have a data architect, DBA or experienced SAS programmer around, I would push these complex, repeating business rules to their world (analytic data prep) where they can apply these and many other rules at the database level, past the data warehouse. This work would allow the analysts to rapidly consume this information with Tableau and explore it from many directions. This is exactly the approach I have applied at many companies including Netflix, REI and while teaching on the Faculty of The American Marketing Association.
Other examples of things that belong at the analytic data prep layer include customer segmentation, lifetime value estimation, propensity to buy based on an offer or category, etc. This would hold true with other analytic and BI tools, not just Tableau.
I hope that helps you move forward to a successful solution for your situation.
Director of Analytic Product Management
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