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Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
Bye, Bye Research.  Hello Data Mining!
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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 <br />ask regarding today’s presentation?<br />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.<br />Got Tweet? #PLData<br />
    • 2. Bye, Bye Research.<br />Hello Data Mining!<br />Hosted by Sean Case, SVP, Peanut Labs<br />Wednesday, March 10, 2010<br />Peanut Labs, Inc. · 114 Sansome Street, Suite 920 · San Francisco, CA 94104<br />www.peanutlabs.com<br />
    • 3. Presentations by:<br /><ul><li>Jean Davis, Co-founder, Conversition
    • 4. Catherine van Zuylen, VP, Product Marketing, Attensity
    • 5. Jim Schwab, VP, Business Development – Social Media, Alterian</li></li></ul><li>Today’s Agenda<br /><ul><li>Social network mining and analysis
    • 6. Text analytics
    • 7. Predictive modeling and analytics
    • 8. Emerging technologies in data mining
    • 9. Plus more!</li></li></ul><li>Pecha Kucha Defined<br /><ul><li> 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</li></li></ul><li>Jean Marie Davis, Co-founder, Conversition<br /><ul><li> 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</li></li></ul><li>
    • 17. Not Bye, Bye Research.<br />It’s welcome Social Media Research.<br />In the Social Network arena there is the opportunity to add social media data to the Marketing Research field.<br />
    • 18. Evolution of Research Science<br /><ul><li> 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.</li></li></ul><li>New Data Set<br />New Data Collection Methodology<br /><ul><li>Instead of asking survey participants to answer questions, we listen to what social media contributors want to talk about</li></li></ul><li>Applying Research Science<br />Market research using a different data source<br /><ul><li>Research means:</li></ul>- Strict data quality processes<br />- Norms and competitive brands<br />- Standardized measures, both box scores and average scores<br />- Key research measures<br />- Category specific measures<br />- Customized client measures<br />- Sampling and weighting<br />
    • 20. Creating the Process<br />Create Search<br />Clean<br />Crawl<br />Clean<br />Sample<br />Weight<br />Score<br />Content Analysis<br />Specify<br />what<br />client wants<br />to measure<br />Identify<br />relevant<br />conversations<br />Identify <br />conversations <br />that do not<br />meet basic<br />quality <br />control <br />requirements<br />Tiered<br />system <br />reflecting<br />unique<br />needs of<br />different<br />data sources<br />Content <br />analysis is <br />applied to <br />every <br />conversation<br />Sampling is <br />used to <br />identify<br />which <br />sources are <br />appropriate <br />for a client<br />Weighting <br />is applied to the <br />sampling matrix <br />to ensure that the <br />included sources <br />are reflected in a <br />consistent<br />proportion <br />over time<br />
    • 21. Data Sources<br />
    • 22. Sample Sizes<br /><ul><li>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. </li></li></ul><li>Demographics<br /><ul><li>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</li></ul>be women<br />be aged 35 to 64<br />have a college degree<br />earn $25k to $75k<br />
    • 25. Scoring Methods<br />
    • 26. Content Analysis<br /><ul><li>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</li></li></ul><li>Sampling & Weighting<br /><ul><li>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</li></li></ul><li>Reporting<br /><ul><li>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</li></li></ul><li>Multiple Brand Comparison<br /><ul><li>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</li></ul>Past 30 days<br />n = 378 to 92,000<br />
    • 39. Retailer Attribute Comparison<br />Employees<br />Crowding<br />Parking Lot<br />Average Scores<br />5.0 = Positive<br />3.0 = Neutral<br />1.0 = Negative<br />Norms<br />4.0 = High<br />3.3 = Normal<br />3.0 = Low<br />Website<br />Hours<br />Washrooms<br /><ul><li>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.</li></li></ul><li>Clouds<br /><ul><li>Data clouds indicate the specific words and phrases that people use in their conversations
    • 42. Popular words indicate:</li></ul>- The interests of people talking about the brand, and therefore the contents of marketing materials<br />- Co-branding and co-sponsorship opportunities that are relevant to your consumers<br />- Appropriate language to use in marketing materials, whether slang or formal<br />Use tennis or football metaphors<br />Show basketball or football in marketing materials<br />Obtain tennis or football celebrity endorsements<br />
