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Bye, Bye Research. Hello Data Mining!

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  • Where a user can thenaccess information derived from lithium-powered forums, together with FAQs, service manuals, and other information
  • Transcript

    • 1. Have a question you’d like to
      ask regarding today’s presentation?
      We welcome you to typeyour questions in the ‘Question & Answer’ window at any time during today’s Webinar. We will answer as many questions as time allows during the Q & A session following this presentation.
      Got Tweet? #PLData
    • 2. Bye, Bye Research.
      Hello Data Mining!
      Hosted by Sean Case, SVP, Peanut Labs
      Wednesday, March 10, 2010
      Peanut Labs, Inc. · 114 Sansome Street, Suite 920 · San Francisco, CA 94104
      www.peanutlabs.com
    • 3. Presentations by:
      • Jean Davis, Co-founder, Conversition
      • 4. Catherine van Zuylen, VP, Product Marketing, Attensity
      • 5. Jim Schwab, VP, Business Development – Social Media, Alterian
    • Today’s Agenda
      • Social network mining and analysis
      • 6. Text analytics
      • 7. Predictive modeling and analytics
      • 8. Emerging technologies in data mining
      • 9. Plus more!
    • Pecha Kucha Defined
      • Usually pronounced in three syllables like “pe-chak-cha”
      • 10. A presentation format in which one presenter shows 20 slides for 20 seconds each, for a total of six minutes and 40 seconds
      • 11. Devised in Tokyo in February 2003 by Astrid Klein and Mark Dytham of Tokyo’s Klein-Dytham Architecture
      • 12. Has since turned into a massive celebration, with events happening in hundreds of cities around the world
    • Jean Marie Davis, Co-founder, Conversition
      • Co-founded Conversition in February 2009
      • 13. Formerly the President of Ipsos Online, North America
      • 14. 25+ years of experience in global marketing research
      • 15. Known for her story telling, Jean authored The Little Church that Could, a fun and inspirational review of the signs posted outside one church for an entire year
      • 16. Follow Jean on Twitter @JeanMarie50
    • 17. Not Bye, Bye Research.
      It’s welcome Social Media Research.
      In the Social Network arena there is the opportunity to add social media data to the Marketing Research field.
    • 18. Evolution of Research Science
      • Marketing research techniques that assure data quality and create valuable data are very similar for each type of research – mail, face-to-face, phone, online.
      • 19. Process and methods need to be developed to make social media data be another source for Marketing Research.
    • New Data Set
      New Data Collection Methodology
      • Instead of asking survey participants to answer questions, we listen to what social media contributors want to talk about
    • Applying Research Science
      Market research using a different data source
      • Research means:
      - Strict data quality processes
      - Norms and competitive brands
      - Standardized measures, both box scores and average scores
      - Key research measures
      - Category specific measures
      - Customized client measures
      - Sampling and weighting
    • 20. Creating the Process
      Create Search
      Clean
      Crawl
      Clean
      Sample
      Weight
      Score
      Content Analysis
      Specify
      what
      client wants
      to measure
      Identify
      relevant
      conversations
      Identify
      conversations
      that do not
      meet basic
      quality
      control
      requirements
      Tiered
      system
      reflecting
      unique
      needs of
      different
      data sources
      Content
      analysis is
      applied to
      every
      conversation
      Sampling is
      used to
      identify
      which
      sources are
      appropriate
      for a client
      Weighting
      is applied to the
      sampling matrix
      to ensure that the
      included sources
      are reflected in a
      consistent
      proportion
      over time
    • 21. Data Sources
    • 22. Sample Sizes
      • Social media presence of Client Brand A and C, and Competitive A, B, and C are very good and well suited to social media research.
      • 23. Social media presence of Client Brand B is extremely low and may not be suited for quantitative research at this time.
    • Demographics
      • Social media contributors do not share their demographic information when they contribute online but we do know the demographic make-up of many popular social media websites including twitter, flickr, and blogger.
      • 24. People talking about this brand are more likely to
      be women
      be aged 35 to 64
      have a college degree
      earn $25k to $75k
