This document summarizes an webinar on advanced social media analytics for the automotive industry. It provides examples of using correlations to analyze how gender relates to brand and feature preferences. It also discusses using regression to predict vehicle purchases based on opinions of attributes and features. Finally, it explains how factor analysis can group brands and attributes that consumers commonly associate to better understand competitive landscapes.

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Advanced Analytics for Social Media Research:
Examples from the automotive industry
January 2013
Social media listening data by researchers, for researchers

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Track brand mentions
Identify positive and negative brand attributes
Identify sources of negativity
Monitor an ad campaign
Measure category norms
Standard Social Media Research Uses
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3
4
5

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Correlations – How does gender correlate with brand choice?
Which brands and features are preferred by men and by women?
Regression – Which features best predict purchase of
specific brands? How do combinations of variables work
together to predict an overarching variable?
Factor analysis – How do brands or features
cluster together as being similar in consumer’s
minds? What clusters “appear”? What is the best
“package?”
Advanced Social Media Research Uses
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Data + Category Experts = Insights
3
Expert methodologists
collecting, cleaning, coding, and
calibrating data specific to your
research objectives
Industry analysts using category
and normative expertise to
analyze and interpret data
Relevant, valid, and reliable
conclusions, insights, and
recommendations
YourLogoHere

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Research Method
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Datasets
1. Branded: Random sample of verbatims
mentioning a brand name
(e.g., GMC, Honda, Lexus). To measure
correlations.
• N>250 000
2. Branded purchasing: Random sample of
verbatims mentioning a brand and purchase. To
predict purchase.
N>100 000
3. Branded pairs: Random sample of verbatims
mentioning at least TWO brand names. To run
brand factor analysis.
• N>100 000
Data Collection Criteria
• Consumer focus
• Dealership messaging removed
• Viral games and jokes removed
Collect
Clean
Categorize
Calibrate
• Clean out spam and non-
relevant chatter (e.g., fun
engagement conversations
on Facebook)
• Scour the internet for
thousands of messages
related to the brand
• Categorize verbatims into
relevant content
areas, e.g., pricing, recomm
endations, commercials, cel
ebrities
• Calibrate the sentiment into
5-point Likert scale buckets
specific to the brand and
category

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What is a correlation?
A statistical process for identifying how two variables relate with
each other.
• E.g., there exists a positive correlation between
education and price paid for vehicles
– Expensive cars tend to be owned by people with higher education
– Budget cars tend to be owned by people with lower education
– A correlation does not mean one variable causes the other. Sending an
uneducated person to school will not cause them to buy an expensive
car nor vice versa. The more likely scenario is that higher education
leads to higher income which enables one to purchase a more
expensive vehicle, if desired.
R=0.3 R=0.15
R=0.0
1

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Correlations: Women’s Brand Preferences
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Women are more likely than men to speak positively about
midsize vehicles and base level SUVs.
Lexus (r=0.34)
Nissan Pathfinder (r=0.34)
Nissan Maxima (r=0.31)
Peugeot (r=0.28)
BMW X5 (r=0.27)
Chevrolet Impala (r=0.25)
Mitsubishi Eclipse (r=0.25)
e.g., 6% of the variance in positive opinions about
Lexus can be attributed to gender (r=0.34)
Analysis: Gender must be specified (n=56 000), Brand non-mention
treated as pair-wise missing, Minimum sample size per brand n>=30

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Correlations: Men’s Brand Preferences
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Men are more likely to speak positively about sporty cars
and adventure trucks.
Jeep Safari (r=0.32)
GMC Yukon (r=0.22)
Ford Fiesta (r=0.17)
Mazda Miata (r=0.11)
Toyota Tacoma (r=0.10)
Ford Mustang (r=0.10)
e.g., 5.6% of the variance in positive opinions about
Jeep Safari can be attributed to gender (r=0.32)
Analysis: Gender must be specified (n=56 000), Brand non-mention
treated as pair-wise missing, Minimum sample size per brand n>=30

