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Advanced Analytics for Social Media Research


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

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Advanced Analytics for Social Media Research

  1. 1. Advanced Analytics for Social Media Research:Examples from the automotive industryJanuary 2013Social media listening data by researchers, for researchers Please tweet! #RNWebinars @LoveStats
  2. 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. 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. 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. 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. 6. 1What is a correlation?A statistical process for identifying how two variables relate witheach 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=0.155 Please tweet! #RNWebinars @LoveStats
  7. 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. 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. 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. 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. 11. 2What is Regression?A statistical method for estimating relationships among variables. Todetermine whether and by how much the change in the value of onevariable 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 C10 Please tweet! #RNWebinars @LoveStats
  12. 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 neutral11 opinion, Subsample required mention of past purchase Please tweet! #RNWebinars @LoveStats
  13. 13. Explaining Purchases of JeepPeople who have purchased a Jeep talk more positively their vehicle beinghighly 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 PositivePurchaseOpinion = Types X 0.13 + Doors X 0.11 + Engine X 0.10 + Sexy X 0.07Analysis: 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. 14. Explaining Women’s Purchases of JeepWomen who have purchased a Jeep talk more positively about theirvehicle in terms of pride, reliability (e.g., errors, servicing), andappearance (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 PositivePurchaseOpinion = Pride X 0.19 + Error X 0.13 + Honesty X 0.10 + Fashion X 0.09Analysis: 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. 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. 16. Factor Analysis DataTo run a factor analysis, each piece of data must incorporate atleast 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. 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 competitors16 Please tweet! #RNWebinars @LoveStats
  18. 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, MustangAnalysis: 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. 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 HorsepowerAnalysis: 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. 20. What about conjoint?Unfortunately, social media research is not ideal for runningconjoint 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. 21. WatchoutsIrrelevant data, spam, and viral jokes create false correlations betweenbrands. If this data is not removed prior to the analysis, statistics willerroneously 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 colour20 Please tweet! #RNWebinars @LoveStats
  22. 22. Thank you Please tweet!21 #RNWebinars @LoveStats