    • 25. Scoring Methods
    • 26. Content Analysis
      • A method of grouping similar Conversations together so that they can be evaluated as a whole.
      • 27. Retailers: Parking, check-out lines, categories (electronics, apparel)
      • 28. CPG: taste, feel, product, price
      • 29. Determine which sets of conversations are similar to each other based on tone of voice and content of the conversation.
      • 30. Sentiment: positive/negative
    • Sampling & Weighting
      • Sources can be sampled and weighted according to the distribution of internet categories
      • 31. Can be weighted to redistribute sample so that overrepresented categories are less likely to skew the data
    • Reporting
      • Data can bring results in familiar data reports.
      • 32. Brand comparisons
      • 33. Attribute reporting
      • 34. Data can bring results in new data reports.
      • 35. Cloud reporting
      • 36. Psychographics
    • Multiple Brand Comparison
      • Sentiment and volume of chatter were tracked beginning from September 1, 2009
      • 37. Brands with the most positive sentiment include Brand A, Brand G, Brand H, and Brand N.
      • 38. Brands with the most chatter include Brand B, Brand J, and Brand L
      Past 30 days
      n = 378 to 92,000
    • 39. Retailer Attribute Comparison
      Employees
      Crowding
      Parking Lot
      Average Scores
      5.0 = Positive
      3.0 = Neutral
      1.0 = Negative
      Norms
      4.0 = High
      3.3 = Normal
      3.0 = Low
      Website
      Hours
      Washrooms
      • Radar maps allow one to evaluate multiple brands on multiple constructs in one single chart. Brands with the largest web, or circle, are viewed the most positively by consumers. In this case, constructs relevant to retailers have been selected to compare Brand Green retailer with Brand Grey retailer.
      • 40. Scores are most positive in relation to crowding, the parking lot, and the hours. On the other hands, scores are much lower for opinions of employees and the website.
      • 41. While Brand Green outperforms Brand Grey on nearly every construct. However, Brand Green and Brand Grey generate very similar opinions related to their websites.
    • Clouds
      • Data clouds indicate the specific words and phrases that people use in their conversations
      • 42. Popular words indicate:
      - The interests of people talking about the brand, and therefore the contents of marketing materials
      - Co-branding and co-sponsorship opportunities that are relevant to your consumers
      - Appropriate language to use in marketing materials, whether slang or formal
      Use tennis or football metaphors
      Show basketball or football in marketing materials
      Obtain tennis or football celebrity endorsements
    • 43. Psychographics
      • Despite the fact that Brand A and Brand B generate similar emotion scores, by reviewing the assortment of constructs and identifying those with greater and lesser frequencies, psychographic differentiators of brands can be discovered
      • 44. The first three constructs are revealing in that each word relates to the exact same idea. However, the words used among Brand A consumers are more intellectual.
      • 45. This trend follows through in the discussions of technology where Brand A consumers use more technical words.
      • 46. Income and schooling also reflect a higher socio-economic status for Brand A consumers
      • 47. Brand A consumers reflect a higher socio-economic status than Brand B consumers
    • The End
      • Say “Hello” to Social Media Research
      - New data collection methodology
      -Create a process from data collection to reporting
      - Apply research techniques to the data to create a valid, valuable, actionable data set
      - Create new and familiar reports
      - Continue to validate and improve processes
    • 48. March 10, 2010
      Thank you to Peanut Labs for inviting Conversition to share in their webinar!
      Jean Davis, jean@conversition.com
      March 10, 2010
    • 49. Any questions for Jean?
      We welcome you to type your questions in the ‘Question & Answer’ window at any time during today’s Webinar. We will answer as many questions as time allows during the Q & A session following this presentation.
    • 50. Catherine H van Zuylen, VP, Product Marketing, Attensity
      • A consultant at The Grommet Group
      • 51. Formerly Vice President of Marketing at Block Shield
      • 52. 20 years of experience in product management, product marketing and marketing communications
      • 53. A Silicon Valley native who grew up across from an apricot orchard and won several blue ribbons at the country fair for her fruits and vegetables
      • 54. Follow Catherine on Twitter @catevz
    • Leveraging Customer Conversations Through LARA
