JessupJamesPTRComprehensiveAssignment

James Jessup Analytics | Winter Park, FL 32792 | www.jamesjessup.com
Chipotle Mexican Grill Social Media
Pattern Analysis
Patterns and Recognition Comprehensive Assignment
JAMES JESSUP
3.25.2015

CONTENTS
INTRODUCTION……………………………….………………….…………………………….3
METHODOLOGIES ……………………………………………………………………….……3
BAYESIAN PROBABILITY OF INCREASED STOCK PRICE …………………………….3
COST OF DATA/ VALUE OF DATA …………………………………………..………….….4
DATA PATTERNS AND TRENDS ………………………………………………..…………..5
CLUSTERING …………………………………………………………………………………..6
CORRELATION ANALYSIS …………………………………………………………………..7
NETWORK ANALYSIS ……………………………………………………….………………..8
EMERGING TRENDS ……………………………………….…………………………..……..8
OPTIMIZATION ……………………………………………………………..……………….....9
TREND ANALYSIS AND FORECASTS …………………………………………...………..9
RECOMMENDATIONS ……………………………………………….………………..……..11
CONCLUSION……………………………………………………………………….………....11
APPENDIX………………………………………………………………………………………12
REFERENCES…………………………………………………………….……………………15

Chipotle Mexican Grill
Introduction
Chipotle Mexican Grill (NYSE: CMG) is a chain of Quick-Service-Restaurants that
serve Mexican-themed foods using high quality ingredients. Started in 1993, the
chain has 1600 restaurants. Their focus is on fresh, high-quality ingredients and
environmentally responsible business and food practices. Sales in 2013 were
$3.21 Billion, with an upward trend over the previous five years. (Jessup 2014)
Like many companies, Chipotle is taking advantage of social media trends. With
2.4 million likes on Facebook and 566K followers on Twitter, there are many
insights to be taken advantage of.
This report explores some of the patters observed from analysis of Chipotle’s
social media data.
Methodologies
Data was gathered about Chipotle’s Twitter account (#ChipotleTweets) using an
online site Twitonomy.com. Twitonomy collects information about Tweets,
Followers, ReTweets, Favorites, and summarizes this information. Twitter
provides 3200 tweets, which gave several days worth of information. The sample
size had to be limited to this amount.
Given more resources, a larger sample could be collected and analyzed.
Information about Chipotle’s Facebook account comes from
SimplyMeasured.com
Data about Chipotle’s stock price was provided by NASDAQ.
Bayesian Probability of Increased Stock Price
A Word Map of Chipotle’s Tweets provided the most common words used
in the text of their tweets. (SEE APPENDIX Figure 1) One of the most frequent
words of note was the word “Sorry”. This begs the question: Does use of the
word “Sorry” have an effect on the stock price?
This was converted into a question involving Bayes Theorem:
What is the probability of a positive stock price change, given a certain
ratio of comments with the word “Sorry” in them?

Data was collected and analyzed for two factors:
Dates with higher-than-average use of the word “Sorry”
Dates where the stock price had a positive change
Results:
CMG Price Change (A)
(+)
change (-) neg change
Sorry
Ratio (B)
>Average 5 2 7
<Average 1 2 3
6 4 10
P (A|B)=(P(B|A)*P(A)) / P(B)
P (A|B)= (5/7* 6/10) / 7/10 = .42857
Given a higher-than-average ratio of comments with the word “Sorry” in them,
there is a 43% chance the stock price will increase.
Conversely, there is a 66% chance that the stock price will have a positive
increase, given a lower-than-average “Sorry” ratio.
P (+change)|(<Average)= (1/3* 6/10) / 3/10 = .66
If the ratio of Tweets including the word “Sorry” goes below the average, there is
a higher-than-average chance of a positive stock price change. It would be wise
to monitor this, and when the opening price is down, look to the Twitter-verse and
see if there is anything that needs to be apologized for!
Cost of Data/ Value of Data
The cost of an annual subscription to Twitonomy is a very reasonable $199.00.
The average annual salary for a Social Media Manager in Denver CO (Chipotle’s
headquarters) is $45,775. (Payscale 2015)
The monetary value of Social Media Management is difficult to quantify. “56% of
marketers still list the ‘inability to tie social media to business outcomes’ as the
largest pain point of measuring social media ROI.” (Wong/ Forbes 2014)
If one could predict stock price increases with 66% accuracy, this alone would
prove invaluable.
A company’s ability to positively affect their own stock price, by responding to
social media would be similarly invaluable.
It is VASTLY worth the expense for a company of Chipotle’s size to measure and
monitor their social media presence.

