Netflix and Chill: How I Made My 2017 Iron Viz Feeder Entry

I’ll be honest, finding inspiration for this challenge was not easy. Of course, I, like most of us, have my favourite movies and TV shows that I could binge watch for hours. But the challenge lies in telling a data story, not the story of the film or TV show. Those stories have been told brilliantly well by their award-winning directors, actors, and actresses. I needed to find something that wouldn’t tempt me to repeat those ideas, something with a data story that would stand on its own.

So I procrastinated. Appropriately. By binge watching on Netflix. I haven’t always been a Netflix fan, I used to be a quite dedicated cord-watcher. Mostly (shamefully) because I have a strange obsession with awful reality TV shows and Dr. Phil, which you just can’t get on Netflix! I started to think about my own personal history with Netflix, and then got curious about the history of Netflix itself. I started where any good researcher starts: Google.

The first thing I found out was that Netflix started over a dispute over a $40 late fee that Reed Hastings (the founder of Netflix) had been hit with when he borrowed Apollo 13 from Blockbuster. That got me thinking about the history of Blockbuster as well. I still clearly remember the skeleton of a Blockbuster store that sat eerily empty on my bus route to university when I was living in Vancouver. What happened? Who gutted you my friend?

My research eventually led me to this:
netflix-vs-blockbuster-revenues

I think this one image is the perfect example of a data story that hits hard. The sweeping nosedive into bankruptcy, and the David that came out on top of Goliath. Ouch. Painful, but there was my inspiration for this Iron Viz feeder.

Data Sources

Finding information and datasources for Netflix was no problem. I found datasets for the movies and TV shows featured, information about their subscribers, and even the original dataset that was used in their famous competition where they challenged data scientists to beat the accuracy of their recommendation system. Blockbuster on the other hand, was much more challenging.

The issue with Blockbuster is that it went bankrupt around 2010, when data analytics was just starting to enter mainstream consciousness. Prior to that, at their prime, no one had bothered to collect and collate data about the company, or if they had they certainly did not put it out on Kaggle or Github, both of which were born just as Blockbuster died out. So my only source was Blockbuster’s financial statements.

Now, even the keenest accountant will probably tell you that for an average Jane like me, reading financial statements is not the most riveting piece of literature. But I started all the way back to 1999 and powered on through. And I started to see a narrative come to light.

What I found was that Blockbuster’s late fees were a huge source of strife for the company, even in it’s early days. It was involved in several lawsuits related to its late fees and despite customer dissatisfaction, they weren’t willing to let go. This was understandable given that at one time they raked in almost $800 million from late fees alone! It wasn’t until Netflix and other competitors entered the scene that they started to rethink things and introduced their “no late fees” pitch. Possibly (probably) too little too late.

Design

In terms of design and analytics, I tried to keep everything as minimal and simple as possible. This is financial statement data, but I don’t believe it needs to be cached in accountant-speak to be effective. The most “complex” chart I used is probably the jump plot, but I felt like it gave another perspective of Blockbuster’s decline/Netflix’s rise beyond the trend line. (Note: Thank you to Chris deMartini for outlining how to build this chart, and Robin Kennedy for helping me figure it all out!)

The only colours I used in the viz were a pale yellow-grey for the background (I dislike white, it’s quite jarring on computer screens), charcoal grey (I dislike black for the same reason), and red and blue (company branding colours) to represent Netflix and Blockbuster respectively. I tried to eliminate colour legends as much as possible and wherever I mentioned the names of the companies, I used the red and blue colours to indicate that these are the companies that my charts referenced.

I was mindful of users’ interactions with my trend lines, so I included dots overlaid on the lines to make it easier for users to know where to point their cursor for information from the tooltip. I also used a calculation to switch between displaying information in millions or billions beyond a certain threshold so that users would always be able to see Netflix’s KPIs, even before they made their first $ billion. I included an annotation in my bubble charts because I knew it would be challenging to find the little pixel that proportionally represented $40 compared to Blockbuster’s hourly revenue. I tried to make everything easy, simple, and smooth.

And yes, I did use a pie chart, although it’s technically more of a DVD chart, but I felt like I’d throw in a bit of artistic liberty in the mix. I also felt that because I was only showing two proportions of the whole (DVD), it was an appropriate use of a pie/DVD and really emphasised how much Blockbuster’s late fees contributed to their revenue.

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