      Catherine H van Zuylen
      VP, Product Marketing
      cvanzuylen@attensity.com
      www.attensity.com
      Twitter: @attensity
    • 55. Attensity: Over 20 years experience understanding customer conversations in text; 6 patents in natural language processing
      Suite of applications for social media monitoring, Voice of the Customer Analysis, and Self-Service/Agent Service
      Over 500 customers worldwide
      Me: 15 years in marketing; 10+ years in text analytics and internet media
      A Few Words About Me and Attensity
    • 56. “Customer Information” is changing and growing exponentially
      Twitter hit the 10 billion tweet mark last week :
      over 20% are about
      products and services
      Over 247 billion emails are sent every day
      Millions of customer interaction records in a typical large company.
    • 57. To effectively harness these “customer conversations”, you need a program to comprehensively
      Listen across customer conversation channels
      Analyze accurately and efficiently
      Relate this information to other information
      Act on the information
      We call this the LARA methodology
    • 58. LARA Methodology: Listen, Analyze, Relate, Act
      Are you listening where your customers are talking?
      Are your “social media” listening efforts isolated from your “CRM” listening efforts and separate from your “survey” listening? Are you monitoring your internal customer communities?
      Text Analysis can help bridge these gaps.
    • 59. Text Analysis is not Search“Search” is for finding relevant or recent documents that contain a term of interest
    • 60. But it’s hard with search to get the “big picture”
      What do people think
      about my company?
      What problems are they having?
      What do they like about me vs.
      the competition?
      What new ideas do they have?
      Who is thinking
      of switching?
      34
    • 61. “Search” starts with you feeding a system words to look for. “Text Analysis” starts with the data itself and lets it tell a story
      Dynamic Text Profiling
      Documents
      Entities, sentiments, events and relationships, intent, etc
      ?
      XML or other “tags”
    • 62. Text Analysis starts the same way some search engines do…
      Automatic Language and Character Encoding Identification
      Identify paragraphs and sentences within text
      Word Segmentation (Tokenization) and De-Compounding
      Part-of-Speech Tagging
      Stemming
      Noun-Phrase Identification
    • 63. Then continues with Entity Extraction…
      Who: People, Person Position, Social Security Numbers
      What: Companies, Organizations, Financial Indexes, Products (software, weapons, vehicles, etc…)
      When: Dates, Days, Holidays, Months, Years, Times, Time Periods
      Where: Addresses, Cities, States, Countries, Facilities (stadiums, plants), Internet Addresses, Phone Numbers
      How Much: Currencies, Measures
      Concepts (i.e. Global piracy, unstructured data…)
      Can be pattern-based – tell the system that a “Prop-Noun followed by Smith” is probably a person
      Or machine learning – feed it a million proper names and let it deduce names from those examples…
    • 64. Practical Text Analysis in Action
      Let’s say that I am a major retailer, and someone posted a review that starts out
      I bought this Gucciscarffor my mom in your Santana Row store last week.
      Entities (brands, people, locations, times, products…)
    • 65. To “connect the dots” in data, you also need to extract noun-verb relationships, sentiment…
      I bought this Gucci scarf for my mom in your Santana Row store last week.
      I really like the pattern, but I don’t like how it itches.
      Entities (brands, people, locations, times, products…)
      Events and relationships: action and purchasing reason
      Sentiment (extreme positive, positive, negative, extreme negative)
    • 66. To “connect the dots” in data, you also need to extract suggestions, intent…
      I bought this Gucci scarf for my mom in your Santana Row store last week.
      I really like the pattern, but I don’t like how it itches.
      I wish this scarf came in cotton.
      If Gucci made more cotton scarves, I would buy them all.
      Entities (brands, people, locations, times, products…)
      Events and relationships (I : buy : this Gucci scarf | I : buy : for mom)
      Sentiment (extreme positive, positive, negative, extreme negative)
      Suggestions (I : wish : this scarf came in cotton)
      Intent (to purchase, to leave) (If Gucci made more cotton scarves, I would buy them.)
    • 67. How do you do this? You parse sentences like a human…and extract triples…
    • 68. …and voices (intent, recurrence, etc)
      Question [?] voice:
      How can I get free shipping with future orders?
       