Source:
http://www.socialmediaexaminer.com/SocialMediaMarketingIndustryReport2014.pdf

Data Patterns and Trends
Twitonomy provided data and summarizations of Chipotle’s Twitter usage,
followers and the text of the last 3200 tweets.
The relevant features that could be extracted were:
Date
Response or original Tweet
The agent who tweeted
Number of Retweets
Number of Favorites
Nasdaq provided the following information about the stock prices:
Closing Price
Volume of shares traded
Opening Price
Daily High
Daily Low
Change from Previous Day’s Closing Price
There was too little data to tease out Consumer Sentiment. It would have been
interested to see if one particular agent was associated with better sentiment. It
turns out, the agent DOES seem to be correlated to Stock Price movement. More
on that follows.
Some variables are both dependent variables AND independent variables.
Correlation analysis was done in a matrix to see which variables were most
strongly correlated.
Temporal trends were found for number of tweets, with daily trends peaking in
the evenings, and weekly trends peaking on Wednesdays.
(SEE APPENDIX, Figures 4, 5, 6, and 7 for trend visualizations)
Stock price tended to be higher Thursdays, but not significantly so.

Clustering
Facebook posts were analyzed using SimplyMeasured. They were grouped by
Type of post; Link, Photo, Status, Video or Other and these clusters were
analyzed for Engagement. This was compared to Chipotle’s competitor in the
Quick Service Restaurant, Quiznos Subs.
(Image provided by SimplyMeasured)
Users responded much more frequently to Images than other types of posts.
Additional suggested research would center on clustering Chipotle’s Twitter
followers.
These would be clustered by
Age
Gender
Marital status
Family status
Number of Followers
Number of Tweets
It would be possible to use K-Means clustering to assign Followers to a certain
cluster, given that the number of desired clusters is known.
Fuzzy C-means clustering would allow a user to belong to two or more clusters.
This method would not be recommended, as it would lead to overlap and
duplicate entries.
This level of detail about the Twitter followers was not available at this time, but is
recommended for future research.

Correlation Analysis
Correlation analysis was run between all the variables to see which ones were
most strongly correlated. This heat map shows areas of the greatest correlation
in Red and Green. Yellow shows weaker correlations.
Several strong correlations were discarded as obvious (Candice generates many
tweets, therefore, she has a high Tweet ratio; Opening price is related to closing
price.)
Significant correlations were sought across categories.
The number of Tweets containing the word “Sorry” had a strong negative
correlation with Opening Stock Price, Daily High Stock Price and Daily Low stock
price. It was moderately positively correlated with the volume and the Change in
Stock price.
Joe is positively correlated with the closing price. He tweets, and the price goes
up.
James and Myra are negatively correlated with the stock price. When they Tweet,
the Price goes down.
Wayne and James are moderately correlated with a positive change in stock
price.
It should be noted that these are Correlations and do not imply causation.
To test for temporal associations, the correlations were run using both the
preceding day and the following day’s stock prices (to see if a low stock price one
day caused and increase in Tweets the following day, or vice verse). The
correlations were generally lower, lending to the idea that these correlations are
not causal, but coincidentally associated.
Individual tweets are sometimes spatially associated. These tweets are about
customer service issues at individual locations, rather than affecting Chipotle on
a national or global level.

