Lab1

Brand Strategy and Super
Bowl Twitter Analytics Lab

Image Source: Adweek.com

INTRODUCTION

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Twitter popularity as 2nd screen

Infographic Source: Fortune, 2015

About Twitter

4

Twitter Stats (cont’d)

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About Super Bowl Ads

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Motivation
• Cost per 30 seconds slot – 5 mil (CNNMoney, 2017).
• Brand strategy in Super Bowl ads? Enhance &

reach.
• The role of IS tools in this strategy?

– Analytics of audience (demographics, locations etc.).
– Supplementary info for future decision making.

• What is the relationship between Social Media
buzz around Super Bowl ads with ad ratings?
– Twitter activities surrounding SB ads with USA Today

Index ratings (USA Today)?

3 Tweet Examples
Those Budweiser commercials had me balling my eyes
out!! #Budweiser #SuperBowl #horsepuppy #tearjerker

Thanks #CocaCola & #Cheerios for showing U.S.
multicultural families and successfully including
diverse markets #adbowl #AmericaIsBeautiful

Scarlett Johansson should realize that the only real
flavor of #SodaStream is oppression
#superbowl

Objective
• Learn to extract valuable metrics from social

media data using MS Excel.
• Know what insights are valuable to brands.
• Simple introduction to (review on) descriptive

statistics, correlation, charts, regressions, and
word clouds.

• Lab instructions become more vague as lab
progresses to encourage student self learning.

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Lab Setup
• Download this presentation and watch VIDEO.
• Download the SB 2014 spreadsheet — This is a

summary of actual tweets *downloaded using
PHP and MySQL scripts (done prior).

• Download the doritos.txt tweet text file*.
• You will need MS Excel with Data Analysis

ToolPak available.
• Prepare a Word doc for submission.

Research Question
• Start with research question:

– Is social media (Twitter) an effective tool for
measuring brand performance (SB ads)?

• How?
– Find a relationship between Twitter metrics and SB

performance.
– If a relationship exists then social media may be a

valuable (real time, low cost) monitoring mechanism
in addition to existing tools.

– In addition, social media provides rich feedback
(WOM, influencer, competitors, etc.).

ANALYSIS

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Analysis
1. Descriptive Statistics
2. Correlation Analysis
3. Charts (Bar chart and scatter plot)
4. OLS Regression
5. Word Cloud

SB 2014* Dataset

• Super Bowl 2014 Ads
• 51 Ads
• Contains social media measures and USA

Today index for each ad.
• Measures extracted from downloading and

analyzing tweets for each ad.

*Seattle Seahawks beat Denver Broncos (43-8) 14

Dataset

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Variables
• USA Index – Super Bowl ad performance index from

USA Today. This is the outcome variable.
• RT –retweets for each ad.
• Tweet Count – tweets for each ad.
• Unique sender – count of people talking about the

ad.
• Pre-release – (1/0) whether this ad was release

before the Super Bowl game.
• Celebrity – (1/0) whether a celebrity(s) is in the ad.

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Descriptive Statistics
1. Open SB 2014 spreadsheet in Excel
2. Go to Data > Data Analysis > Descriptive Analysis
3. For the Input Range, highlight all the number cells

(including labels) but excluding Ad Name and brand
columns.

Descriptive Statistics
4. Select ‘labels in first row’ and ‘summary

statistics’

Descriptive Statistics
5. Results appear in next Sheet.
6. Copy Result to Word doc.

Descriptive Statistics
7. Discuss result. Focus on average, min, max

for:
– USA Index
– Tweet count
– Unique sender
– RT

Correlation Analysis
1. Select Correlation then OK.

2. In the Correlation window, make sure all
number cells are selected then click OK.

Correlation Analysis
3. Results will appear in next Sheet.
4. Discuss which variables are highly correlated. Why?
5. Copy Result to Word doc.

Bar Chart
• Select the tweet count column and sort by largest to

smallest.

Bar Chart
• Insert a Bar chart of ‘tweet count’ and ‘ad names’.

Discuss what you found.
• Add to Word doc.

Scatter Plot
• Do a Scatter Plot of ‘Tweet count with USA Index’

and discuss. Add the trend line. What does the trend
imply? Add to Word doc.

What is OLS linear regression?
• OLS Linear regression is the most basic and

commonly used predictive analysis. Regression
estimates are used to describe data and to explain
the relationship between one dependent variable (y)
and one or more independent variables (x).

• At the center of the regression analysis is the task of
fitting a single line through a scatter plot. The
simplest form with one dependent and one
independent variable is defined by the formula
y = a + b*x. 26

1. Select Data Analysis and run regression.
2. Select USA Index for Y Range and all others (except

ad name, brand, and USA Index) for X Range. Select
labels and confidence level at 95%. Then click OK.

OLS Regression (USA Index)

3. Copy to Word doc.
OLS Regression (USA Index)

4. What to report?
– Adjusted R-squared
– Variables with p value < .10 (only unique senders)

5. Discuss Results. What variables can explain
Y? You are expected to use your knowledge
from your stats class here. What can you
imply from this result?

OLS Regression (USA Index)

Word Cloud
1. Go to
2. Click File->Open text file
3. Upload the ‘doritos’ tweet text file.

Word Cloud
1. The word cloud for ‘doritos’ should appear

(see pic to the right).
2. Discuss word cloud.
3. Why are certain terms

more frequently used?
4. How can doritos use this

strategically?

Summary Paragraph
• Write a summary paragraph for lab.
• Describe what you have learned.
• What IS tools are used?
• How does this lab help you to understand the

relationship between organizational strategy and IS
tools?

• How can these tools/techniques help with decision
making?

• Can this be applied in your future career?
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SUBMISSION

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Lab Submission
• In a Word doc, insert the following:

1. Descriptive Statistics
2. Correlation
3. 2 Charts (Bar Chart and Scatter Plot)
4. Regression Result
5. Word Cloud
6. Summary paragraph

• Organize well to help easier grading == higher scores.
• Submit on canvas

END