Advanced Analytics with Social Media Data
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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.
Advanced Analytics with Social Media Data
- 1. Please tweet! #RNWebinars @LoveStats Advanced Analytics for Social Media Research: Examples from the automotive industry January 2013 Social media listening data by researchers, for researchers
- 2. Please tweet! #RNWebinars @LoveStats 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 1 1 2 3 4 5
- 3. Please tweet! #RNWebinars @LoveStats 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 2 1 2 3
- 4. Please tweet! #RNWebinars @LoveStats 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
- 5. Please tweet! #RNWebinars @LoveStats Research Method 4 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
- 6. Please tweet! #RNWebinars @LoveStats5 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
- 7. Please tweet! #RNWebinars @LoveStats Correlations: Women’s Brand Preferences 6 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
- 8. Please tweet! #RNWebinars @LoveStats Correlations: Men’s Brand Preferences 7 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
- 9. Please tweet! #RNWebinars @LoveStats Correlations: Women’s Feature Preferences 8 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
- 10. Please tweet! #RNWebinars @LoveStats Correlations: Men’s Feature Preferences 9 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
- 11. Please tweet! #RNWebinars @LoveStats10 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
- 12. Please tweet! #RNWebinars @LoveStats11 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
- 13. Please tweet! #RNWebinars @LoveStats12 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
- 14. Please tweet! #RNWebinars @LoveStats13 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
- 15. Please tweet! #RNWebinars @LoveStats14 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
- 16. Please tweet! #RNWebinars @LoveStats15 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.”
- 17. Please tweet! #RNWebinars @LoveStats16 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
- 18. Please tweet! #RNWebinars @LoveStats17 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
- 19. Please tweet! #RNWebinars @LoveStats18 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
- 20. Please tweet! #RNWebinars @LoveStats19 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.”
- 21. Please tweet! #RNWebinars @LoveStats20 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 !!
- 22. Please tweet! #RNWebinars @LoveStats Thank you 21 hello@conversition.com www.conversition.com
Editor's Notes
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