    • 43. Psychographics<br /><ul><li>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</li></li></ul><li>The End<br /><ul><li>Say “Hello” to Social Media Research</li></ul>- New data collection methodology<br />-Create a process from data collection to reporting<br />- Apply research techniques to the data to create a valid, valuable, actionable data set<br />- Create new and familiar reports<br />- Continue to validate and improve processes<br />
    • 48. March 10, 2010<br />Thank you to Peanut Labs for inviting Conversition to share in their webinar!<br />Jean Davis, jean@conversition.com<br />March 10, 2010<br />
    • 49. Any questions for Jean?<br />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.<br />
    • 50. Catherine H van Zuylen, VP, Product Marketing, Attensity<br /><ul><li> 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</li></li></ul><li>Leveraging Customer Conversations Through LARA<br />Catherine H van Zuylen<br />VP, Product Marketing<br />cvanzuylen@attensity.com<br />www.attensity.com<br />Twitter: @attensity<br />
    • 55. Attensity: Over 20 years experience understanding customer conversations in text; 6 patents in natural language processing<br />Suite of applications for social media monitoring, Voice of the Customer Analysis, and Self-Service/Agent Service<br />Over 500 customers worldwide<br />Me: 15 years in marketing; 10+ years in text analytics and internet media<br />A Few Words About Me and Attensity<br />
    • 56. “Customer Information” is changing and growing exponentially<br />Twitter hit the 10 billion tweet mark last week : <br />over 20% are about <br />products and services<br />Over 247 billion emails are sent every day<br />Millions of customer interaction records in a typical large company.<br />
    • 57. To effectively harness these “customer conversations”, you need a program to comprehensively<br />Listen across customer conversation channels<br />Analyze accurately and efficiently<br />Relate this information to other information<br />Act on the information<br />We call this the LARA methodology<br />
    • 58. LARA Methodology: Listen, Analyze, Relate, Act<br />Are you listening where your customers are talking? <br />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?<br />Text Analysis can help bridge these gaps.<br />
    • 59. Text Analysis is not Search“Search” is for finding relevant or recent documents that contain a term of interest<br />
    • 60. But it’s hard with search to get the “big picture”<br />What do people think <br />about my company?<br />What problems are they having?<br />What do they like about me vs. <br />the competition?<br />What new ideas do they have?<br />Who is thinking <br />of switching?<br />34<br />
    • 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<br />Dynamic Text Profiling<br />Documents<br />Entities, sentiments, events and relationships, intent, etc<br />?<br />XML or other “tags”<br />
    • 62. Text Analysis starts the same way some search engines do…<br />Automatic Language and Character Encoding Identification <br />Identify paragraphs and sentences within text<br />Word Segmentation (Tokenization) and De-Compounding<br />Part-of-Speech Tagging <br />Stemming <br />Noun-Phrase Identification<br />
    • 63. Then continues with Entity Extraction…<br />Who: People, Person Position, Social Security Numbers<br />What: Companies, Organizations, Financial Indexes, Products (software, weapons, vehicles, etc…)<br />When: Dates, Days, Holidays, Months, Years, Times, Time Periods<br />Where: Addresses, Cities, States, Countries, Facilities (stadiums, plants), Internet Addresses, Phone Numbers <br />How Much: Currencies, Measures<br />Concepts (i.e. Global piracy, unstructured data…)<br />Can be pattern-based – tell the system that a “Prop-Noun followed by Smith” is probably a person<br />Or machine learning – feed it a million proper names and let it deduce names from those examples…<br />
    • 64. Practical Text Analysis in Action<br />Let’s say that I am a major retailer, and someone posted a review that starts out<br />I bought this Gucciscarffor my mom in your Santana Row store last week. <br />Entities (brands, people, locations, times, products…)<br />
    • 65. To “connect the dots” in data, you also need to extract noun-verb relationships, sentiment…<br />I bought this Gucci scarf for my mom in your Santana Row store last week. <br />I really like the pattern, but I don’t like how it itches.<br />Entities (brands, people, locations, times, products…)<br />Events and relationships: action and purchasing reason<br />Sentiment (extreme positive, positive, negative, extreme negative)<br />
