combine social media analytics with survey research and...
TRANSCRIPT
Combine Social Media Analytics with Survey Research
and Discover the Unknown!!!05/08/2012!
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Why we do what we do!
Find out something that we didn’t know that gives us new information !to make better decisions about the future!
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Find out something that we didn’t know!
Profile of “something we didn’t know”:"!- New information (unknown previously)!- Shared by a population of interest!- Usually negative in nature!
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The right information!
• Not all information is important to our goal; quite a lot of noise!
• Understand if information is applicable to our customer!
• Something we can do something about: “get ahead of” !
• Something we can verify!
Let’s not look for the wrong needle!
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Make better business decisions!
• Preserve business!• Build reputation!• Build customer satisfaction!• Build growth!
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Overall process!
Plan! Harvest! Structure & Understand! Validate!
• Figure out who we want to listen to !• Figure out where we want to listen !• Review what we already know (noise and navel gazing)!!
• Remove dirty data !• Remove what we know: “noise” and ”navel gazing”!• Find new info by combining Classification and Clustering techniques!
• The tool we use to scrape depends on the channel !• Sample is fine; population scrape is impossible anyway!• The more attributes the better!
• Look at both qualitative and quantitative feedback!• We now have insight, validate by digging deeper!
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Plan! Scrape! Analyze! Survey!
Tools!
• Apache Mahout !• Gate (NLP)"• RapidMiner (NLP)"• Carrot2 (Clustering)"• NLTK "• LIWC"
• Alterian AS2"• Sysomos"• Clarabridge"
• Let’s not get wedded to a tool for both scraping and analysis!
• Social Mention.com"• DiscoverText"• OdinText"• Twitter Scraping Tools (tens of them)"
• Omnibus"• DIY survey tool "! Must-haves:"• Weight responses to census rep!• Provide quality panel!• Offer templates and norms!!
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Where to scrape depends on who!
! Region – U.S., Europe, Asia, etc.!
! Target demographics!! Gender!! Language!! Age!! Ethnicity/Race!! Employment/Income!! Education !
! Specialized populations:!! Influencers!! Current consumers!! Competitors!! Youth!!
Facebook QZone Vkontakte Odnoklassniki !
Draugirmand Zing"Twitter "Baduu"
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Case Study!
! Victoria’s Secret"
! Focused on a couple of brands (besides VS overall):!! Dream Angels!! Pink
!! VS Pink target:"
! Female, United States, 18-24 year olds!
! Victoria’s Secret/Dream Angels target:"! Female, more global, larger age range (18-34 and older)!
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Figure out what we already know (noise and navel gazing)!! Noise: Meme-ish textual data that while entertaining may not give further
insight!
! Navel Gazing: Specific promotions or ad copy that adds to chatter yet not wanted for our purposes!
• Each write-up is slightly different!• NLP helps us identify these and remove from consideration!
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The tool we use to scrape depends on the channel!
! Some tools do not cover all the different channels (twitter, blogs, forums) or geo-regions; select wisely and use more than one tool!
! Scraping criteria should not be undervalued in terms of direct impact on results; the more time you spend on refining your scraping criteria, the better your corpus will be. Period.!
! Look for advanced searching capabilities – proximity searching, stop word list, filtering on different attributes (authors, !
! Advanced searching capabilities may be different for every channel you use!
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Sample of posts is fine; population scrape is impossible!
! Search criteria should stay consistent across pulls (for monitoring purposes)!
! Unless we are the FBI, you’ll never get every single post, so don’t try !
! More important is tool and channel stays constant across time !
! See analysis done by FreshNetworks (2011): !
http://www.freshnetworks.com/blog/2010/06/social-media-monitoring-review-download-the-final-report/!
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The more attributes that come with, the better!
! Normal for quite a lot of the structured data to be null; that’s ok – in aggregate, the information is directionally accurate !
! You can use this data in your analysis!
! Attributes such as –!• Dates (posted, etc)!• Gender!• Region!• Author!• Source URL!• Channel!• Detected Language!• Associated Images!• Ages
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Clarabridge"
DiscoverText"
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Victoria’s Secret – Target channels/Tools!
! Target channels for Victoria’s Secret"! Facebook!
! http://www.facebook.com/victoriassecret (18M fans)!! http://www.facebook.com/vspink (11M fans)
!! Twitter Search API:!
! http://search.twitter.com/search.json?q=VS%20pink (VS and Pink)!! “Victoria’s Secret” and “VS”!! “Dream Angels”
!! Blogs: !
! “Victoria’s Secret”, “VS” and “Pink”, “Dream Angels” !
! Tools used:"• Social Mention.com (blogs)!• DiscoverText (facebook, twitter)!• Clarabridge (facebook, twitter)!• Twitter Scraping Tools (twitter)!
Examples only – in real life, this is insufficient; this should take 15
slides to be “complete”!
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Remove/filter out dirty data!
Victoria’s Secret example"
“VS” and “Pink” picked up false positives!
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Remove what you know: “noise” and “navel gazing”!
