How Analytics Intensive has the Media and Entertainment industry become ?


What do we all do to have fun or entertain ourselves over a weekend?

Our idea of entertainment is to relax at home and watch a movie or watch our favorite program on television or head out to the theatres to watch a new release or go to our favorite bar to watch a game.

Yes, there are other things to do as well like playing a sport, hiking, playing board games, so on and so forth.

However, to be honest, majority of us actually prefer the enjoyment and fun when it is to do with media and entertainment.

I myself am a tennis player and love getting out on court and playing a good game. Apart from that, I spend a lot of my time watching videos about poker, tennis, football and other things on YouTube.

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It isn’t surprising that 100 hours of videos are uploaded every minute on YouTube. YouTube actually has more than 1 exabyte (Google how much an Exabyte is) of data.

Netflix and Chill?

Sometimes, we get lazy too and just prefer ‘chilling’ at home. Yes, we all have been there. I personally think Netflix caught on to the pulse of people like us.

Order a nice meal, open a bottle of wine and watch a great movie or binge watch a new show on Netflix. And we wonder where the term ‘Netflix and Chill’ originated from!

Media on the Go!

Media is available to us on every device available in the market. Be it a computer, laptop, tablet, console or mobile, media can be played on anything and everything. Applications ranging from YouTube to Amazon Prime are available across all devices.

The Data Avalanche!

Now that I have brought up Amazon Prime, what a genius Jeff Bezos actually is! What do we all associate Amazon with? Online shopping right?!

Bezos actually saw the potential in the media industry and ventured from being the largest online shopping platform to being one of the largest video internet libraries.

Many called the move dumb, and now all of them are subscribers of Amazon Prime. The irony is evident, isn’t it?

YouTube has nearly 1.8 billion users every month (that is just the number of people logged in), Netflix has close to 118 million subscribers, Amazon Prime has close to 100 million subscribers, the number of television households in the world is 1.7 billion, a total of 1,351,442,001 tickets have been sold in the United States in 2018 alone.

Overwhelmed with the numbers yet? This further proves my point that majority of us love media and entertainment.

These numbers indicate only the number of people that are hooked on. Imagine the data being generated by them!

This data is what the big players dissect extremely carefully in order to use the insights to become Numero Uno! All of them have an in-house army of data scientists ready to slice and dice the data or may outsource it to reduce their burden.

Analytics in Media and Entertainment!

We finally arrive at the data analytics part of the media and entertainment industry.

Advanced analytics are the agents and catalysts that are the source of improvement.

How Listening lead to being Brilliant!

Let’s talk about the user interface of Netflix for a minute.

Netflix actually compiled data from sources that were anonymous forms via social media about what the users thought could be a better version of Netflix.

They compiled data from millions of forms, categorized it and analyzed what the users wanted.

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In exactly 3 months, we had the ‘Skip Intro’ button and ‘Next episode starts in _ seconds’ button.

I guess Jeff Bezos didn’t want to copy them outright, so he just added a ‘Next episode starts in _ seconds’ button.

With user interface becoming smoother and easier, there is no reason for users to look for another alternative.

Making us addicts!

Haven’t you all noticed that the recommendation engines do not let you log off? Yes, it has happened to the best of us!

Netflix does something a little out of the ordinary. Todd Yellin, the VP of Product at Netflix says “Based on time, recommendations are weighted differently.

We experiment with a lot of signals.

Netflix may show you shorter programs, or ones you’re halfway through, when you login late at night and may not be looking to watch an entire show from scratch” Through all this Yellin never did reveal how much of a difference it made while opening the app at 3:00 pm and 3:00 am.

The Netflix algorithm tempts us by showcasing an unfinished show that we didn’t finish. The attempt to get us back on board to finish the show is subtle yet makes 70% of us go back to the show says Yellin.

YouTube and Netflix use similar AI powered recommendation engines which make use of deep learning neural networks to understand how human behavior functions.

Because, Seeing is Believing!

Netflix uses a different kind of analytics too. Have you all noticed the landing cards that display images for each title as people scroll through? These cards keep changing every time you login.

Take the case of the show ‘Stranger Things’, Netflix used the landing card of Millie Bobby Brown (Eleven) and the next time they used Dustin, Lucas, Mike and Will on the landing card.

Whichever cards attract maximum number of clicks are the ones used widely.

Doesn’t all this make the mind think that Machine Learning and AI are doing wonders in the world of Media and Entertainment? They have touched every possible area in this field.

The Movie Goer Club!

We still haven’t gotten to the part where movies are analyzed.

The movie industry is analyzed in multiple ways by using technologies like IoT (Internet of Things), Big Data Analytics, AI and Machine Learning.

We laugh when Kevin Heart is hilarious on screen but we don’t analyze why it actually prompted a laugh from us.

The above mentioned technologies analyze patterns in the tone, the narrative structure, the background music, the colors and many more variables to determine why we laughed, rather than coming up with an explanation like – “because it was Kevin Heart!”

The film by itself is not the only source of information which reveals why the movie succeeded.

The various forms of Analysis!

Enter – Audience analytics. Audience analytics in this case is a mix of social media analytics and text analytics.

Social media reactions on online communities like Twitter, Facebook, Reddit and others are analyzed to learn why some movies make a killing and well, some don’t! When we watch a movie and post a status, our sentiment towards the movie or a particular scene can be obtained.

post a status, our sentiment towards the movie or a particular scene can be obtained.

We can analyze how a movie will do and this will give us an idea as to whether it was better to release the movie on DVD or was it worth all the effort to bring it to the theatres.

A Rather Scary Example!

Analyzing the movie involves analysis of every aspect of the movie and comparing it with previous hits to see if the perception of people has changed or not.

A simple example – The background music of the movies The Exorcist (1973) and The Conjuring (2013) were compared.

Based on the screams in 1973, the screams were obviously a lot more in 2013. The perception of people towards horror had changed a lot in 40 years.

Both movies were massive hits, but required a different approach to bring out the horror. The Exorcist had a BGM that caused sudden spikes in decibel.

The conjuring had a slow build up BGM before the massive scare.

James Wan did his research and based on years of analytics, came up with the perfect BGM and storyline which was in tune with what people were looking for in a horror flick and he managed to scare the hell out of everyone.

So, Is Analytics in Media and Entertainment useful?

The best in the business are making use of analytics big time to decipher insights, make their user base grow and make tons of money.

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Creating something like Netflix and YouTube is one thing, to use advanced analytics to constantly better the product is a whole other thing.

That is exactly what analytics can help you do! The movie makers can analyze various changes in perception to deliver blockbusters like how James Wan did.

The movie production houses can track audience sentiment and social media reactions to realize why their movie rocked the box office or bombed at the box office.

Author: Abhimanyu Sundar
Abhimanyu is a sportsman, an avid reader with a massive interest in sports. He is passionate about digital marketing and loves discussions about Big Data.