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Correlations: Women’s Feature Preferences
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Stereotypes abound as women chat more positively about easy
driving (e.g., suspension) and appearance (e.g., dashboard)
features.
Grill (r = 0.38)
Suspension (r = 0.36)
Dashboard (r = 0.35)
Interior (r = 0.33)
Steering (r = 0.32)
(High correlation with
automatic transmission but
sample size was only 17)
Analysis: Gender specified (n=56 000), Feature non-mention treated as
pair-wise missing, Minimum sample size per feature n>=30

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Correlations: Men’s Feature Preferences
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Stereotypes continue as men chat positively about blasting
their tunes (i.e. radio) and speeding (i.e. accelerator).
Car Radio (r=0.38)
Accelerator (r=0.11)
Headlight (r=0.10)
(High correlation with
manual transmission but
sample size was only 25)
Analysis: Gender specified (n=56 000), Feature non-mention treated as
pair-wise missing, Minimum sample size per feature n>=30

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What is Regression?
A statistical method for estimating relationships among variables. To
determine whether and by how much the change in the value of one
variable affects the value of another variable.
Purchase
2 X
Variable
A
1 X
Variable
B
0.5 X
Variable
C
+= +
Can we determine which variables influence purchase opinions?
• Is it a simple or complex relationship with few or many variables?
• Do these relationships differ based on the brand?
 We can then focus our marketing attention in these areas with the appropriate
level of importance
2

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Explaining Past Purchase
People who have purchased a vehicle focus on quality (e.g., servicing,
errors), personality characteristics (e.g., honesty, pride), and features
(e.g., color, size, fuel economy)
• Variables to account for 30% of variance: 17
• Variables to account for total variance (40%): 118
• Variables excluded from total : 200
• Key Variables:
Color, Servicing, Errors, Functionality, Size, Recommend,
Engine, Intelligence, Honesty, Pride, Fast, Fuel
Economy, Ease, Doors, Wheels
Positive
Purchase
Opinion
Servicing
X 0.12
Recomm
end X
0.11
Honesty
X 0.08+= +
Fuel
Economy
X 0.08
+
Analysis: n>36 000, Exploratory stepwise, Feature non-mention recoded as neutral
opinion, Subsample required mention of past purchase

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Explaining Purchases of Jeep
People who have purchased a Jeep talk more positively their vehicle being
highly functional, requiring few repairs, and being sexy in appearance.
• Number of variables: 23
• % of Variance accounted for: 30%
• Positive Variables: Truck types,
Functionality, Intelligence, Doors, Error, Size,
Engine, Servicing, Tires, Repairs, Exciting,
Wheels, Sexy, Transmission, Different
Positive
Purchase
Opinion
Types X
0.13
Doors X
0.11
Engine X
0.10+= + Sexy X
0.07+
Analysis: n>4600, Exploratory stepwise, Feature non-mention treated as neutral
opinion, Subsample required mention of both purchase and Jeep brand

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Explaining Women’s Purchases of Jeep
Women who have purchased a Jeep talk more positively about their
vehicle in terms of pride, reliability (e.g., errors, servicing), and
appearance (e.g., hubcaps, fashionable)
• Number of variables: 15
• % of Variance accounted for: 27%
• Key Variables: Pride, Error, Truck
Types, Size, Honesty, Cleanliness, Servicing,
Doors, Brakes, Warranty, Hubcaps, Fashiona
ble, Intelligence
Positive
Purchase
Opinion
Pride X
0.19
Error X
0.13
Honesty
X 0.10+= + Fashion
X 0.09+
Analysis: n>460, Exploratory stepwise, Feature non-mention treated as neutral
opinion, Subsample required mention of purchase, Jeep brand, and female author

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What is Factor Analysis?
A statistic for determining which variables or brand names or product features
are commonly associated with each other. The reader’s task is to determine why
statistics put those items together and “name” the over-arching concept.
Medium
X-
small
Small
X-large
Large
Polyester Velvet
Leather
Cotton
Nylon
Silk
What is Factor #1? Sizes What is Factor #2? Fabric
3

