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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




                                                  Please tweet!
                                              #RNWebinars @LoveStats
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
                                           Please tweet!
1                                      #RNWebinars @LoveStats
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?”



                                                 Please tweet!
2                                            #RNWebinars @LoveStats
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

                                                                 Please tweet!
3                                                            #RNWebinars @LoveStats
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


                                                                    Please tweet!
4                                                               #RNWebinars @LoveStats
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




5                                                                                 Please tweet!
                                                                              #RNWebinars @LoveStats
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
                                                                            Please tweet!
6   treated as pair-wise missing, Minimum sample size per brand n>=30
                                                                        #RNWebinars @LoveStats
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
                                                                            Please tweet!
7   treated as pair-wise missing, Minimum sample size per brand n>=30
                                                                        #RNWebinars @LoveStats
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
                                                                                   Please tweet!
8   pair-wise missing, Minimum sample size per feature n>=30
                                                                               #RNWebinars @LoveStats
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
                                                                                     Please tweet!
9   pair-wise missing, Minimum sample size per feature n>=30
                                                                                 #RNWebinars @LoveStats
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


10                                                        Please tweet!
                                                      #RNWebinars @LoveStats
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
                                                                                              Please tweet!
                                                                                          #RNWebinars @LoveStats
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
                                                                                         Please tweet!
opinion, Subsample required mention of both purchase and Jeep brand
                                                                                     #RNWebinars @LoveStats
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
                                                                                         Please tweet!
opinion, Subsample required mention of purchase, Jeep brand, and female author
                                                                                     #RNWebinars @LoveStats
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



                                                     Please tweet!
14                                               #RNWebinars @LoveStats
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.”



15                                                   Please tweet!
                                                 #RNWebinars @LoveStats
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




16                                               Please tweet!
                                             #RNWebinars @LoveStats
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,
                                                                                                 Please tweet!
Minimum sample size per brand n>=30, 11 factors based on scree plot
                                                                                             #RNWebinars @LoveStats
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,
                                                                                    Please tweet!
Minimum sample size per feature n>=30, 17 factors based on scree plot
                                                                                #RNWebinars @LoveStats
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.”




19                                                         Please tweet!
                                                       #RNWebinars @LoveStats
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




20                                                              Please tweet!
                                                            #RNWebinars @LoveStats
Thank you




                 hello@conversition.com

                 www.conversition.com




                                      Please tweet!
21                                #RNWebinars @LoveStats

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Advanced Analytics for Social Media Research

  • 1. Advanced Analytics for Social Media Research: Examples from the automotive industry January 2013 Social media listening data by researchers, for researchers Please tweet! #RNWebinars @LoveStats
  • 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 Please tweet! 1 #RNWebinars @LoveStats
  • 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?” Please tweet! 2 #RNWebinars @LoveStats
  • 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 Please tweet! 3 #RNWebinars @LoveStats
  • 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 Please tweet! 4 #RNWebinars @LoveStats
  • 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 5 Please tweet! #RNWebinars @LoveStats
  • 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 Please tweet! 6 treated as pair-wise missing, Minimum sample size per brand n>=30 #RNWebinars @LoveStats
  • 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 Please tweet! 7 treated as pair-wise missing, Minimum sample size per brand n>=30 #RNWebinars @LoveStats
  • 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 Please tweet! 8 pair-wise missing, Minimum sample size per feature n>=30 #RNWebinars @LoveStats
  • 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 Please tweet! 9 pair-wise missing, Minimum sample size per feature n>=30 #RNWebinars @LoveStats
  • 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 10 Please tweet! #RNWebinars @LoveStats
  • 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 Please tweet! #RNWebinars @LoveStats
  • 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 Please tweet! opinion, Subsample required mention of both purchase and Jeep brand #RNWebinars @LoveStats
  • 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 Please tweet! opinion, Subsample required mention of purchase, Jeep brand, and female author #RNWebinars @LoveStats
  • 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 Please tweet! 14 #RNWebinars @LoveStats
  • 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.” 15 Please tweet! #RNWebinars @LoveStats
  • 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 16 Please tweet! #RNWebinars @LoveStats
  • 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, Please tweet! Minimum sample size per brand n>=30, 11 factors based on scree plot #RNWebinars @LoveStats
  • 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, Please tweet! Minimum sample size per feature n>=30, 17 factors based on scree plot #RNWebinars @LoveStats
  • 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.” 19 Please tweet! #RNWebinars @LoveStats
  • 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 20 Please tweet! #RNWebinars @LoveStats
  • 22. Thank you hello@conversition.com www.conversition.com Please tweet! 21 #RNWebinars @LoveStats