      Condition [if/then] voice:.
      I would shop more frequently if you offered free shipping.
       
      Intent [intent] voice:
      I plan to place an order today.
       
      Negation [not] negates the meaning of the verb:
      You did not have the size I was looking for in stock
       
    • 69. …and voices (intent, recurrence, etc)
      Question [?] voice:
      How can I get free shipping with future orders?
       
      Condition [if/then] voice:.
      I would shop more frequently if you offered free shipping.
       
      Intent [intent] voice:
      I plan to place an order today.
       
      Negation [not] negates the meaning of the verb:
      You did not have the size I was looking for in stock
       
      Augment [more] voice:
      The staff were incredibly professional
       
      Recurrence [again] voice:
      I had to enter my information several times for the order to process
       
      Indefinite voice representing suggestions or requests.
      You should sell wedding dresses, too!
    • 70. LARA Methodology: Listen, Analyze, Relate, Act
      Once you’ve done text analysis, you can relate the text to structured information…
      01/24/2010
      By errodd from San Jose, CA
      I bought this Gucci scarf for my mom in your Santana Row store last week.
      I really like the pattern, but I don’t like how it itches.
      I wish this scarf came in cotton.
      If Gucci made more cotton scarves, I would buy them all.
      Can help you answer questions like
      What were the top concerns of people who rated this product a “4”?
    • 71. LARA Methodology: Listen, Analyze, Relate, Act: What Can You Do with Text Analysis?
      The output from text analysis can be exported as XML…
      It can also be used directly in applications that
      Seek out and deliver information to those who need it
      Route and respond to communications
      Mine and report on information
    • 72. “Seek Out” information for a self-service knowledgebase
      Problem
      Solution
      Manufacturer: Apple
      Product: Macbook, Projector, Monitor
      Component: Adapter cord, Mini-DVI, VGA
      Action: Do a presentation, connect
    • 73. Route and respond to all customer communications
      Responses can be reviewed by agent before sending
      “refund policy” email response auto-generated
      Read text and extract
      knowledge about what the document is saying
      People
      Places
      Events
      Topics
      Sentiment

