Data Science

Who won the ads battle at Super Bowl 50? We analyzed the sentiment of tweets to find out

 

Introduction

On Monday we showed you how we analyzed 1.8 million tweets associated with Super Bowl 50 in order to gauge the public’s reaction to the event. While the Denver Broncos and Carolina Panthers waged war on the field, a battle of ever-increasing popularity and importance was taking place off it. I am of course talking about the Super Bowl ads battle, where top brands pay top coin for a 30-second slot during one of sport’s greatest spectacles.

This post comes on the back of the ‘Text Analytics Delivers Game-Changing Customer Insight’ webinar that we ran in conjunction with our friends at RapidMiner. You can check out the video here.

With a viewership of 111.9 million in the United States alone (35% of population), Super Bowl 50 was the third most-watched event in US history, coming in just behind Super Bowl 49 and Super Bowl 48. In fact, the Super Bowl accounts for the top seven US broadcast events of all time, so it’s easy to see why brands pay what they do to be involved, which is roughly $4.5 – $5 million for that 30 seconds of airtime alone. That’s over $166,000 per second.

So after analyzing viewer sentiment toward the pigskin throwers on the field, we thought it would also be cool to find out which brands brought home the bacon in the ads battle, this time using RapidMiner and AYLIEN Text Analysis.

 

 

Data Collection  – Twitter

Over the course of two days and nights, we collected 120,000 tweets that mentioned, or were related to, the brands that advertised during Super Bowl 50. We focused our attention on 15 top brands by analyzing sentiment and clustering the results to see how viewers reacted to the various ads on show. Ultimately, we wanted to uncover the major winners and losers based on viewer sentiment from collected tweets.

Using the RapidMiner Search Twitter operator, we gathered tweets related to our 15 brands. and then got to work on prepping our data. To do this, we;

  • cleaned the tweets by removing links
  • removed retweets
  • kept the meta data we needed (user ID’s, geolocation, hashtags and mentions)
  • removed non-subjective tweets to ensure we were only concentrating on tweets that contained opinions and that we were mining relevant tweets that would give us real insights into the opinions of the viewers.

Initially, we focused mainly on volume to get a handle on what exactly people were talking about, what brands they mentioned most, and how the brand-related chatter developed in the build-up to the game, during the game, and in the aftermath. As you can see from the graph below, there were clear and predictable spikes in chatter volume during the game itself. What is also interesting to see is how the brands managed to generate significant hype even before their ad was aired, and continued to do so for hours after the game had ended and the Panthers fans had cried themselves to sleep. Sorry Carolina 🙁

As you can see, Amazon completely dominated with just under 40% of all collected tweets mentioning the brand or their related keywords (Kindle, Echo, etc). The graph shows how the brand chatter develops before, during and after the game, with Amazon remaining top throughout in terms of volume.

One interesting observation we made from this graph was the sharp increase in chatter around Budweiser, in an otherwise (relatively) quiet period for the beer brand. We decided to do a bit of research on this spike and came across a tweet from Budweiser’s Head of Marketing Communications which quickly explained the sharp increase;

 

 

February 8, 2016

Hmmm! We’ll leave you to decide on the legitimacy of this claim but either way, it really shows the power of celebrities and the effect they can have on brand awareness with a simple mention.


K-means clustering of brand-related keywords

Next we wanted to find out what people were talking about when they tweeted about our 15 chosen brands. With the help of Thomas Ott, from the super-smart team at RapidMiner, we used RapidMiner’s text processing capabilities to create clusters of words using the k-means algorithm. This allowed us to understand what it was that people were talking about in each tweet.

As an example, let’s take a look at our chatter-volume champions, Amazon.

 

This keyword cluster nicely displays the words that were used in Amazon-related tweets. For organizations wanting to know what words and phrases customers are using in relation to their brand, this information can be extremely valuable.

 

 

The true voice of the customer

While it was interesting to know what keywords people were using and what brands they were tweeting about, what we really wanted to know was their opinion towards each brand and the sentiment of their tweets – whether it be positive, negative or neutral. We wanted to hear the true voice of the customer.

To achieve this, we utilized the Sentiment Analysis capabilities of our AYLIEN Text Analysis API to give us an indication of the polarity of the text. As expected, 60-70% of the tweets we collected were neutral, with viewers expressing neither positive or negative sentiment in their tweets. The real insight, however, came from the tweets with positive and negative polarity. This is where we found some clear winners and losers in the Super Bowl ads battle.

So let’s take a look at the good, the bad and the ugly from Super Bowl 50;

The Good

As you may have guessed from the previous graph showing the high volume of chatter around the brand, Amazon came out on top in the battle of the brands. The graph below shows viewer sentiment toward Amazon’s ad campaign and that tall green spike represents a lot of love for what was a star-studded 30 second triumph for the retail giant.

This amount of positive sentiment was, of course, a huge plus for Amazon. However, we all know that love doesn’t pay the bills and the goal for this ad campaign was to boost sales for their latest gadget, the Amazon Echo.

Result: In a matter of days, the Echo rose to second place in the bestsellers list. Well played Amazon.


The Bad

At the opposite end of the scale, we had the brand that received the highest amount of negative sentiment toward their Super Bowl ad. In what was their first Super Bowl appearance, PayPal failed to inspire and ultimately paid the price for playing it too safe.

As you can see from the graph below, PayPal suffered a sharp increase in negative sentiment immediately after their ad aired.

While Amazon were basking in the glory of a superbly executed ad campaign with significant sales increases, PayPal were being mocked by the likes of AdWeek who mirrored general opinion that their Super Bowl offering was ‘safe’ and ‘boring’.


 

The Ugly?

One brand that certainly didn’t play it safe was Mountain Dew with their Puppy Monkey Baby ad. If you haven’t seen it, picture a dog head on a monkey torso with human baby legs, wearing a diaper. Yep.

Initial reaction proved to be mixed, with sentiment leaning more towards the negative side than positive as viewers perhaps found Mountain Dew’s hybrid creature a tad disturbing.



The blue line in the graph below shows polarity swaying from positive to negative, perhaps indicating a love-it-or-hate-it response from viewers.

Mountain Dew clearly went for the shock factor here and while their ad may have drawn as much negative sentiment as it did positive, the viral appeal of this little monster can not be denied. At the time of writing, the Puppy Monkey Baby ad has been viewed 22.8 million times on YouTube alone. Compare this to PayPal (1.7 million views) and Amazon (17.8 million views) and you can see how successful that shock factor has been.

Key Takeaways

In today’s world, if someone wants to express their opinion on a brand, product, service, or anything really, they will more than likely do so on social media. It is therefore important for organizations to perform social listening to gauge customer sentiment toward their brand, campaigns or even their competitors. There is a wealth of information published through user generated content that can be accessed in near real-time using Text Analysis and Text Mining solutions and techniques.

  • Social media is the modern day focus group
  • The business insight that can be mined from online chatter is often overlooked
  • User generated content is plentiful, it’s timely and it’s often opinionated which means it’s extremely useful to brands
  • If you’re running a Super Bowl ad, fill it with A-list celebrities or a Puppymonkeybaby!





Text Analysis API - Sign up




Author


Avatar

Noel Bambrick

Customer Success Manager @ AYLIEN A graduate of the Dublin Institute of Technology and Digital Marketing Institute in Ireland, Noel heads up Customer Success here at AYLIEN. A keen runner, writer and traveller, Noel joined the team having previously gained experience with SaaS companies in Australia and Canada. Twitter: @noelbambrick