Quantified Self Data and Research Question

The AMC theaters app on my phone keeps track of every movie I see at an AMC.  It also keeps track of every movie trailer I was shown before the start of each movie.  When I found this out, I decided to record my enjoyment level on a scale of one to ten for every trailer I saw, every time I saw it.  Everyone gets tired of seeing the same movie trailer (or commercial, or episode) over and over again.  I wondered if I could visualize a relationship between enjoyment level and how often I see a certain trailer.  My research question was: How does my enjoyment of certain movie trailers change over time, particularly in relation to how frequently I see the trailer?

My Dashboard

My dashboard features two tables that display the enjoyment level rating I gave each movie trailer per viewing instance.  I decided to section the trailers into two groups: trailers for movies I ended up seeing or plan to see in the future, and trailers for movies I have not and do not plan to see in the future.  I noticed that anticipation had a lot to do with enjoyment level, and I wanted to explore if seeing a trailer for a movie I was anticipating over and over in close proximity still had negative effects.  Creating two groups out of the trailers allowed me to tell a story about anticipation levels as well.  

To the right of the tables are two line charts that correspond to the table they are next to.  The line charts visually show that there is a negative trend in enjoyment level as times viewed increase, particularly for movies I do not plan to see, but sometimes with the exception of movies I am highly anticipating.  I feel the line charts are good at visualizing the negative trend, but I do not think they are sufficient on their own at displaying enjoyment level ratings which is why I paired them with the tables.  The tables encourage a deeper examination of each data point.

Underneath the tables are two bar charts that display the average change in enjoyment level rating based on number of days between viewings.  Here, I measured my viewing instances of each trailer by how many days were between this one and the last and created two groups: 1-7 days between viewings, and more than seven days between viewings.  I measured my ratings by the difference between the enjoyment level on this viewing and the last.  The values of my bar charts indicate that on average my enjoyment level went down between viewing instances for both movies I was and was not anticipating seeing and regardless of time between viewings.  However, enjoyment level goes down more on average for movies I am not anticipating seeing, and goes down more on average when the viewing instances are within seven days of each other.  

Audience and Conclusions

My visualization could be of interest to any fellow movie enjoyer who can compare my enjoyment level to their own if they have seen any of the same trailers.  They might even learn about new movies and end up seeing some themselves.  The answer to my research question specifically could be of interest to people who work in film marketing and development and creating trailers.  The best trailers I saw were for movies I was anticipating seeing, but gave away so little that they could actually increase my anticipation rather than grow old and tiresome like most trailers. 

Improvements

I am not convinced that I found the best way in displaying the average change in enjoyment level in relation to how many days go by between viewings through just the two bar charts I used.  The average changes are just single numbers, but I wonder if there is a way to display the change more visually than in just an average number.  I also think my dashboard is on the brink of having too much information for too small of a space, but I think I did my best to keep it intuitive and readable.  It could be more visually exciting or attention-grabbing, but Tableau is fairly limited in that regard.