      Refund policy? Email
      Routed to Customer Service for Follow-up and Resolution
      intent to leave tweet
      Automatically routed as a mobile alert to legal for review
      Threatening to sue posting
    • 74. Mine and report on sentiments, complaints, compliments, and “intentional” behavior across all customer conversations
      Better understanding their customers
      Better understanding their customers and gain early warning on product issues
    • 75. Thank You.Leveraging Customer Conversations Through LARA
      Catherine H van Zuylen
      VP, Product Marketing
      cvanzuylen@attensity.com
      www.attensity.com
      Twitter: @attensity
    • 76. Any questions for Catherine?
      We welcome you to type your questions in the ‘Question & Answer’ window at any time during today’s Webinar. We will answer as many questions as time allows during the Q & A session following this presentation.
    • 77. Jim Schwab, VP, Business Development – Social Media, Alterian
      • Formerly SVP of Sales and Marketing at Harris Interactive
      • 78. Has close to 800 followers on Twitter
      • 79. A graduate of the State University of New York College at Brockport
      • 80. When not preoccupied with helping marketing, advertising, PR and customer service professionals to provide visibility and tools to understand what consumers and media are saying online, Jim enjoys keeping up with his 3 kids
      • 81. Follow Jim on Twitter @JImSchwab
    • Bye, Bye Research. Hello Data Mining!Tapping into Social MediaJim SchwabVP Business Development, Social MediaAlterianMarch 10, 2010
    • 82. Agenda
      Quick Intro
      • And my observations over the last couple years
      Social Media, why should you care
      Some caveats & challenges
      Finding the right tool for mining social media
      • And how to use it!
      Some examples
      What should you do?
      • Listen, learn, engage and participate
      Leveraging Social Media Content
    • 83. Alterian SM2 at a Glance
      • A software technology focused on social media monitoring and analytics
      • 84. Founded in 2005 commercially launched August 2008
      • 85. 10,000+ users Globally
      • 86. Freemium
      • 87. Professional
      • 88. Big brands and agencies alike
      • 89. Microsoft, Intuit, McKinsey Consulting
      • 90. Edelman, Carlson Marketing, Epsilon, Experian
    • Quick introMy observations…..
      We have to be where the consumers are!
      Budgets are moving!
      If I can do it anyone can!
      The adoption curve is being followed
      • But much more rapidly
      • 91. New solutions are emerging that make social media more main street focused
    • Quick introAbout me…..
      I’m not a tech geek
      I’m not a data jockey
      I’m not a trained analyst
      I’m passionate about understanding how to deliver the right message to the right audience at the right time using the right mix of channels
      • NOT AN EASY TASK!!
    • Why should you care?Consumers are overwhelmed
    • 92. Why should you care?Listen, learn & engage
      Twitter
      “i was just talking about this the other day - how ineffective/lame the new tropicana packaging is…”
      YouTube
      “just got my new toshiba netbook. seems to be working great. will be nice to use this rather then lugging around my big dell….”
      Blog
      “if you really want to stretch your dollars you can use your registered starbucks card to buy an iced coffee and get a free refill….”
    • 93. Some caveats & challenges Social media content is dynamic and unpredictable
      • It’s not magic!!
      • 94. Blogger, tweeters and SM authors do not cooperate with marketers and customer service professionals
      • 95. SM Content is NOT like your regular customer database
      • 96. SM has no boarders or zip codes
      • 97. SM has little demographics
      • 98. You won’t capture every SM post out there
      • 99. It’s unstructured
      • 100. Automated sentiment is a real challenge
    • How do you get to relevant content?Filter filterfilter…..mine minemine
      The Universe of Content
      1,000,000,000,000,000
      Key words Continuous cleaning
      Exclusions Alerts
      Platforms Content structure
      Language Representativeness
      Location Irrelevant content
      Time period Spam
      Project goals
      Content that is relevant to you
      10,000 posts about my brand + purchase intent + promo terms & time period + competitive mentions
    • 101. What is being said…..
      Where is it being said…..
    • 102. Who’s driving the conversations…..
      Compared to my competition…..
    • 103. Why should you care?Turn unstructured text into actionable insight….
    • 104. Social Media Monitoring Applications Client survey results, bucketed into 10 categories
      Listening / Monitoring
      Reputation & Crisis Management
      Engagement & outreach
      Market Research
      Influencer identification
      Competitive analysis
      Customer support
      SEO and link building
      Support Loyalty Programs
      Augment mystery shopper programs
    • 105. Increase brand recognition and media attention
      The project
      OLX is the next generation of free online classifieds.
      • Blogger outreach
      • 106. Online PR
      OLX wanted to run a 4 month trial period before proceeding any further. Unknown territory…..
      Chris Abraham, President and COO
      chris.abraham@abrahamharrison.com
      +1 202 352 5051
    • 107. The payback
      Year on year increase in the US
      The payback
      Increase in volume, across languages
      Chris Abraham, President and COO
      chris.abraham@abrahamharrison.com
      +1 202 352 5051
    • 108. The payback and key learnings
      • OLX.com web traffic increased 40% over the 4 month trial
      • 109. Abraham & Harrison renewed for 12 month contract
      • 110. Twitter accounts in 3 languages, 5 in 6 months
      Chris Abraham, President and COO
      chris.abraham@abrahamharrison.com
      +1 202 352 5051
    • 111. Help client move the brand image among key influencers
      The project
      Two part project
      12 month audit of conversations, in depth analytics
      • Report and recommendations delivered
      • 112. Segmentation and profiles built of key targets
      Approval on recommended approach to influencers
      • Outreach and PR program
      Wendy Scherer, Founder Partner
      wscherer@socialstudiesgroup.com
      +1 202 715 3884
    • 113. Segmentations & Profile
      Their Views:
      “..recent concerns about excessive dairy consumption and the
      possible effects on health.”
      Favorite web sites
      Most used social media channels
      Their Profile:
      “They heavily reference the
      writings of Michael Pollan,
      who advocates natural food
      production ……..generally recommend
      choosing foods from a variety of food groups.”
    • 114. The payback and key learnings
      • Based on initial work the company has built a team (in house and agency) to do SM engagement.
      • 115. Begun to specialize their team
      • 116. Fantastic time saver in finding influencers
      • 117. Can’t be salesy – this is SOCIAL media
      • 118. Education materials on diet data, nutrition, gluten free…etc.
      • 119. Market & thought leader type conversations have increased
      Wendy Scherer, Founder Partner
      wscherer@socialstudiesgroup.com
      +1 202 715 3884
    • 120. Find the right tool for the jobListen, learn, engage and participate…..
      • Self service vs Professional service/agency
      • 121. Reporting
      • 122. Powerful and flexible functionality
      • 123. You HAVE to be able to dig into the weeds…….or you risk analysis based on bad data
      • 124. There are many vendors!
      • 125. High tech software to low tech Jim’s Social Media Company
      • 126. Many start ups
      • 127. There are only a few real players in the software space
      • 128. And many good agencies
    • Thank you
      Sign up for a FREE Social Media Monitoring account!!!
      Jim Schwab
      +1.585.261.9433
      Jim.schwab@alterian.com
      @jimschwab
      SM2 Freemium http://sm2.techrigy.com
      Alterian SM2
      Social Media Monitoring
    • 129. Any questions for Jim?
      We welcome you to type your questions in the ‘Question & Answer’ window at any time during today’s Webinar. We will answer as many questions as time allows during the Q & A session following this presentation.
    • 130. Q & A Session
      We welcome any questions you may have regarding the content of today’s Webinar.
    • 131. Special thank you to each of our threepresenters!
    • 132. Thank you for joining us!
      The slide deck along with a recording of today’s presentation will be available for download via our website. We will be sending all attendees a link to the
      slide deck as soon as it is available.