Best Practice: Classifications vs. Separate Variables
I'm so surprised there are no other questions out there like this. So, here it goes:
I'm wanting to understand the best practice for capturing a group of related user preferences and then turning around and gathering the metrics for them. Let's use the following as an example. These are not the real world values. Say we have a section on our site that lists a bunch of sports publications, and each have an indicator for what sports are covered within. This does NOT work as a filter, it simply displays an icon or more representing the the sports genre represented within the publication.
sportsPreferences:
- baseball
- soccer
- football
- hockey
- basketball
- volleyball
Here's the scenario: When the user logs in, all of the preferences are "turned on" by default. The user may then go to a page where they can modify the list of preferences so that the full list of publications are updated to display the specific sports preferences the user selected. Important to note is that none of the displayed products are filtered out of the list. They simply update to display only the specific sports genres where they are relevant to the options the user elected to keep.
From this scenario, the business wants to understand how many users are turning off at least one choice, and then the ranking of the items left on vs. the ranking of the preferences turned off.
My initial thought is to capture all choices as a single string of true/false values and then separating them by using a classification rule. However, I'm wondering if that's the best way to go. Has anyone out there had a similar situation where you might be able to recommend a better approach?
Thanks in advance!