    • 66. To “connect the dots” in data, you also need to extract suggestions, intent…<br />I bought this Gucci scarf for my mom in your Santana Row store last week. <br />I really like the pattern, but I don’t like how it itches. <br />I wish this scarf came in cotton. <br />If Gucci made more cotton scarves, I would buy them all.<br />Entities (brands, people, locations, times, products…)<br />Events and relationships (I : buy : this Gucci scarf | I : buy : for mom)<br />Sentiment (extreme positive, positive, negative, extreme negative)<br />Suggestions (I : wish : this scarf came in cotton)<br />Intent (to purchase, to leave) (If Gucci made more cotton scarves, I would buy them.)<br />
    • 67. How do you do this? You parse sentences like a human…and extract triples…<br />
    • 68. …and voices (intent, recurrence, etc)<br />Question [?] voice:<br />How can I get free shipping with future orders?<br /> <br />Condition [if/then] voice:.<br />I would shop more frequently if you offered free shipping.<br /> <br />Intent [intent] voice:<br />I plan to place an order today.<br /> <br />Negation [not] negates the meaning of the verb:<br />You did not have the size I was looking for in stock<br /> <br />
    • 69. …and voices (intent, recurrence, etc)<br />Question [?] voice:<br />How can I get free shipping with future orders?<br /> <br />Condition [if/then] voice:.<br />I would shop more frequently if you offered free shipping.<br /> <br />Intent [intent] voice:<br />I plan to place an order today.<br /> <br />Negation [not] negates the meaning of the verb:<br />You did not have the size I was looking for in stock<br /> <br />Augment [more] voice:<br />The staff were incredibly professional<br /> <br />Recurrence [again] voice:<br />I had to enter my information several times for the order to process<br /> <br />Indefinite voice representing suggestions or requests.<br />You should sell wedding dresses, too!<br />
    • 70. LARA Methodology: Listen, Analyze, Relate, Act<br />Once you’ve done text analysis, you can relate the text to structured information…<br />01/24/2010<br />By errodd from San Jose, CA<br />I bought this Gucci scarf for my mom in your Santana Row store last week. <br />I really like the pattern, but I don’t like how it itches. <br />I wish this scarf came in cotton. <br />If Gucci made more cotton scarves, I would buy them all.<br />Can help you answer questions like<br />What were the top concerns of people who rated this product a “4”?<br />
    • 71. LARA Methodology: Listen, Analyze, Relate, Act: What Can You Do with Text Analysis?<br />The output from text analysis can be exported as XML…<br />It can also be used directly in applications that<br />Seek out and deliver information to those who need it<br />Route and respond to communications<br />Mine and report on information<br />
    • 72. “Seek Out” information for a self-service knowledgebase<br />Problem<br />Solution<br />Manufacturer: Apple<br />Product: Macbook, Projector, Monitor<br />Component: Adapter cord, Mini-DVI, VGA<br />Action: Do a presentation, connect<br />
    • 73. Route and respond to all customer communications<br />Responses can be reviewed by agent before sending<br />“refund policy” email response auto-generated<br />Read text and extract<br />knowledge about what the document is saying<br />People<br />Places<br />Events<br />Topics<br />Sentiment <br />…<br />Refund policy? Email<br />Routed to Customer Service for Follow-up and Resolution<br />intent to leave tweet<br />Automatically routed as a mobile alert to legal for review<br />Threatening to sue posting<br />
    • 74. Mine and report on sentiments, complaints, compliments, and “intentional” behavior across all customer conversations<br />Better understanding their customers<br />Better understanding their customers and gain early warning on product issues<br />
    • 75. Thank You.Leveraging Customer Conversations Through LARA<br />Catherine H van Zuylen<br />VP, Product Marketing<br />cvanzuylen@attensity.com<br />www.attensity.com<br />Twitter: @attensity<br />
    • 76. Any questions for Catherine?<br />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.<br />
    • 77. Jim Schwab, VP, Business Development – Social Media, Alterian<br /><ul><li> 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</li></li></ul><li>Bye, Bye Research. Hello Data Mining!Tapping into Social MediaJim SchwabVP Business Development, Social MediaAlterianMarch 10, 2010<br />
    • 82. Agenda<br />Quick Intro<br /><ul><li>And my observations over the last couple years</li></ul>Social Media, why should you care<br />Some caveats & challenges<br />Finding the right tool for mining social media<br /><ul><li>And how to use it!</li></ul>Some examples<br />What should you do?<br /><ul><li>Listen, learn, engage and participate</li></ul>Leveraging Social Media Content<br />
    • 83. Alterian SM2 at a Glance<br /><ul><li>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</li></li></ul><li>Quick introMy observations…..<br />We have to be where the consumers are!<br />Budgets are moving! <br />If I can do it anyone can!<br />The adoption curve is being followed<br /><ul><li>But much more rapidly