Network Analysis
Network analysis can be used to see how Chipotle’s Super-Users use
social media to influence others.
Users
most

retweeted

@FoodRev
(1),
@XavierMission
(1),
@DaniNierenberg
(1),
@sarahfoodtank
(1),

@esmailyas
(1),
@SKCAcademy
(1),
@thenataliegray
(1),
@bleachersmusic
(1)

Users
most

replied
to

@StephenAnfield
(11),
@nathanieljlars
(9),
@tikaliciousss
(8),
@KD_TRE
(7),

@waryhulk
(7),
@willythewormm
(6),
@RangersFan4205
(6),
@NYFarmer
(5),

@meganxbrown
(5),
@the1bark
(5)

Users
most

mentioned

@ChipotleUK
(16),
@StephenAnfield
(11),
@TEDxManhattan
(9),
@nathanieljlars

(9),
@tikaliciousss
(8),
@KD_TRE
(7),
@waryhulk
(7),
@RangersFan4205
(6),

@willythewormm
(6),
@_amberstars_
(5)

Wolfram Alpha has an excellent Network Analysis tool that allows users to see
the clusters within their network. Campaigns can be developed to specifically
target each cluster.
(image source Wolphram Alpha)
SimplyMeasured also provides a Content Detail report that answers the question:
Which individual posts are successful? This can be linked to clusters and
networked to mount a successful marketing campaign.
(SEE APPENDIX Figure 3)
Emerging Trends
The data provided produced the following trends:
Number of Tweets peaked in the evenings. (SEE APPENDIX Figure 4)
Number of Tweets peaked Wednesday. (SEE APPENDIX Figure 5)
Candice is the primary Tweeter. (SEE APPENDIX Figure 4)

Optimization
With regards to Stock Price, the more times the word “Sorry” is tweeted, the
greater the chance of a positive change in stock price. It helps if Joe, Wayne or
James tweet, also.
More thorough data would be needed and a more thorough analysis of followers
to optimize a social media campaign. Optimization may not be quantifiable from a
social media standpoint, but it can be improved, compared to either sales or
stock price. Social media DOES affect bottom line, as noted in the correlation
between “Sorry” appearing in a tweet, and positive change in stock price.
Optimization may be as simple as having someone on Twitter and Facebook to
respond to customer issues. Currently, it’s important enough to Chipotle to have
someone doing this 24 hours a day, seven days a week.
There are many tools to improve Facebook and Twitter Analytics. (SproutSocial,
Topsy, Tweetonomy, Tweetreach) Many of these offer a “per query” price in the
$1200 range. This was out of the scope of the budget for this report, however an
enterprise-wide initiative to thoroughly explore the Twitterverse would be wise to
engage one of these products. One of the other requirements is access to the
Twitter account. Such access was not available for this research.
Trend Analysis and Forecasts
Chipotle’s stock price tends to be highest on Thursdays.
Chipotle’s Tweets peak Wednesday evening.
Chipotle’s stock is lowest Monday, following two days of decreased Twitter
activity.
This may well be coincidental.
Thursday’s average increase over Monday is only 0.5% of Chipotle’s stock price.
This is not enough to warrant action (at this point) but it IS worth observing and
noting, should future data provide stronger conclusions.
Chipotle’s stock does not appear to have short-term trends. Over the last five
years, they’ve shown an average of 114 points per year in growth.

Next week, the anticipated price will be $682 (+/- 6.22)
Over the next month, expect gains in the realm of $9.36, to a price of $689, (with
a margin of error of $8.47).
A year from now, the linear regression model supports a stock price of $794 per
share. Estimated margin of error would be $61.53.
Chipotle gains new Twitter followers every day. As long as they continue to stick
to their corporate values of responsibility, fresh food and sustainable growth, I
see no impediment to this trend.
0

200

400

600

800

16:00

5/19/10

7/19/10

9/15/10

11/11/10

1/11/11

3/11/11

5/10/11

7/8/11

9/6/11

11/2/11

1/3/12

3/2/12

5/1/12

6/28/12

8/27/12

10/24/12

12/26/12

2/26/13

4/25/13

6/24/13

8/21/13

10/18/13

12/17/13

2/18/14

4/16/14

6/16/14

8/13/14

10/10/14

12/9/14

2/9/15

CMG
Stock
Price
rises
114
points
annually

0

200

400

600

800

1000

3/23/10
3/23/11
3/23/12
3/23/13
3/23/14
3/23/15
3/23/16

Closing
Price
with
Projections

Closing
Price
Projected
Linear
(Closing
Price)

