Storytelling with Data:Think Broad. Mine Deep. Explain Simply.

SLC|SEM Digital Marketing Conference August 25, 2016

www.emperitas.com / 801.810.5869 / 4609 South 2300 East Suite 204, Holladay, UT 84117

Data’s Role in Digital Marketing

Why You Need to Be Using Data

• The digital revolution means it’s never

been easier to generate or collect data.

• Data is the competitive decider right now.

• Any data you use should complement your

gut intuition, not replace it.

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The Problem Is…

• 80% of data projects are failing right now.

• It’s because the analytics lack context & translation.

• The results are confusing, ugly, and too technical.

• The analysis isn’t tied to a clear problem or strategic decision.

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Purpose of This Presentation – Effective Storytelling with Data

• Thinking Broadly – Capture all relevant information and data.

• Mining Deeply – Use the most powerful analytics available.

• Explaining Simply – Translate the results into plain English.

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Thinking Broadly

The Story I’m Telling Today

• I picked a random topic to show as an example of storytelling with data.

• Our protagonists are two white guys who wanted

to build a global brand teaching history through rap.

• After failing at live performances, they turned to

YouTube and within five years created the

most successful internet show ever…7

Epic Rap Battles of History*

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*Source: https://youtu.be/njos57IJf-0

Epic Rap Battles of History

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Epic Rap Battles’ Impact

Most successful internet show ever – now on it’s 5th season. YouTube is running

traditional advertising (TV, Billboard, etc.) to promote the ERB channel.

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64 Episodes

13.5mnSubscribers

3bnViews

Their Brand Promise – Fans Are the Ones Driving It

• Each video ends with the same call to action:

• “Who Won? Who’s Next?” You Decide.”

• Highly visual production, meticulously researched,

intellectually engaging, and irreverent.

• Give their fans behind the scenes access to see how the show is made.

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ERB’s Pop Culture Impact

• Regularly feature other YouTubers & celebrities.

• Spawned huge numbers of copy cat channels and

fan sites, and has been featured in multiple “react to”

videos (i.e. “Elders React” & “Teens React”).

• Being used in India to teach English.*

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*Source: https://dspace.mah.se/handle/2043/16234

Mining Deeply

Any Analytics Project Needs a Clear Target

• We can use data to see what’s driving engagement,

and if they’re living up to their brand promise.

• Ideally we’d use ERB’s proprietary channel data,

but they haven’t given us that…yet.

• This meant we had to look for public data sources.

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YouTube API (Version 3)

• YouTube’s open API provided us with:

• Video Title

• Date Posted

• Video Length (in seconds)

• # of Views

• # of Comments

• # of Likes and # of Dislikes

• Samples of Comments

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*Data was pulled the afternoon of August 23rd, 2016

Creating New Variables for Our Analysis

• From the YouTube data, we created new variables:

• Net Likes (Likes - Dislikes)

• Likes Ratio (Likes/Dislikes)

• Days Since Posting (August 23rd 2016 - Day Posted)

• Comments Per Days Since Posting (Comments/DSP)

• Views Per Days Since Posting (Views/DSP)

• Net Likes Per Days Since Posting (Net Likes/DSP)

• Comments Per Video Length (Comments/Video Length)

• Views Per Days Since Posting (Views/DSP)

• Net Likes Per Days Since Posting (Net Likes/DSP)

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Fan Voting Data from Fandom (Wikia)

• The producers of the show read their video

comments for future battle suggestions from fans.

• The comments also contain votes, but there’s no tally.

• Fandom runs a poll for each of the battles, so

we merged this data with the YouTube API data.

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NLP & Manual Quantification

• Natural language processing allowed patterns

to be discovered, such as the role of profanity in all

episodes and the comments.

• The gender of the challengers, and whether

they were real or fictional, were other variables

that we manually added to the data set.

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Mining Deep Using Open Source Tools

• This combined data set is available on our website.

• We used R & RStudio (both open-source) to run

the analysis, and Tableau to make the visualizations.

• We focused on answering the questions of what drives fan

engagement & if ERB is living up to its brand promise.

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Explaining Simply

What’s Driving Fan Engagement?

• How do the battles stack up against each other?

• Views, Comments, Net Likes, Likes Ratio.

• What about deflating these metrics by Time Since Posting?

• What role does profanity play in the battles?

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Comments & Views Across Episodes

• Comments

• Average (204,800) and the Median (170,900).

• One major outlier (606,951) – Barack Obama vs Mitt Romney.

• Views

• Average (49,470,000) and the Median (41,360,000).

• Same outlier (123,600,000) – Barack Obama vs Mitt Romney

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Total Views by Episode (Chronologically Ordered)

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Total Views by Episode (Chronologically Ordered & Combining Vader v Hitler Battles)

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Net Likes & Likes Ratio Across Episodes

• Net Likes

• Average (352,300) and the Median (340,200).

• Two outliers this time (871,720) – Barack Obama vs Mitt Romney

and (763,799) – Steve Jobs vs Bill Gates.

• Likes Ratio

• Average (40x) and the Median (39x).

• One outlier (104x) – J. R. R. Tolkien vs George R. R. Martin

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Likes & Dislikes by Episode (Chronologically Ordered)*

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*Source: y-axis increments are scaled differently

The Power & Prevalence of Profanity

• They are speaking the same language as their fans, and it’s profane.

• Average of 4 “traditional” profanities across battles. Hitler vs Vader #2 has the most

profanity at 11.

• Top profaner was Marilyn Monroe (at 8), though Darth Vader

had the most profanities per second (7 in 33 seconds).

• Eve had 2x the profanities (7) of Adam (3)

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Are They Living up to Their Brand Promise?

• Why are challengers winning their battles?

• How does gender and fictional status affect it?

• Are they getting better at picking fan battle ideas

that increase engagement?

• Views, Comments, Net Likes, Likes Ratio.

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Predicting Battle Winners

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• The longer a challenger raps, the higher the probability of

winning the battle.

• Gender didn’t seem to make a difference, but a real challenger

is significantly more likely to win against a fictional opponent.

• Each profanity increases the chance of winning by 11%.

Are They Getting Better over Time?

• Since each video has been available for different

amounts of time, we need to deflate everything

by the number of days since posting.

• This gives us a clearer picture of the relative

performance of each battle over time and allows

us to answer if they’re improving.

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Total Views per Day Since Posting by Episode (Chronologically Ordered)

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Likes Ratio per Day Since Posting by Episode (Chronologically Ordered)

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Likes & Dislikes per Day Since Posting by Episode (Chronologically Ordered)*

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*Source: y-axis increments are scaled differently

The End

Knowing Is Half the Battle

• Now you know:

• The story of a unique brand living up to its promise and engaging its fans.

• The story of how the data collection and analysis was done to be

able to tell this story.

• This is a process you can replicate by following the same

three steps: think broad, mine deep, explain simply.

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The Conversation Doesn’t Have to End Here…luciano@emperitas.com / 801-810-5869 / EmperitasSG / 4609 South 2300 East Suite 204, Holladay, UT 84117