Advanced Analytics for Social Media Research: Examples from the automotive industry (January 2013 Webinar)

1. 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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2. Standard Social Media Research Uses
1 Track brand mentions
2 Identify positive and negative brand attributes
3 Identify sources of negativity
4 Monitor an ad campaign
5 Measure category norms
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3. Advanced Social Media Research Uses
1 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
2 specific brands? How do combinations of variables work
together to predict an overarching variable?
Factor analysis – How do brands or features
3 cluster together as being similar in consumer’s
minds? What clusters “appear”? What is the best
“package?”
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4. Data + Category Experts = Insights
Expert methodologists collecting,
cleaning, coding, and calibrating
data specific to your research
objectives
Industry analysts using category
and normative expertise to YourLogoHere
analyze and interpret data
Relevant, valid, and reliable
conclusions, insights, and
recommendations
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5. Research Method
Datasets
• Scour the internet for
Collect thousands of messages
1. Branded: Random sample of verbatims
mentioning a brand name (e.g., GMC, Honda, related to the brand
Lexus). To measure correlations.
• N>250 000 • Clean out spam and non-
relevant chatter (e.g., fun
Clean engagement conversations
2. Branded purchasing: Random sample of
verbatims mentioning a brand and purchase. To on Facebook)
predict purchase.
N>100 000
• Categorize verbatims into
3. Branded pairs: Random sample of verbatims Categorize relevant content areas, e.g.,
mentioning at least TWO brand names. To run pricing, recommendations,
brand factor analysis. commercials, celebrities
• N>100 000
• Calibrate the sentiment into
Calibrate 5-point Likert scale buckets
Data Collection Criteria specific to the brand and
category
• Consumer focus
• Dealership messaging removed
• Viral games and jokes removed
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6. 1
What is a correlation?
A statistical process for identifying how two variables relate with
each other.
R=0.0
• 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
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7. Correlations: Women’s Brand Preferences
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
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6 treated as pair-wise missing, Minimum sample size per brand n>=30
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8. Correlations: Men’s Brand Preferences
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
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7 treated as pair-wise missing, Minimum sample size per brand n>=30
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9. Correlations: Women’s Feature Preferences
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
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8 pair-wise missing, Minimum sample size per feature n>=30
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10. Correlations: Men’s Feature Preferences
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
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9 pair-wise missing, Minimum sample size per feature n>=30
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11. 2
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.
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
2X 1X 0.5 X
Purchase = Variable
A
+ Variable
B
+ Variable
C
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12. 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 Recomm Fuel
Purchase
Opinion
= Servicing
X 0.12 + end X
0.11
+ Honesty
X 0.08 + Economy
X 0.08
Analysis: n>36 000, Exploratory stepwise, Feature non-mention recoded as neutral
11
opinion, Subsample required mention of past purchase
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13. 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
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opinion, Subsample required mention of both purchase and Jeep brand
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14. 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, Fashionable,
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
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opinion, Subsample required mention of purchase, Jeep brand, and female author
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15. 3
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.
What is Factor #1? Sizes What is Factor #2? Fabric
Large
Leather
Polyester Velvet
Medium Small
Cotton
Nylon
X-
small X-large Silk
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16. 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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17. 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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18. Results: Automotive Brands
Consumers categorize vehicles by size, adventurousness, and
luxuriousness.
How consumers Subcompact Midsize Luxury
categorize you
Peugeot, Kia, Pontiac, Ferrari,
VW Golf, Oldsmobile Porsche, Audi
Peugeot 206, Cutlass, R8, BMW M3,
VW Passat Buick, Taurus Ford Mustang
Fashionably
Trucks
Friendly
Chrysler,
Toyota Yaris,
Jeep, Dodge,
Your real Prius, Kia,
Cherokee,
Miata, Nissan
competitors Maxima
Explorer,
Mustang
Analysis: n=75 000, Equimax rotation, Nonresponse recoded as neutral,
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Minimum sample size per brand n>=30, 11 factors based on scree plot
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19. 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.
Exterior Interior
Fuel Economy Power
Appearance Appearance
Hubcaps, Engine,
Dashboard, Hybrid, Horsepower,
Chrome, Electric cars,
Beige, Pink, Turbo,
Bumper, Coupe, Fuel
Mirrors, Cup Torque,
Grill, economy
holder Manual
Headlight
Safety Fuel System Colors Drive Systems
ABS, Traction Fuel supply, Black, White, RWD, FWD,
control, Fuel tank, Air Red, Blue, AWD, 4WD,
Airbags, Tire intake. Spark Green, Pink, Turbo,
Pressure plug Yellow Horsepower
Analysis: n=100 000, Equimax rotation, Nonresponse coded as neutral,
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Minimum sample size per feature n>=30, 17 factors based on scree plot
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20. 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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21. 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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22. Thank you
hello@conversition.com
www.conversition.com
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