The document provides an agenda for a webinar on leveraging social media data mining. It introduces Jim Schwab from Alterian who will discuss why social media should be of interest to marketers and how to find the right tools to mine social media. It outlines that Schwab will cover listening to, learning from, engaging with, and participating in social media. He will also provide examples of how to leverage social media content.
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Bye, Bye Research. Hello Data Mining!
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
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
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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
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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.
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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
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 LPast 30 days n = 378 to 92,000
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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.
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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
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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
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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.
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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
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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.
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
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….”
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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
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
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106. Online PROLX 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
110. Twitter accounts in 3 languages, 5 in 6 monthsChris Abraham, President and COO chris.abraham@abrahamharrison.com +1 202 352 5051
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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.”
127. There are only a few real players in the software space
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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.
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.
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