    • 91. New solutions are emerging that make social media more main street focused</li></li></ul><li>Quick introAbout me…..<br />I’m not a tech geek<br />I’m not a data jockey<br />I’m not a trained analyst<br />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<br /><ul><li>NOT AN EASY TASK!!</li></li></ul><li>Why should you care?Consumers are overwhelmed<br />
    • 92. Why should you care?Listen, learn & engage<br />Twitter<br />“i was just talking about this the other day - how ineffective/lame the new tropicana packaging is…”<br />YouTube<br />“just got my new toshiba netbook. seems to be working great. will be nice to use this rather then lugging around my big dell….”<br />Blog<br />“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….”<br />
    • 93. Some caveats & challenges Social media content is dynamic and unpredictable<br /><ul><li>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</li></li></ul><li>How do you get to relevant content?Filter filterfilter…..mine minemine<br />The Universe of Content<br /> 1,000,000,000,000,000 <br />Key words Continuous cleaning<br />Exclusions Alerts<br />Platforms Content structure<br />Language Representativeness<br />Location Irrelevant content<br />Time period Spam<br /> Project goals<br />Content that is relevant to you<br />10,000 posts about my brand + purchase intent + promo terms & time period + competitive mentions<br />
    • 101. What is being said…..<br />Where is it being said…..<br />
    • 102. Who’s driving the conversations…..<br />Compared to my competition…..<br />
    • 103. Why should you care?Turn unstructured text into actionable insight….<br />
    • 104. Social Media Monitoring Applications Client survey results, bucketed into 10 categories<br />Listening / Monitoring <br />Reputation & Crisis Management<br />Engagement & outreach<br />Market Research <br />Influencer identification <br />Competitive analysis <br />Customer support <br />SEO and link building <br />Support Loyalty Programs<br />Augment mystery shopper programs <br />
    • 105. Increase brand recognition and media attention<br />The project<br />OLX is the next generation of free online classifieds. <br /><ul><li>Blogger outreach
    • 106. Online PR</li></ul>OLX wanted to run a 4 month trial period before proceeding any further. Unknown territory…..<br />Chris Abraham, President and COO<br />chris.abraham@abrahamharrison.com<br />+1 202 352 5051<br />
    • 107. The payback<br />Year on year increase in the US<br />The payback<br />Increase in volume, across languages<br />Chris Abraham, President and COO<br />chris.abraham@abrahamharrison.com<br />+1 202 352 5051<br />
    • 108. The payback and key learnings<br /><ul><li>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</li></ul>Chris Abraham, President and COO<br />chris.abraham@abrahamharrison.com<br />+1 202 352 5051<br />
    • 111. Help client move the brand image among key influencers<br />The project<br />Two part project<br />12 month audit of conversations, in depth analytics<br /><ul><li>Report and recommendations delivered
    • 112. Segmentation and profiles built of key targets</li></ul>Approval on recommended approach to influencers<br /><ul><li>Outreach and PR program</li></ul>Wendy Scherer, Founder Partner<br />wscherer@socialstudiesgroup.com<br />+1 202 715 3884<br />
    • 113. Segmentations & Profile<br />Their Views:<br />“..recent concerns about excessive dairy consumption and the<br />possible effects on health.”<br />Favorite web sites<br />Most used social media channels<br />Their Profile:<br />“They heavily reference the<br />writings of Michael Pollan,<br />who advocates natural food<br />production ……..generally recommend<br />choosing foods from a variety of food groups.”<br />
    • 114. The payback and key learnings<br /><ul><li>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</li></ul>Wendy Scherer, Founder Partner<br />wscherer@socialstudiesgroup.com<br />+1 202 715 3884<br />
    • 120. Find the right tool for the jobListen, learn, engage and participate…..<br /><ul><li>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</li></li></ul><li>Thank you<br />Sign up for a FREE Social Media Monitoring account!!!<br />Jim Schwab<br />+1.585.261.9433<br />Jim.schwab@alterian.com<br /> @jimschwab <br />SM2 Freemium http://sm2.techrigy.com<br />Alterian SM2<br />Social Media Monitoring <br />
    • 129. Any questions for Jim?<br />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.<br />
    • 130. Q & A Session<br />We welcome any questions you may have regarding the content of today’s Webinar.<br />
    • 131. Special thank you to each of our threepresenters!<br />
    • 132. Thank you for joining us!<br />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<br />slide deck as soon as it is available.<br />

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