Recommendations
The data leads to three recommendations:
It is worthwhile to analyze who is responding to Chipotle’s Social Media. There
needs to be greater analysis of this and how Chipotle’s responses affect
consumer sentiment and sales. These need to be quantified and managed
strategically, aligned with Chipotle’s business objectives. Wolfram Alpha
analytics can help with this, as well as creating a consistent message and
strategy within the organization. Best practices should be documented, quantified
and spread.
Stock price is vulnerable to outside forces. This is true with all businesses. IT is
recommended that these correlations be more thoroughly understood so they
can be exploited. If one staff member has the power to raise stock prices, this
should be used strategically. Algorithms could be developed to assist with this.
ModelRisk is a statistical software package that can assist with this.
Customer service is critical. The correlation between stock price and “Sorry”,
though tenuous, does show a link between consumer sentiment and consumer
confidence in a brand. This needs to be better understood. More detailed twitter
analysis from Gnip will be useful.
Conclusion
Social media’s affect on a Stock’s price is not easily understood, but easily
felt. In the competitive environment of Quick Service Restaurants, the customers
want a company that will not only fill their stomachs, but warms their hearts.
Understanding consumer sentiment through social media analysis will yield rich
returns in consumer sentiment insights. The data is given freely by the
consumers; it’s up to the corporations to make the most of it.

Appendix

Figure
1.
Word
Map
of
Chipotle’s
Tweets


Figure
2.
Heat
Map
of
Correlation
Matrix

Figure
3.
SimplyMeasured
Content
Detail

Figure
4.
Tweets
by
Time
of
Day

0

100

200

300

400

12am

1am

2am

3am

4am

5am

6am

7am

8am

9am

10am

11am

12pm

1pm

2pm

3pm

4pm

5pm

6pm

7pm

8pm

9pm

10pm

11pm

#
of
Tweets
peaks
in
the
Evening


Figure
5.
Daily
average
Tweets

Figure
6.
Number
of
Tweets
per
agent

Figure
7.
Average
Stock
Price

0

200

400

600

800

Sun

Mon

Tue

Wed

Thu

Fri

Sat

#
of
Tweets
peaks
Wednesday

0

200

400

600

800

1000

1200

Candice
Joe
Rusty
James
Skyllo
Myra
Wayne

Candice
is
the
Primary
Tweeter

664

664.5

665

665.5

666

666.5

667

667.5

668

668.5

669

Monday
Tuesday
Wednesday
Thursday
Friday

Average
Stock
Price
Dluctuates
0.5%

References
Chipotle: About Us. (n.d.). Retrieved November 21, 2014, from
http://www.chipotle.com/en-us/company/about_us.aspx
Chipotle Mexican Grill, Inc. Historical Stock Prices. (n.d.). Retrieved March 24,
2015, from http://www.nasdaq.com/symbol/cmg/historical
Jessup, J. (2014, November 21) Bigger Better Faster More. Full Sail University.
Winter Park, FL
Social Media Manager Salary in Denver, Colorado (United States) United States
Home Change Country Don't see what you are looking for?Get A Free Custom
Salary Report ». (n.d.). Retrieved March 24, 2015, from
http://www.payscale.com/research/US/Job=Social_Media_Manager/Salary/1e12d
e1c/Denver-CO
Stelzner, M. (2014, May 1). Social Media Marketing Industry Report. Retrieved
March 24, 2015, from
http://www.socialmediaexaminer.com/SocialMediaMarketingIndustryReport2014.
pdf
Wong, K. (2014, May 13). What Is The Value Of Social Media Engagement?
Retrieved March 24, 2015, from
http://www.forbes.com/sites/kylewong/2014/05/13/what-is-the-value-of-social-
media-engagement/

THANK YOU.
FOR MORE INFORMATION CONTACT: JAMES JESSUP
JJESSUP@FULLSAIL.COM

JessupJamesPTRComprehensiveAssignment

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