Storytelling with data think broad, mine deep, explain simply
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Transcript of Storytelling with data think broad, mine deep, explain simply
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…[email protected] / 801-810-5869 / EmperitasSG / 4609 South 2300 East Suite 204, Holladay, UT 84117