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Factor Analysis Data
To run a factor analysis, each piece of data must incorporate at
least two brand (or feature) mentions
• “In a few years, I want a red or black Range Rover and a sports car. Maybe a
BMW or Mercedes.”
• “I need to know if I should get the 2 door bmw or 4 door mazda 3. Help me
guys!”
• “Toyota Land Cruiser is way better than jeep in every way. With that price, it
had better be.”
• “Would you buy a Mercury Mountaineer with lower miles or a Lexus with
higher miles? Thanks for your help.”

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How to Use Factor Analysis
• Identify the real competitive set, not what
researchers or brand managers assume or assign
• Better understand consumer perceptions of your
brand
• Discover new ways that consumers think about
your brand
• Market against the most relevant competitors

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Results: Automotive Brands
Consumers categorize vehicles by size, adventurousness, and
luxuriousness.
Ferrari, Porsc
he, Audi
R8, BMW
M3, Ford
Mustang
Luxury
Chrysler, Jeep
, Dodge, Cher
okee, Explore
r, Mustang
Trucks
Peugeot, Kia,
VW
Golf, Peugeot
206, VW
Passat
Subcompact
Pontiac, Olds
mobile
Cutlass, Buick
, Taurus
Midsize
Toyota
Yaris, Prius, Ki
a, Miata, Niss
an Maxima
Fashionably
Friendly
Your real
competitors
How consumers
categorize you
Analysis: n=75 000, Equimax rotation, Nonresponse recoded as
neutral, Minimum sample size per brand n>=30, 11 factors based on scree
plot

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Results: Automotive Features
Consumers categorize features into many buckets, some focused on the
interior or exterior appearance, while others are focused on specific
systems, such as fuel or drive system.
ABS, Traction
control, Airb
ags, Tire
Pressure
Safety
RWD, FWD,
AWD, 4WD, T
urbo, Horsep
ower
Drive Systems
Fuel
supply, Fuel
tank, Air
intake. Spark
plug
Fuel System
Black, White,
Red, Blue,
Green, Pink,
Yellow
Colors
Hubcaps, Chr
ome, Bumpe
r, Grill, Headl
ight
Exterior
Appearance
Dashboard, B
eige, Pink, M
irrors, Cup
holder
Interior
Appearance
Engine, Hors
epower, Turb
o, Torque, M
anual
Power
Hybrid, Elect
ric
cars, Coupe,
Fuel
economy
Fuel Economy
Analysis: n=100 000, Equimax rotation, Nonresponse coded as
neutral, Minimum sample size per feature n>=30, 17 factors based on scree
plot

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What about conjoint?
Unfortunately, social media research is not ideal for running
conjoint analyses. Surveys are much better suited to this need.
• Frequency of direct comparisons of one product feature in one social media
sentiment: Extremely rare
• Ability to isolate two distinct opinions and apply the appropriate sentiment to
each: Extremely difficult
“It pains me to see a price of $22k but if they offer $18k, I’ll take it.”
“I can’t afford $25k so I’m pumped for when the price comes down to $23k.”

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Watchouts
Irrelevant data, spam, and viral jokes create false correlations between
brands. If this data is not removed prior to the analysis, statistics will
erroneously identify them as real associations.
• Irrelevant data
– Come test drive this 2010 Chevrolet Malibu LT. We also have the
Impala, Toyota Camry, Honda Accord, Nissan Altima, and Ford Fusion.
• Spam
– free perscription volvo bieber gaga nike honda adidas free fedex
saturday delivery toyota britney
• Viral Jokes
– Boyfriend: see that new, red mercedes benz parked beside our
neighbour’s ferrari? Girlfriend: whoooa! its gorgeous! Boyfriend: yeah
... I bought you a toothbrush of that colour
!!

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Thank you
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