! In cluster map to right, we can see that about 5% of the scraped posts from blogs were promos – clustering helps us identify the unnecessary posts(if we’re good, we can anticipate, remove from search criteria, and not have to clean them out of the dataset later)!
Victoria’s Secret example"
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Find new info by combining Classification and Clustering techniques!
Classification"
! Starting with a known taxonomy (or classification scheme), create rules to classify specific texts for a subset of texts (an iterative activity)!
! Then use machine learning to identify more text that fall into the given classification scheme !
Brand"VS Pink!Very Sexy!Body by Victoria!Dream Angels!Incredible!Bombshell™!Fabulous!Cotton Lingerie!The Showstopper!Gorgeous Collection!The Nakeds!BioFit!Sexy Tee!Glamour!!Style"Push-Up!Full Coverage!Demi Cup!Add 2 Cup Sizes!Strapless!Racerback!Wireless!T-Shirt!Unlined!Size DDD!Sports Bras!
Panties"Thongs!Hiphuggers!Bikinis!Cheekies!Briefs!Boyshorts!Garters!V-Strings!Seamless!Shapewear!Cotton!!!Sleepwear"Pajamas!Nighties!Lounge!Robes & Slippers!Lingerie!All Lingerie!Babydolls & Slips!Corsets & Teddies!Garters!!!!
Clothing...!Shoes...!Swimwear...!
panties!
bras!
sweats!Pink brand"
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Find new info by combining Classification and Clustering techniques!
! Clustering is the automated assigning of text into different groups based on similarities within the text!
! Clustering may be applied on the entire textual corpus!
! Different algorithms give different results!
! Clustering may be applied on a previously bucketed sub-corpus (for example, all texts that fall into the VS Pink bucket)!
from carrot2 documentation"
carrot2" clarabridge"
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Applying sentiment to classifications!
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1. highest token sentiment value in the sentence based on word analysis: For example, it is +3.!
2. Add an increment for every additional token of same token sentiment!
3. Add a smaller increment for every token one level lower in sentiment.!
clarabridge"
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Victoria’s Secret results!
VS is not for real women. It is for delusional 20-somethings who think PINK across their asses looks cool.!“…I hate pink”!I do not mean to sound like a bitch but there should be an age limit on wearing VS PINK clothing...!
Your stores need to carry bikinis in the bigger sizes in store.!
They are neon pink snakeskin print. ^_^ They sound so ugly. Lol. VS will never fail you..!
! Identified Issue" ! Qualitative Example"
2% - Stores (not catalogs) are not carrying the right size in swimwear (classification/clustering/sentiment)!
7% - Victoria’s Secret isn’t addressing the needs of women outside the 18-24 year old VS Pink target (classification/clustering/sentiment)"
1% - VS merchandise is “ugly”"(clustering/sentiment)"
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We now have insight, validate by digging deeper!! VS Pink target:"
! Female, United States, 18-24 year olds!
! Victoria’s Secret/Dream Angels target:"! Female, more global, larger age range (18-34 and older)!
! We want to find out the impact of the following issue by surveying females in the United States:!
Your stores need to carry bikinis in the bigger sizes in store.!
2% - Stores (not catalogs) are not carrying the right size in swimwear (classification/clustering/sentiment)!
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Use question templates!
! Demographics:"! Gender!! Language!! Age!! Ethnicity/Race!! Employment/Income!! Education
!! Behavioral:"
! Have you entered a Victoria’s Secret store in the past six months? {If yes, then}!
! Have you shopped for swimwear at Victoria’s Secret? {if yes, then}!
! Opinion:"! When you shop for swimwear at Victoria’s Secret, are you able to
find your size?!
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Use a quality panel!
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Collect Data!
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Weight responses to census rep!
Without weighting, the results may be interpreted as estimates for the population under test (as opposed to estimates relating to the achieved sample)For example, in Victoria’s Secret survey, without weighting, the responses by females between 18-24 years old will only represent those who answered the survey, instead of their population within the U.S.:!
Age" Online U.S. Pop" VS Survey"18-24! 15%! 10%!25-34! 20%! 22%!35-44! 22%! 31%!45-54! 20%! 18%!55-64! 14%! 10%!65+! 9%! 9%!
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Results: Swimwear!
Your stores need to carry bikinis in the bigger sizes in store.!
! Identified Issue" ! Qualitative Example"
2% - Stores (not catalogs) are not carrying the right size in swimwear (classification/clustering/sentiment)!
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Plan! Harvest! Structure & Understand! Validate!
Recap!
• We wanted to listen to VS consumers!
• VS consumers drove the channel selection: FB, Twitter, Blogs!!
• Removed dirty data, noise, and navel gazing !• Found three potential issues by combining Classification and Clustering techniques!
• Used multiple tools to scrape !• Scraped quantitative-sized data about Victoria’s Secret, VS Pink, Dream Angels!!
• Ran a quick online survey to validate the issues!
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Thank You!"!!
Please connect with me on LinkedIn http://www.linkedin.com/pub/carol-sue-haney/0/751/21!
!Twitter: carolsuehaney!
!Carol Sue Haney!
[email protected]!802.254.2117!
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